From 5cef625c1b9d4e5c1757842d536db37fd0691bda Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Tue, 2 Apr 2024 17:58:15 +0300
Subject: [PATCH 01/40] gradio app added

---
 README.md               |  30 +++
 gradio_app.ipynb        | 143 +++++++++++++
 gradio_app.py           | 437 ++++++++++++++++++++++++++++++++++++++++
 gradio_requirements.txt |   3 +
 start-jupyter.bat       |   1 +
 start-jupyter.sh        |   1 +
 6 files changed, 615 insertions(+)
 create mode 100644 gradio_app.ipynb
 create mode 100644 gradio_app.py
 create mode 100644 gradio_requirements.txt

diff --git a/README.md b/README.md
index 74713fa..2a5be3a 100644
--- a/README.md
+++ b/README.md
@@ -90,6 +90,36 @@ If you have encountered version issues when running things, checkout [environmen
 ## Inference Examples
 Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) and [`inference_tts.ipynb`](./inference_tts.ipynb)
 
+## Gradio
+After environment setup install additional dependencies:
+```bash
+pip install -r gradio_requirements.txt
+```
+
+Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
+```bash
+python gradio_app.py
+```
+It is ready to use on [default url](http://127.0.0.1:7860).
+
+### How to use it
+1. (optionally) Select models
+2. Load models
+3. Transcribe
+4. (optionally) Tweak some parameters
+5. Run
+6. (optionally) Rerun part-by-part in Long TTS mode
+
+### Some features
+Smart transcript: write only what you want to generate, but don't work if you edit original transcript
+
+TTS mode: Zero-shot TTS
+
+Edit mode: Speech editing
+
+Long TTS mode: Easy TTS on long texts
+
+
 ## Training
 To train an VoiceCraft model, you need to prepare the following parts: 
 1. utterances and their transcripts
diff --git a/gradio_app.ipynb b/gradio_app.ipynb
new file mode 100644
index 0000000..0d3f946
--- /dev/null
+++ b/gradio_app.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b6a0c92",
+   "metadata": {},
+   "source": [
+    "### Only do the below if you are using docker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270aa2cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install OS deps\n",
+    "!sudo apt-get update && sudo apt-get install -y \\\n",
+    "    git-core \\\n",
+    "    ffmpeg \\\n",
+    "    espeak-ng"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ba5f452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update and setup Conda voicecraft environment\n",
+    "!conda update -y -n base -c conda-forge conda\n",
+    "!conda create -y -n voicecraft python=3.9.16 && \\\n",
+    "    conda init bash"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ef2935c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install conda and pip stuff in the activated conda above context\n",
+    "!echo -e \"Grab a cup a coffee and a slice of pizza...\\n\\n\"\n",
+    "\n",
+    "# make sure $HOME and $USER are setup so this will source the conda environment\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    conda install -y -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068 && \\\n",
+    "    pip install torch==2.0.1 && \\\n",
+    "    pip install tensorboard==2.16.2 && \\\n",
+    "    pip install phonemizer==3.2.1 && \\\n",
+    "    pip install torchaudio==2.0.2 && \\\n",
+    "    pip install datasets==2.16.0 && \\\n",
+    "    pip install torchmetrics==0.11.1\n",
+    "\n",
+    "# do this one last otherwise you'll get an error about torch compiler missing due to xformer mismatch\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fca57eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# okay setup the conda environment such that jupyter notebook can find the kernel\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    conda install -y -n voicecraft ipykernel --update-deps --force-reinstall\n",
+    "\n",
+    "# installs the Jupyter kernel into /home/myusername/.local/share/jupyter/kernels/voicecraft\n",
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    python3 -m ipykernel install --user --name=voicecraft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "961faa43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!source ~/.bashrc && \\\n",
+    "    conda activate voicecraft && \\\n",
+    "    pip install -r gradio_requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9c4436",
+   "metadata": {},
+   "source": [
+    "# STOP\n",
+    "You have to do this part manually using the mouse/keyboard and the tabs at the top.\n",
+    "\n",
+    "* Refresh your browser to make sure it picks up the new kernel.\n",
+    "* Kernel -> Change Kernel -> Select Kernel -> voicecraft\n",
+    "* Kernel -> Restart Kernel -> Yes\n",
+    "\n",
+    "Now you can run the rest of the notebook and get an audio sample output. It will automatically download more models and such. The next time you use this container, you can just start below here as the dependencies will remain available until you delete the docker container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f089aa96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gradio_app import app\n",
+    "app.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "voicecraft",
+   "language": "python",
+   "name": "voicecraft"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gradio_app.py b/gradio_app.py
new file mode 100644
index 0000000..956175c
--- /dev/null
+++ b/gradio_app.py
@@ -0,0 +1,437 @@
+import gradio as gr
+import torch
+import torchaudio
+from data.tokenizer import (
+    AudioTokenizer,
+    TextTokenizer,
+)
+from models import voicecraft
+import whisper
+from whisper.tokenizer import get_tokenizer
+import os
+import io
+
+
+whisper_model = None
+voicecraft_model = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def load_models(input_audio, transcribe_btn, run_btn, rerun_btn):
+    def impl(whisper_model_choice, voicecraft_model_choice):
+        global whisper_model, voicecraft_model
+        whisper_model = whisper.load_model(whisper_model_choice)
+
+        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+        
+        voicecraft_name = f"{voicecraft_model_choice}.pth"
+        ckpt_fn = f"./pretrained_models/{voicecraft_name}"
+        encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+        if not os.path.exists(ckpt_fn):
+            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
+            os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+        if not os.path.exists(encodec_fn):
+            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
+            os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+
+        voicecraft_model = {}
+        voicecraft_model["ckpt"] = torch.load(ckpt_fn, map_location="cpu")
+        voicecraft_model["model"] = voicecraft.VoiceCraft(voicecraft_model["ckpt"]["config"])
+        voicecraft_model["model"].load_state_dict(voicecraft_model["ckpt"]["model"])
+        voicecraft_model["model"].to(device)
+        voicecraft_model["model"].eval()
+
+        voicecraft_model["text_tokenizer"] = TextTokenizer(backend="espeak")
+        voicecraft_model["audio_tokenizer"] = AudioTokenizer(signature=encodec_fn)
+
+        return [
+            input_audio.update(interactive=True),
+            transcribe_btn.update(interactive=True),
+            run_btn.update(interactive=True),
+            rerun_btn.update(interactive=True)
+        ]
+    return impl
+
+
+def transcribe(audio_path):
+    tokenizer = get_tokenizer(multilingual=False)
+    number_tokens = [
+        i
+        for i in range(tokenizer.eot)
+        if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+    ]
+    result = whisper_model.transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
+    words = [word_info for segment in result["segments"] for word_info in segment["words"]]
+    
+    transcript = result["text"]
+    transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
+    transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
+
+    choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
+    edit_from_word = gr.Dropdown(label="First word to edit", value=choices[0], choices=choices, interactive=True)
+    edit_to_word = gr.Dropdown(label="Last word to edit", value=choices[-1], choices=choices, interactive=True)
+
+    return [
+        transcript, transcript_with_start_time, transcript_with_end_time,
+        edit_from_word, edit_to_word, words
+    ]
+
+
+def get_output_audio(audio_tensors, codec_audio_sr):
+    result = torch.cat(audio_tensors, 1)
+    buffer = io.BytesIO()
+    torchaudio.save(buffer, result, int(codec_audio_sr), format="wav")
+    buffer.seek(0)
+    return buffer.read()
+
+
+def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+        stop_repetition, sample_batch_size, kvcache, silence_tokens,
+        audio_path, word_info, transcript, smart_transcript,
+        mode, prompt_end_time, edit_start_time, edit_end_time,
+        split_text, selected_sentence, previous_audio_tensors):
+    if mode == "Long TTS":
+        if split_text == "Newline":
+            sentences = transcript.split('\n')
+        else:
+            from nltk.tokenize import sent_tokenize
+            sentences = sent_tokenize(transcript.replace("\n", " "))
+    elif mode == "Rerun":
+        colon_position = selected_sentence.find(':')
+        selected_sentence_idx = int(selected_sentence[:colon_position])
+        sentences = [selected_sentence[colon_position + 1:]]
+    else:
+        sentences = [transcript.replace("\n", " ")]
+
+    info = torchaudio.info(audio_path)
+    audio_dur = info.num_frames / info.sample_rate
+
+    audio_tensors = []
+    inference_transcript = ""
+    for sentence in sentences:
+        decode_config = {"top_k": top_k, "top_p": top_p, "temperature": temperature, "stop_repetition": stop_repetition,
+                         "kvcache": kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr,
+                         "silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
+        if mode != "Edit":
+            from inference_tts_scale import inference_one_sample
+
+            if smart_transcript:
+                target_transcript = ""
+                for word in word_info:
+                    if word["end"] < prompt_end_time:
+                        target_transcript += word["word"]
+                    elif (word["start"] + word["end"]) / 2 < prompt_end_time:
+                        # include part of the word it it's big, but adjust prompt_end_time
+                        target_transcript += word["word"]
+                        prompt_end_time = word["end"]
+                        break
+                    else:
+                        break
+                target_transcript += f" {sentence}"
+            else:
+                target_transcript = sentence
+
+            inference_transcript += target_transcript + "\n"
+
+            prompt_end_frame = int(min(audio_dur, prompt_end_time) * info.sample_rate)
+            _, gen_audio = inference_one_sample(voicecraft_model["model"],
+                                                voicecraft_model["ckpt"]["config"],
+                                                voicecraft_model["ckpt"]["phn2num"],
+                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
+                                                audio_path, target_transcript, device, decode_config,
+                                                prompt_end_frame)
+        else:
+            from inference_speech_editing_scale import inference_one_sample
+
+            if smart_transcript:
+                target_transcript = ""
+                for word in word_info:
+                    if word["start"] < edit_start_time:
+                        target_transcript += word["word"]
+                    else:
+                        break
+                target_transcript += f" {sentence}"
+                for word in word_info:
+                    if word["end"] > edit_end_time:
+                        target_transcript += word["word"]
+            else:
+                target_transcript = sentence
+
+            inference_transcript += target_transcript + "\n"
+
+            morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
+            mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
+            mask_interval = torch.LongTensor(mask_interval)
+            
+            _, gen_audio = inference_one_sample(voicecraft_model["model"],
+                                                voicecraft_model["ckpt"]["config"],
+                                                voicecraft_model["ckpt"]["phn2num"],
+                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
+                                                audio_path, target_transcript, mask_interval, device, decode_config)
+        gen_audio = gen_audio[0].cpu()
+        audio_tensors.append(gen_audio)
+
+    if mode != "Rerun":
+        output_audio = get_output_audio(audio_tensors, codec_audio_sr)
+        sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
+        component = gr.Dropdown(label="Sentence", choices=sentences, value=sentences[0],
+                                info="Select sentence you want to regenerate")
+        return output_audio, inference_transcript, component, audio_tensors
+    else:
+        previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
+        output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
+        sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
+        return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
+    
+    
+def update_input_audio(prompt_end_time, edit_start_time, edit_end_time):
+    def impl(audio_path):
+        info = torchaudio.info(audio_path)
+        max_time = round(info.num_frames / info.sample_rate, 2)
+        return [
+            prompt_end_time.update(maximum=max_time, value=max_time),
+            edit_start_time.update(maximum=max_time, value=0),
+            edit_end_time.update(maximum=max_time, value=max_time),
+        ]
+    return impl
+
+    
+def change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls):
+    def impl(mode):
+        return [
+            prompt_end_time.update(visible=mode != "Edit"),
+            split_text.update(visible=mode == "Long TTS"),
+            edit_word_mode.update(visible=mode == "Edit"),
+            segment_control.update(visible=mode == "Edit"),
+            precise_segment_control.update(visible=mode == "Edit"),
+            long_tts_controls.update(visible=mode == "Long TTS"),
+        ]
+    return impl
+
+
+def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
+    if selected_sentence is None:
+        return None
+    colon_position = selected_sentence.find(':')
+    selected_sentence_idx = int(selected_sentence[:colon_position])
+    return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
+
+
+def update_bound_word(is_first_word, edit_time):
+    def impl(selected_word, edit_word_mode):
+        word_start_time = float(selected_word.split(' ')[0])
+        word_end_time = float(selected_word.split(' ')[-1])
+        if edit_word_mode == "Replace half":
+            bound_time = (word_start_time + word_end_time) / 2
+        elif is_first_word:
+            bound_time = word_start_time
+        else:
+            bound_time = word_end_time
+
+        return edit_time.update(value=bound_time)
+    return impl
+
+
+def update_bound_words(edit_start_time, edit_end_time):
+    def impl(from_selected_word, to_selected_word, edit_word_mode):
+        return [
+            update_bound_word(True, edit_start_time)(from_selected_word, edit_word_mode),
+            update_bound_word(True, edit_end_time)(to_selected_word, edit_word_mode),
+        ]
+    return impl
+
+
+smart_transcript_info = """
+If enabled, the target transcript will be constructed for you:</br>
+ - In TTS and Long TTS mode just write the text you want to synthesize.</br>
+ - In Edit mode just write the text to replace selected editing segment.</br>
+If disabled, you should write the target transcript yourself:</br>
+ - In TTS mode write prompt transcript followed by generation transcript.</br>
+ - In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
+ - In Edit mode write full prompt</br>
+"""
+demo_text = {
+    "TTS": {
+        "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
+        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis as well!"
+    },
+    "Edit": {
+        "smart": "saw the mirage of the lake in the distance,",
+        "regular": "But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,"
+    },
+    "Long TTS": {
+        "smart": "You can run TTS on a big text!\n"
+                 "Just write it line-by-line. Or sentence-by-sentence.\n"
+                 "If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!",
+        "regular": "But when I had approached so near to them, the common You can run TTS on a big text!\n"
+                   "But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
+                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
+    }
+}
+all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
+demo_words = [
+    '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
+    '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
+    '3.72  which 3.78', '3.78  the 3.98', '3.98  sense 4.18', '4.18  deceives, 4.88', '5.06  lost 5.26', '5.26  not 5.74',
+    '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
+]
+
+
+def update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time):
+    def impl(mode, smart_transcript, edit_word_mode):
+        if transcript.value not in all_demo_texts:
+            return [transcript, edit_from_word, edit_to_word, prompt_end_time]
+        
+        replace_half = edit_word_mode == "Replace half"
+        return [
+            transcript.update(value=demo_text[mode]["smart" if smart_transcript else "regular"]),
+            edit_from_word.update(value="0.26  I 0.44" if replace_half else "0.44  had 0.6"),
+            edit_to_word.update(value="3.72  which 3.78" if replace_half else "2.9  object, 3.3"),
+            prompt_end_time.update(value=3.01),
+        ]
+    return impl
+
+
+with gr.Blocks() as app:
+    with gr.Row():
+        with gr.Column(scale=2):
+            load_models_btn = gr.Button(value="Load models")
+        with gr.Column(scale=5):
+            with gr.Accordion("Select models", open=False):
+                with gr.Row():
+                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
+                                                    choices=["tiny.en", "base.en", "small.en", "medium.en", "large"])
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=False)
+            with gr.Group():
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, interactive=False,
+                                                 info="Use whisper model to get the transcript. Fix it if necessary.")
+                with gr.Accordion("Word start time", open=False):
+                    transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
+                with gr.Accordion("Word end time", open=False):
+                    transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
+
+                transcribe_btn = gr.Button(value="Transcribe", interactive=False)
+            
+        with gr.Column(scale=3):
+            with gr.Group():
+                transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
+                with gr.Row():
+                    smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
+                    with gr.Accordion(label="?", open=False):
+                        info = gr.HTML(value=smart_transcript_info)
+                mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                
+                prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+                split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline", visible=False,
+                                      info="Split text into parts and run TTS for each part.")
+                edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half", visible=False,
+                                          info="What to do with first and last word")
+                with gr.Row(visible=False) as segment_control:
+                    edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, interactive=True)
+                    edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, interactive=True)
+                with gr.Accordion("Precise segment control", open=False, visible=False) as precise_segment_control:
+                    edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
+                    edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
+
+                run_btn = gr.Button(value="Run", interactive=False)
+
+        with gr.Column(scale=2):
+            output_audio = gr.Audio(label="Output Audio")
+            with gr.Accordion("Inference transcript", open=False):
+                inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
+                                                  info="Inference was performed on this transcript.")
+            with gr.Group(visible=False) as long_tts_controls:
+                sentence_selector = gr.Dropdown(label="Sentence", value=None,
+                                                info="Select sentence you want to regenerate")
+                sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
+                rerun_btn = gr.Button(value="Rerun", interactive=False)
+
+    with gr.Row():
+        with gr.Accordion("VoiceCraft config", open=False):
+            left_margin = gr.Number(label="left_margin", value=0.08)
+            right_margin = gr.Number(label="right_margin", value=0.08)
+            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
+            codec_sr = gr.Number(label="codec_sr", value=50)
+            top_k = gr.Number(label="top_k", value=0)
+            top_p = gr.Number(label="top_p", value=0.8)
+            temperature = gr.Number(label="temperature", value=1)
+            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
+                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
+            sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
+                                          info="generate this many samples and choose the shortest one")
+            kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
+                                info="set to 0 to use less VRAM, but with slower inference")
+            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
+
+    
+    audio_tensors = gr.State()
+    word_info = gr.State()
+
+    mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                inputs=[mode, smart_transcript, edit_word_mode],
+                outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    edit_word_mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                          inputs=[mode, smart_transcript, edit_word_mode],
+                          outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    smart_transcript.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
+                            inputs=[mode, smart_transcript, edit_word_mode],
+                            outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+    
+    load_models_btn.click(fn=load_models(input_audio, transcribe_btn, run_btn, rerun_btn),
+                          inputs=[whisper_model_choice, voicecraft_model_choice],
+                          outputs=[input_audio, transcribe_btn, run_btn, rerun_btn])
+
+    input_audio.change(fn=update_input_audio(prompt_end_time, edit_start_time, edit_end_time),
+                       inputs=[input_audio], outputs=[prompt_end_time, edit_start_time, edit_end_time])
+    transcribe_btn.click(fn=transcribe, inputs=[input_audio],
+                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
+
+    mode.change(fn=change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls),
+                inputs=[mode], outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+
+    run_btn.click(fn=run,
+                  inputs=[
+                      left_margin, right_margin,
+                      codec_audio_sr, codec_sr,
+                      top_k, top_p, temperature,
+                      stop_repetition, sample_batch_size,
+                      kvcache, silence_tokens,
+                      input_audio, word_info, transcript, smart_transcript,
+                      mode, prompt_end_time, edit_start_time, edit_end_time,
+                      split_text, sentence_selector, audio_tensors
+                  ],
+                  outputs=[
+                      output_audio, inference_transcript, sentence_selector, audio_tensors
+                  ])
+    
+    sentence_selector.change(fn=load_sentence, inputs=[sentence_selector, codec_audio_sr, audio_tensors], outputs=[sentence_audio])
+    rerun_btn.click(fn=run,
+                    inputs=[
+                        left_margin, right_margin,
+                        codec_audio_sr, codec_sr,
+                        top_k, top_p, temperature,
+                        stop_repetition, sample_batch_size,
+                        kvcache, silence_tokens,
+                        input_audio, word_info, transcript, smart_transcript,
+                        gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                        split_text, sentence_selector, audio_tensors
+                    ],
+                    outputs=[
+                        output_audio, inference_transcript, sentence_audio, audio_tensors
+                    ])
+    
+    edit_word_mode.change(fn=update_bound_words(edit_start_time, edit_end_time),
+                          inputs=[edit_from_word, edit_to_word, edit_word_mode], outputs=[edit_start_time, edit_end_time])
+    edit_from_word.change(fn=update_bound_word(True, edit_start_time),
+                          inputs=[edit_from_word, edit_word_mode], outputs=[edit_start_time])
+    edit_to_word.change(fn=update_bound_word(False, edit_end_time),
+                        inputs=[edit_to_word, edit_word_mode], outputs=[edit_end_time])
+
+
+if __name__ == "__main__":
+    app.launch()
\ No newline at end of file
diff --git a/gradio_requirements.txt b/gradio_requirements.txt
new file mode 100644
index 0000000..949acc7
--- /dev/null
+++ b/gradio_requirements.txt
@@ -0,0 +1,3 @@
+gradio==3.50.2
+nltk>=3.8.1
+openai-whisper>=20231117
\ No newline at end of file
diff --git a/start-jupyter.bat b/start-jupyter.bat
index fc69502..eb98b66 100644
--- a/start-jupyter.bat
+++ b/start-jupyter.bat
@@ -5,6 +5,7 @@ echo Creating and running the Jupyter container...
 docker run -it -d ^
     --gpus all ^
     -p 8888:8888 ^
+    -p 7860:7860 ^
     --name jupyter ^
     --user root ^
     -e NB_USER="%username%" ^
diff --git a/start-jupyter.sh b/start-jupyter.sh
index 5888bfb..cb54572 100755
--- a/start-jupyter.sh
+++ b/start-jupyter.sh
@@ -8,6 +8,7 @@ docker run -it \
     -d \
     --gpus all \
     -p 8888:8888 \
+    -p 7860:7860 \
     --name jupyter \
     --user root \
     -e NB_USER="$USER" \

From 74fa65979d65acc74bdf7cb05c2eaa65c877acca Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 05:01:55 +0300
Subject: [PATCH 02/40] deprecated .update not used anymore, better error
 handling, can use voicecraft without whisper

---
 gradio_app.py | 289 ++++++++++++++++++++++++++------------------------
 1 file changed, 149 insertions(+), 140 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 956175c..cab37c4 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -6,62 +6,63 @@ from data.tokenizer import (
     TextTokenizer,
 )
 from models import voicecraft
-import whisper
-from whisper.tokenizer import get_tokenizer
 import os
 import io
 
 
-whisper_model = None
-voicecraft_model = None
-device = "cuda" if torch.cuda.is_available() else "cpu"
+def load_models(whisper_model_choice, voicecraft_model_choice):
+    whisper_model, voicecraft_model = None, None
+    if whisper_model_choice is not None:
+        import whisper
+        from whisper.tokenizer import get_tokenizer
+        whisper_model = {
+            "model": whisper.load_model(whisper_model_choice),
+            "tokenizer": get_tokenizer(multilingual=False)
+        }
+
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    voicecraft_name = f"{voicecraft_model_choice}.pth"
+    ckpt_fn = f"./pretrained_models/{voicecraft_name}"
+    encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+    if not os.path.exists(ckpt_fn):
+        os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
+        os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+    if not os.path.exists(encodec_fn):
+        os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
+        os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+
+    ckpt = torch.load(ckpt_fn, map_location="cpu")
+    model = voicecraft.VoiceCraft(ckpt["config"])
+    model.load_state_dict(ckpt["model"])
+    model.to(device)
+    model.eval()
+    voicecraft_model = {
+        "ckpt": ckpt,
+        "model": model,
+        "text_tokenizer": TextTokenizer(backend="espeak"),
+        "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
+    }
+
+    return [
+        whisper_model,
+        voicecraft_model,
+        gr.Audio(interactive=True),
+    ]
 
 
-def load_models(input_audio, transcribe_btn, run_btn, rerun_btn):
-    def impl(whisper_model_choice, voicecraft_model_choice):
-        global whisper_model, voicecraft_model
-        whisper_model = whisper.load_model(whisper_model_choice)
-
-        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-        
-        voicecraft_name = f"{voicecraft_model_choice}.pth"
-        ckpt_fn = f"./pretrained_models/{voicecraft_name}"
-        encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
-        if not os.path.exists(ckpt_fn):
-            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
-            os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
-        if not os.path.exists(encodec_fn):
-            os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
-            os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
-
-        voicecraft_model = {}
-        voicecraft_model["ckpt"] = torch.load(ckpt_fn, map_location="cpu")
-        voicecraft_model["model"] = voicecraft.VoiceCraft(voicecraft_model["ckpt"]["config"])
-        voicecraft_model["model"].load_state_dict(voicecraft_model["ckpt"]["model"])
-        voicecraft_model["model"].to(device)
-        voicecraft_model["model"].eval()
-
-        voicecraft_model["text_tokenizer"] = TextTokenizer(backend="espeak")
-        voicecraft_model["audio_tokenizer"] = AudioTokenizer(signature=encodec_fn)
-
-        return [
-            input_audio.update(interactive=True),
-            transcribe_btn.update(interactive=True),
-            run_btn.update(interactive=True),
-            rerun_btn.update(interactive=True)
-        ]
-    return impl
-
-
-def transcribe(audio_path):
-    tokenizer = get_tokenizer(multilingual=False)
+def transcribe(whisper_model, audio_path):
+    if whisper_model is None:
+        raise gr.Error("Whisper model not loaded")
+    
     number_tokens = [
         i
-        for i in range(tokenizer.eot)
-        if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+        for i in range(whisper_model["tokenizer"].eot)
+        if all(c in "0123456789" for c in whisper_model["tokenizer"].decode([i]).removeprefix(" "))
     ]
-    result = whisper_model.transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
+    result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
     words = [word_info for segment in result["segments"] for word_info in segment["words"]]
     
     transcript = result["text"]
@@ -69,12 +70,12 @@ def transcribe(audio_path):
     transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
 
     choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
-    edit_from_word = gr.Dropdown(label="First word to edit", value=choices[0], choices=choices, interactive=True)
-    edit_to_word = gr.Dropdown(label="Last word to edit", value=choices[-1], choices=choices, interactive=True)
 
     return [
         transcript, transcript_with_start_time, transcript_with_end_time,
-        edit_from_word, edit_to_word, words
+        gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
+        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
+        words
     ]
 
 
@@ -86,11 +87,16 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(voicecraft_model, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
         split_text, selected_sentence, previous_audio_tensors):
+    if voicecraft_model is None:
+        raise gr.Error("VoiceCraft model not loaded")
+    if smart_transcript and (word_info is None):
+        raise gr.Error("Can't use smart transcript: whisper transcript not found")
+
     if mode == "Long TTS":
         if split_text == "Newline":
             sentences = transcript.split('\n')
@@ -104,6 +110,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     else:
         sentences = [transcript.replace("\n", " ")]
 
+    device = "cuda" if torch.cuda.is_available() else "cpu"
     info = torchaudio.info(audio_path)
     audio_dur = info.num_frames / info.sample_rate
 
@@ -116,7 +123,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
         if mode != "Edit":
             from inference_tts_scale import inference_one_sample
 
-            if smart_transcript:
+            if smart_transcript:                
                 target_transcript = ""
                 for word in word_info:
                     if word["end"] < prompt_end_time:
@@ -175,8 +182,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     if mode != "Rerun":
         output_audio = get_output_audio(audio_tensors, codec_audio_sr)
         sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
-        component = gr.Dropdown(label="Sentence", choices=sentences, value=sentences[0],
-                                info="Select sentence you want to regenerate")
+        component = gr.Dropdown(choices=sentences, value=sentences[0])
         return output_audio, inference_transcript, component, audio_tensors
     else:
         previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
@@ -185,29 +191,25 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
         return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
     
     
-def update_input_audio(prompt_end_time, edit_start_time, edit_end_time):
-    def impl(audio_path):
-        info = torchaudio.info(audio_path)
-        max_time = round(info.num_frames / info.sample_rate, 2)
-        return [
-            prompt_end_time.update(maximum=max_time, value=max_time),
-            edit_start_time.update(maximum=max_time, value=0),
-            edit_end_time.update(maximum=max_time, value=max_time),
-        ]
-    return impl
+def update_input_audio(audio_path):
+    info = torchaudio.info(audio_path)
+    max_time = round(info.num_frames / info.sample_rate, 2)
+    return [
+        gr.Slider(maximum=max_time, value=max_time),
+        gr.Slider(maximum=max_time, value=0),
+        gr.Slider(maximum=max_time, value=max_time),
+    ]
 
     
-def change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls):
-    def impl(mode):
-        return [
-            prompt_end_time.update(visible=mode != "Edit"),
-            split_text.update(visible=mode == "Long TTS"),
-            edit_word_mode.update(visible=mode == "Edit"),
-            segment_control.update(visible=mode == "Edit"),
-            precise_segment_control.update(visible=mode == "Edit"),
-            long_tts_controls.update(visible=mode == "Long TTS"),
-        ]
-    return impl
+def change_mode(mode):
+    return [
+        gr.Slider(visible=mode != "Edit"),
+        gr.Radio(visible=mode == "Long TTS"),
+        gr.Radio(visible=mode == "Edit"),
+        gr.Row(visible=mode == "Edit"),
+        gr.Accordion(visible=mode == "Edit"),
+        gr.Group(visible=mode == "Long TTS"),
+    ]
 
 
 def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
@@ -218,28 +220,27 @@ def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
     return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
 
 
-def update_bound_word(is_first_word, edit_time):
-    def impl(selected_word, edit_word_mode):
-        word_start_time = float(selected_word.split(' ')[0])
-        word_end_time = float(selected_word.split(' ')[-1])
-        if edit_word_mode == "Replace half":
-            bound_time = (word_start_time + word_end_time) / 2
-        elif is_first_word:
-            bound_time = word_start_time
-        else:
-            bound_time = word_end_time
+def update_bound_word(is_first_word, selected_word, edit_word_mode):
+    if selected_word is None:
+        return None
 
-        return edit_time.update(value=bound_time)
-    return impl
+    word_start_time = float(selected_word.split(' ')[0])
+    word_end_time = float(selected_word.split(' ')[-1])
+    if edit_word_mode == "Replace half":
+        bound_time = (word_start_time + word_end_time) / 2
+    elif is_first_word:
+        bound_time = word_start_time
+    else:
+        bound_time = word_end_time
+
+    return bound_time
 
 
-def update_bound_words(edit_start_time, edit_end_time):
-    def impl(from_selected_word, to_selected_word, edit_word_mode):
-        return [
-            update_bound_word(True, edit_start_time)(from_selected_word, edit_word_mode),
-            update_bound_word(True, edit_end_time)(to_selected_word, edit_word_mode),
-        ]
-    return impl
+def update_bound_words(from_selected_word, to_selected_word, edit_word_mode):
+    return [
+        update_bound_word(True, from_selected_word, edit_word_mode),
+        update_bound_word(False, to_selected_word, edit_word_mode),
+    ]
 
 
 smart_transcript_info = """
@@ -251,6 +252,7 @@ If disabled, you should write the target transcript yourself:</br>
  - In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
  - In Edit mode write full prompt</br>
 """
+
 demo_text = {
     "TTS": {
         "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
@@ -269,7 +271,9 @@ demo_text = {
                    "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
     }
 }
+
 all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
+
 demo_words = [
     '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
     '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
@@ -278,19 +282,17 @@ demo_words = [
 ]
 
 
-def update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time):
-    def impl(mode, smart_transcript, edit_word_mode):
-        if transcript.value not in all_demo_texts:
-            return [transcript, edit_from_word, edit_to_word, prompt_end_time]
-        
-        replace_half = edit_word_mode == "Replace half"
-        return [
-            transcript.update(value=demo_text[mode]["smart" if smart_transcript else "regular"]),
-            edit_from_word.update(value="0.26  I 0.44" if replace_half else "0.44  had 0.6"),
-            edit_to_word.update(value="3.72  which 3.78" if replace_half else "2.9  object, 3.3"),
-            prompt_end_time.update(value=3.01),
-        ]
-    return impl
+def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time):
+    if transcript not in all_demo_texts:
+        return transcript, edit_from_word, edit_to_word, prompt_end_time
+    
+    replace_half = edit_word_mode == "Replace half"
+    return [
+        demo_text[mode]["smart" if smart_transcript else "regular"],
+        "0.26  I 0.44" if replace_half else "0.44  had 0.6",
+        "3.72  which 3.78" if replace_half else "2.9  object, 3.3",
+        3.01,
+    ]
 
 
 with gr.Blocks() as app:
@@ -302,7 +304,7 @@ with gr.Blocks() as app:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=["tiny.en", "base.en", "small.en", "medium.en", "large"])
+                                                    choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
 
     with gr.Row():
         with gr.Column(scale=2):
@@ -315,7 +317,7 @@ with gr.Blocks() as app:
                 with gr.Accordion("Word end time", open=False):
                     transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
 
-                transcribe_btn = gr.Button(value="Transcribe", interactive=False)
+                transcribe_btn = gr.Button(value="Transcribe")
             
         with gr.Column(scale=3):
             with gr.Group():
@@ -338,7 +340,7 @@ with gr.Blocks() as app:
                     edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
                     edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
 
-                run_btn = gr.Button(value="Run", interactive=False)
+                run_btn = gr.Button(value="Run")
 
         with gr.Column(scale=2):
             output_audio = gr.Audio(label="Output Audio")
@@ -349,7 +351,7 @@ with gr.Blocks() as app:
                 sentence_selector = gr.Dropdown(label="Sentence", value=None,
                                                 info="Select sentence you want to regenerate")
                 sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
-                rerun_btn = gr.Button(value="Rerun", interactive=False)
+                rerun_btn = gr.Button(value="Rerun")
 
     with gr.Row():
         with gr.Accordion("VoiceCraft config", open=False):
@@ -369,34 +371,40 @@ with gr.Blocks() as app:
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
 
     
+    whisper_model = gr.State()
+    voicecraft_model = gr.State()
     audio_tensors = gr.State()
     word_info = gr.State()
 
-    mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                inputs=[mode, smart_transcript, edit_word_mode],
+    
+    mode.change(fn=update_demo,
+                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                 outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
-    edit_word_mode.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                          inputs=[mode, smart_transcript, edit_word_mode],
+    edit_word_mode.change(fn=update_demo,
+                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                           outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
-    smart_transcript.change(fn=update_demo(transcript, edit_from_word, edit_to_word, prompt_end_time),
-                            inputs=[mode, smart_transcript, edit_word_mode],
+    smart_transcript.change(fn=update_demo,
+                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
                             outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
     
-    load_models_btn.click(fn=load_models(input_audio, transcribe_btn, run_btn, rerun_btn),
+    load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[input_audio, transcribe_btn, run_btn, rerun_btn])
-
-    input_audio.change(fn=update_input_audio(prompt_end_time, edit_start_time, edit_end_time),
-                       inputs=[input_audio], outputs=[prompt_end_time, edit_start_time, edit_end_time])
-    transcribe_btn.click(fn=transcribe, inputs=[input_audio],
+                          outputs=[whisper_model, voicecraft_model, input_audio])
+    
+    input_audio.change(fn=update_input_audio,
+                       inputs=[input_audio],
+                       outputs=[prompt_end_time, edit_start_time, edit_end_time])
+    transcribe_btn.click(fn=transcribe,
+                         inputs=[whisper_model, input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
 
-    mode.change(fn=change_mode(prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls),
-                inputs=[mode], outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+    mode.change(fn=change_mode,
+                inputs=[mode],
+                outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
 
     run_btn.click(fn=run,
                   inputs=[
-                      left_margin, right_margin,
+                      voicecraft_model, left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -405,14 +413,14 @@ with gr.Blocks() as app:
                       mode, prompt_end_time, edit_start_time, edit_end_time,
                       split_text, sentence_selector, audio_tensors
                   ],
-                  outputs=[
-                      output_audio, inference_transcript, sentence_selector, audio_tensors
-                  ])
+                  outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
     
-    sentence_selector.change(fn=load_sentence, inputs=[sentence_selector, codec_audio_sr, audio_tensors], outputs=[sentence_audio])
+    sentence_selector.change(fn=load_sentence,
+                             inputs=[sentence_selector, codec_audio_sr, audio_tensors],
+                             outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        left_margin, right_margin,
+                        voicecraft_model, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
@@ -421,16 +429,17 @@ with gr.Blocks() as app:
                         gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
-                    outputs=[
-                        output_audio, inference_transcript, sentence_audio, audio_tensors
-                    ])
+                    outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
     
-    edit_word_mode.change(fn=update_bound_words(edit_start_time, edit_end_time),
-                          inputs=[edit_from_word, edit_to_word, edit_word_mode], outputs=[edit_start_time, edit_end_time])
-    edit_from_word.change(fn=update_bound_word(True, edit_start_time),
-                          inputs=[edit_from_word, edit_word_mode], outputs=[edit_start_time])
-    edit_to_word.change(fn=update_bound_word(False, edit_end_time),
-                        inputs=[edit_to_word, edit_word_mode], outputs=[edit_end_time])
+    edit_from_word.change(fn=update_bound_word,
+                          inputs=[gr.State(True), edit_from_word, edit_word_mode],
+                          outputs=[edit_start_time])
+    edit_to_word.change(fn=update_bound_word,
+                        inputs=[gr.State(False), edit_to_word, edit_word_mode],
+                        outputs=[edit_end_time])
+    edit_word_mode.change(fn=update_bound_words,
+                          inputs=[edit_from_word, edit_to_word, edit_word_mode],
+                          outputs=[edit_start_time, edit_end_time])
 
 
 if __name__ == "__main__":

From f9fed26b15515697965e7da9bad3d46eadc96816 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 05:16:22 +0300
Subject: [PATCH 03/40] global models

---
 gradio_app.py | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index cab37c4..4124444 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -10,8 +10,12 @@ import os
 import io
 
 
+whisper_model, voicecraft_model = None, None
+
+
 def load_models(whisper_model_choice, voicecraft_model_choice):
-    whisper_model, voicecraft_model = None, None
+    global whisper_model, voicecraft_model
+
     if whisper_model_choice is not None:
         import whisper
         from whisper.tokenizer import get_tokenizer
@@ -46,14 +50,10 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
         "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
     }
 
-    return [
-        whisper_model,
-        voicecraft_model,
-        gr.Audio(interactive=True),
-    ]
+    return gr.Audio(interactive=True)
 
 
-def transcribe(whisper_model, audio_path):
+def transcribe(audio_path):
     if whisper_model is None:
         raise gr.Error("Whisper model not loaded")
     
@@ -87,7 +87,7 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(voicecraft_model, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
@@ -371,8 +371,6 @@ with gr.Blocks() as app:
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
 
     
-    whisper_model = gr.State()
-    voicecraft_model = gr.State()
     audio_tensors = gr.State()
     word_info = gr.State()
 
@@ -389,13 +387,13 @@ with gr.Blocks() as app:
     
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[whisper_model, voicecraft_model, input_audio])
+                          outputs=[input_audio])
     
     input_audio.change(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
     transcribe_btn.click(fn=transcribe,
-                         inputs=[whisper_model, input_audio],
+                         inputs=[input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
 
     mode.change(fn=change_mode,
@@ -404,7 +402,7 @@ with gr.Blocks() as app:
 
     run_btn.click(fn=run,
                   inputs=[
-                      voicecraft_model, left_margin, right_margin,
+                      left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -420,7 +418,7 @@ with gr.Blocks() as app:
                              outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        voicecraft_model, left_margin, right_margin,
+                        left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,

From 1a219cf6da69df070cf03966f06433c9a0362de2 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Wed, 3 Apr 2024 20:24:34 +0300
Subject: [PATCH 04/40] bugfixes, seed support, better ui

---
 gradio_app.py | 136 ++++++++++++++++++++++++++++++++++----------------
 1 file changed, 93 insertions(+), 43 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 4124444..707defa 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -8,11 +8,24 @@ from data.tokenizer import (
 from models import voicecraft
 import os
 import io
+import numpy as np
+import random
 
 
 whisper_model, voicecraft_model = None, None
 
 
+def seed_everything(seed):
+    if seed != -1:
+        os.environ['PYTHONHASHSEED'] = str(seed)
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
+
 def load_models(whisper_model_choice, voicecraft_model_choice):
     global whisper_model, voicecraft_model
 
@@ -50,12 +63,13 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
         "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
     }
 
-    return gr.Audio(interactive=True)
+    return gr.Accordion()
 
 
-def transcribe(audio_path):
+def transcribe(seed, audio_path):
     if whisper_model is None:
         raise gr.Error("Whisper model not loaded")
+    seed_everything(seed)
     
     number_tokens = [
         i
@@ -73,6 +87,7 @@ def transcribe(audio_path):
 
     return [
         transcript, transcript_with_start_time, transcript_with_end_time,
+        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # prompt_to_word
         gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
         gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
         words
@@ -87,7 +102,7 @@ def get_output_audio(audio_tensors, codec_audio_sr):
     return buffer.read()
 
 
-def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
+def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
         audio_path, word_info, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
@@ -97,6 +112,7 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     if smart_transcript and (word_info is None):
         raise gr.Error("Can't use smart transcript: whisper transcript not found")
 
+    seed_everything(seed)
     if mode == "Long TTS":
         if split_text == "Newline":
             sentences = transcript.split('\n')
@@ -192,6 +208,9 @@ def run(left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, tempe
     
     
 def update_input_audio(audio_path):
+    if audio_path is None:
+        return 0, 0, 0
+    
     info = torchaudio.info(audio_path)
     max_time = round(info.num_frames / info.sample_rate, 2)
     return [
@@ -202,12 +221,12 @@ def update_input_audio(audio_path):
 
     
 def change_mode(mode):
+    tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
     return [
-        gr.Slider(visible=mode != "Edit"),
-        gr.Radio(visible=mode == "Long TTS"),
+        gr.Group(visible=mode != "Edit"),
+        gr.Group(visible=mode == "Edit"),
         gr.Radio(visible=mode == "Edit"),
-        gr.Row(visible=mode == "Edit"),
-        gr.Accordion(visible=mode == "Edit"),
+        gr.Radio(visible=mode == "Long TTS"),
         gr.Group(visible=mode == "Long TTS"),
     ]
 
@@ -253,6 +272,8 @@ If disabled, you should write the target transcript yourself:</br>
  - In Edit mode write full prompt</br>
 """
 
+demo_original_transcript = " But when I had approached so near to them, the common object, which the sense deceives, lost not by distance any of its marks."
+
 demo_text = {
     "TTS": {
         "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
@@ -281,17 +302,35 @@ demo_words = [
     '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
 ]
 
+demo_word_info = [
+    {'word': ' But', 'start': 0.0, 'end': 0.12},  {'word': ' when', 'start': 0.12, 'end': 0.26},
+    {'word': ' I', 'start': 0.26, 'end': 0.44}, {'word': ' had', 'start': 0.44, 'end': 0.6},
+    {'word': ' approached', 'start': 0.6, 'end': 0.94}, {'word': ' so', 'start': 0.94, 'end': 1.42},
+    {'word': ' near', 'start': 1.42, 'end': 1.78}, {'word': ' to', 'start': 1.78, 'end': 2.02},
+    {'word': ' them,', 'start': 2.02, 'end': 2.24}, {'word': ' the', 'start': 2.52, 'end': 2.58},
+    {'word': ' common', 'start': 2.58, 'end': 2.9}, {'word': ' object,', 'start': 2.9, 'end': 3.3},
+    {'word': ' which', 'start': 3.72, 'end': 3.78}, {'word': ' the', 'start': 3.78, 'end': 3.98},
+    {'word': ' sense', 'start': 3.98, 'end': 4.18}, {'word': ' deceives,', 'start': 4.18, 'end': 4.88},
+    {'word': ' lost', 'start': 5.06, 'end': 5.26}, {'word': ' not', 'start': 5.26, 'end': 5.74},
+    {'word': ' by', 'start': 5.74, 'end': 6.08}, {'word': ' distance', 'start': 6.08, 'end': 6.36},
+    {'word': ' any', 'start': 6.36, 'end': 6.92}, {'word': ' of', 'start': 6.92, 'end': 7.12},
+    {'word': ' its', 'start': 7.12, 'end': 7.26}, {'word': ' marks.', 'start': 7.26, 'end': 7.54}
+]
 
-def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time):
+
+def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
     if transcript not in all_demo_texts:
-        return transcript, edit_from_word, edit_to_word, prompt_end_time
+        return transcript, edit_from_word, edit_to_word
     
     replace_half = edit_word_mode == "Replace half"
+    change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
+    change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
+    demo_edit_from_word_value = demo_words[2] if replace_half else demo_words[3]
+    demo_edit_to_word_value = demo_words[12] if replace_half else demo_words[11]
     return [
         demo_text[mode]["smart" if smart_transcript else "regular"],
-        "0.26  I 0.44" if replace_half else "0.44  had 0.6",
-        "3.72  which 3.78" if replace_half else "2.9  object, 3.3",
-        3.01,
+        demo_edit_from_word_value if change_edit_from_word else edit_from_word,
+        demo_edit_to_word_value if change_edit_to_word else edit_to_word,
     ]
 
 
@@ -300,7 +339,7 @@ with gr.Blocks() as app:
         with gr.Column(scale=2):
             load_models_btn = gr.Button(value="Load models")
         with gr.Column(scale=5):
-            with gr.Accordion("Select models", open=False):
+            with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
@@ -308,9 +347,9 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=False)
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
             with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, interactive=False,
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
                                                  info="Use whisper model to get the transcript. Fix it if necessary.")
                 with gr.Accordion("Word start time", open=False):
                     transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
@@ -325,20 +364,26 @@ with gr.Blocks() as app:
                 with gr.Row():
                     smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
                     with gr.Accordion(label="?", open=False):
-                        info = gr.HTML(value=smart_transcript_info)
-                mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                        info = gr.Markdown(value=smart_transcript_info)
+
+                with gr.Row():
+                    mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                    split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
+                                          info="Split text into parts and run TTS for each part.", visible=False)
+                    edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
+                                              info="What to do with first and last word", visible=False)
                 
-                prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
-                split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline", visible=False,
-                                      info="Split text into parts and run TTS for each part.")
-                edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half", visible=False,
-                                          info="What to do with first and last word")
-                with gr.Row(visible=False) as segment_control:
-                    edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, interactive=True)
-                    edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, interactive=True)
-                with gr.Accordion("Precise segment control", open=False, visible=False) as precise_segment_control:
-                    edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=60, step=0.01, value=0)
-                    edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=60, step=0.01, value=60)
+                with gr.Group() as tts_mode_controls:
+                    prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
+                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+
+                with gr.Group(visible=False) as edit_mode_controls:
+                    with gr.Row():
+                        edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
+                        edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                    with gr.Row():
+                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.01, value=0.35)
+                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.01, value=3.75)
 
                 run_btn = gr.Button(value="Run")
 
@@ -347,7 +392,7 @@ with gr.Blocks() as app:
             with gr.Accordion("Inference transcript", open=False):
                 inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
                                                   info="Inference was performed on this transcript.")
-            with gr.Group(visible=False) as long_tts_controls:
+            with gr.Group(visible=False) as long_tts_sentence_editor:
                 sentence_selector = gr.Dropdown(label="Sentence", value=None,
                                                 info="Select sentence you want to regenerate")
                 sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
@@ -355,6 +400,7 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Accordion("VoiceCraft config", open=False):
+            seed = gr.Number(label="seed", value=-1, precision=0)
             left_margin = gr.Number(label="left_margin", value=0.08)
             right_margin = gr.Number(label="right_margin", value=0.08)
             codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
@@ -372,37 +418,38 @@ with gr.Blocks() as app:
 
     
     audio_tensors = gr.State()
-    word_info = gr.State()
+    word_info = gr.State(value=demo_word_info)
 
     
     mode.change(fn=update_demo,
-                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                outputs=[transcript, edit_from_word, edit_to_word])
     edit_word_mode.change(fn=update_demo,
-                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                          outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                          outputs=[transcript, edit_from_word, edit_to_word])
     smart_transcript.change(fn=update_demo,
-                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word, prompt_end_time],
-                            outputs=[transcript, edit_from_word, edit_to_word, prompt_end_time])
+                            inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                            outputs=[transcript, edit_from_word, edit_to_word])
     
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_model_choice, voicecraft_model_choice],
-                          outputs=[input_audio])
+                          outputs=[models_selector])
     
-    input_audio.change(fn=update_input_audio,
+    input_audio.upload(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
     transcribe_btn.click(fn=transcribe,
-                         inputs=[input_audio],
-                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time, edit_from_word, edit_to_word, word_info])
+                         inputs=[seed, input_audio],
+                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
+                                  prompt_to_word, edit_from_word, edit_to_word, word_info])
 
     mode.change(fn=change_mode,
                 inputs=[mode],
-                outputs=[prompt_end_time, split_text, edit_word_mode, segment_control, precise_segment_control, long_tts_controls])
+                outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
 
     run_btn.click(fn=run,
                   inputs=[
-                      left_margin, right_margin,
+                      seed, left_margin, right_margin,
                       codec_audio_sr, codec_sr,
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
@@ -418,7 +465,7 @@ with gr.Blocks() as app:
                              outputs=[sentence_audio])
     rerun_btn.click(fn=run,
                     inputs=[
-                        left_margin, right_margin,
+                        seed, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
@@ -429,6 +476,9 @@ with gr.Blocks() as app:
                     ],
                     outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
     
+    prompt_to_word.change(fn=update_bound_word,
+                          inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
+                          outputs=[prompt_end_time])
     edit_from_word.change(fn=update_bound_word,
                           inputs=[gr.State(True), edit_from_word, edit_word_mode],
                           outputs=[edit_start_time])

From 3d3f32ba7e3bfb3ef2d7514c6081911f06d5fb2b Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 4 Apr 2024 22:22:27 +0300
Subject: [PATCH 05/40] whisperx added

---
 README.md               |   2 +-
 gradio_app.py           | 231 +++++++++++++++++++++++++++++-----------
 gradio_requirements.txt |   4 +-
 3 files changed, 170 insertions(+), 67 deletions(-)

diff --git a/README.md b/README.md
index 2a5be3a..5e78082 100644
--- a/README.md
+++ b/README.md
@@ -111,7 +111,7 @@ It is ready to use on [default url](http://127.0.0.1:7860).
 6. (optionally) Rerun part-by-part in Long TTS mode
 
 ### Some features
-Smart transcript: write only what you want to generate, but don't work if you edit original transcript
+Smart transcript: write only what you want to generate
 
 TTS mode: Zero-shot TTS
 
diff --git a/gradio_app.py b/gradio_app.py
index 707defa..4321a11 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -10,9 +10,16 @@ import os
 import io
 import numpy as np
 import random
+import uuid
 
 
-whisper_model, voicecraft_model = None, None
+TMP_PATH = "./demo/temp"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+whisper_model, align_model, voicecraft_model = None, None, None
+
+
+def get_random_string():
+    return "".join(str(uuid.uuid4()).split("-"))
 
 
 def seed_everything(seed):
@@ -26,22 +33,63 @@ def seed_everything(seed):
         torch.backends.cudnn.deterministic = True
 
 
-def load_models(whisper_model_choice, voicecraft_model_choice):
-    global whisper_model, voicecraft_model
+class WhisperxAlignModel:
+    def __init__(self):
+        from whisperx import load_align_model
+        self.model, self.metadata = load_align_model(language_code="en", device=device)
+
+    def align(self, segments, audio_path):
+        from whisperx import align, load_audio
+        audio = load_audio(audio_path)
+        return align(segments, self.model, self.metadata, audio, device, return_char_alignments=False)["segments"]
+
+
+class WhisperModel:
+    def __init__(self, model_name):
+        from whisper import load_model
+        self.model = load_model(model_name, device)
 
-    if whisper_model_choice is not None:
-        import whisper
         from whisper.tokenizer import get_tokenizer
-        whisper_model = {
-            "model": whisper.load_model(whisper_model_choice),
-            "tokenizer": get_tokenizer(multilingual=False)
-        }
+        tokenizer = get_tokenizer(multilingual=False)
+        self.supress_tokens = [-1] + [
+            i
+            for i in range(tokenizer.eot)
+            if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
+        ]
+
+    def transcribe(self, audio_path):
+        return self.model.transcribe(audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True)["segments"]
+
+
+class WhisperxModel:
+    def __init__(self, model_name, align_model: WhisperxAlignModel):
+        from whisperx import load_model
+        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True})
+        self.align_model = align_model
+
+    def transcribe(self, audio_path):
+        segments = self.model.transcribe(audio_path, batch_size=8)["segments"]
+        return self.align_model.align(segments, audio_path)
+
+
+def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
+    global transcribe_model, align_model, voicecraft_model
 
     os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
     os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if alignment_model_name is not None:
+        align_model = WhisperxAlignModel()
+
+    if whisper_model_name is not None:
+        if whisper_backend_name == "whisper":
+            transcribe_model = WhisperModel(whisper_model_name)
+        else:
+            if align_model is None:
+                raise gr.Error("Align model required for whisperx backend")
+            transcribe_model = WhisperxModel(whisper_model_name, align_model)
     
-    voicecraft_name = f"{voicecraft_model_choice}.pth"
+    voicecraft_name = f"{voicecraft_model_name}.pth"
     ckpt_fn = f"./pretrained_models/{voicecraft_name}"
     encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
     if not os.path.exists(ckpt_fn):
@@ -66,31 +114,78 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
     return gr.Accordion()
 
 
+def get_transcribe_state(segments):
+    words_info = [word_info for segment in segments for word_info in segment["words"]]
+    return {
+        "segments": segments,
+        "transcript": " ".join([segment["text"] for segment in segments]),
+        "words_info": words_info,
+        "transcript_with_start_time": " ".join([f"{word['start']} {word['word']}" for word in words_info]),
+        "transcript_with_end_time": " ".join([f"{word['word']} {word['end']}" for word in words_info]),
+        "word_bounds": [f"{word['start']} {word['word']} {word['end']}" for word in words_info]
+    }
+
+
 def transcribe(seed, audio_path):
-    if whisper_model is None:
-        raise gr.Error("Whisper model not loaded")
+    if transcribe_model is None:
+        raise gr.Error("Transcription model not loaded")
     seed_everything(seed)
     
-    number_tokens = [
-        i
-        for i in range(whisper_model["tokenizer"].eot)
-        if all(c in "0123456789" for c in whisper_model["tokenizer"].decode([i]).removeprefix(" "))
-    ]
-    result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
-    words = [word_info for segment in result["segments"] for word_info in segment["words"]]
-    
-    transcript = result["text"]
-    transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
-    transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
-
-    choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
+    segments = transcribe_model.transcribe(audio_path)
+    state = get_transcribe_state(segments)
 
     return [
-        transcript, transcript_with_start_time, transcript_with_end_time,
-        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # prompt_to_word
-        gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
-        gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
-        words
+        state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
+        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
+        state
+    ]
+
+
+def align_segments(transcript, audio_path):
+    from aeneas.executetask import ExecuteTask
+    from aeneas.task import Task
+    import json
+    config_string = 'task_language=eng|os_task_file_format=json|is_text_type=plain'
+
+    tmp_transcript_path = os.path.join(TMP_PATH, f"{get_random_string()}.txt")
+    tmp_sync_map_path = os.path.join(TMP_PATH, f"{get_random_string()}.json")
+    with open(tmp_transcript_path, "w") as f:
+        f.write(transcript)
+
+    task = Task(config_string=config_string)
+    task.audio_file_path_absolute = os.path.abspath(audio_path)
+    task.text_file_path_absolute = os.path.abspath(tmp_transcript_path)
+    task.sync_map_file_path_absolute = os.path.abspath(tmp_sync_map_path)
+    ExecuteTask(task).execute()
+    task.output_sync_map_file()
+
+    with open(tmp_sync_map_path, "r") as f:
+        return json.load(f)
+
+
+def align(seed, transcript, audio_path):
+    if align_model is None:
+        raise gr.Error("Align model not loaded")
+    seed_everything(seed)
+
+    fragments = align_segments(transcript, audio_path)
+    segments = [{
+        "start": float(fragment["begin"]),
+        "end": float(fragment["end"]),
+        "text": " ".join(fragment["lines"])
+    } for fragment in fragments["fragments"]]
+    segments = align_model.align(segments, audio_path)
+    state = get_transcribe_state(segments)
+    print(state)
+
+    return [
+        state["transcript_with_start_time"], state["transcript_with_end_time"],
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
+        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
+        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
+        state
     ]
 
 
@@ -104,12 +199,12 @@ def get_output_audio(audio_tensors, codec_audio_sr):
 
 def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
         stop_repetition, sample_batch_size, kvcache, silence_tokens,
-        audio_path, word_info, transcript, smart_transcript,
+        audio_path, transcribe_state, transcript, smart_transcript,
         mode, prompt_end_time, edit_start_time, edit_end_time,
         split_text, selected_sentence, previous_audio_tensors):
     if voicecraft_model is None:
         raise gr.Error("VoiceCraft model not loaded")
-    if smart_transcript and (word_info is None):
+    if smart_transcript and (transcribe_state is None):
         raise gr.Error("Can't use smart transcript: whisper transcript not found")
 
     seed_everything(seed)
@@ -126,7 +221,6 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
     else:
         sentences = [transcript.replace("\n", " ")]
 
-    device = "cuda" if torch.cuda.is_available() else "cpu"
     info = torchaudio.info(audio_path)
     audio_dur = info.num_frames / info.sample_rate
 
@@ -141,7 +235,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
 
             if smart_transcript:                
                 target_transcript = ""
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
                         target_transcript += word["word"]
                     elif (word["start"] + word["end"]) / 2 < prompt_end_time:
@@ -169,13 +263,13 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
 
             if smart_transcript:
                 target_transcript = ""
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["start"] < edit_start_time:
                         target_transcript += word["word"]
                     else:
                         break
                 target_transcript += f" {sentence}"
-                for word in word_info:
+                for word in transcribe_state["words_info"]:
                     if word["end"] > edit_end_time:
                         target_transcript += word["word"]
             else:
@@ -296,25 +390,25 @@ demo_text = {
 all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
 
 demo_words = [
-    '0.0  But 0.12', '0.12  when 0.26', '0.26  I 0.44', '0.44  had 0.6', '0.6  approached 0.94', '0.94  so 1.42',
-    '1.42  near 1.78', '1.78  to 2.02', '2.02  them, 2.24', '2.52  the 2.58', '2.58  common 2.9', '2.9  object, 3.3', 
-    '3.72  which 3.78', '3.78  the 3.98', '3.98  sense 4.18', '4.18  deceives, 4.88', '5.06  lost 5.26', '5.26  not 5.74',
-    '5.74  by 6.08', '6.08  distance 6.36', '6.36  any 6.92', '6.92  of 7.12', '7.12  its 7.26', '7.26  marks. 7.54'
+    '0.029 But 0.149', '0.189 when 0.33', '0.43 I 0.49', '0.53 had 0.65', '0.711 approached 1.152', '1.352 so 1.593',
+    '1.693 near 1.933', '1.994 to 2.074', '2.134 them, 2.354', '2.535 the 2.655', '2.695 common 3.016', '3.196 object, 3.577',
+    '3.717 which 3.898', '3.958 the 4.058', '4.098 sense 4.359', '4.419 deceives, 4.92', '5.101 lost 5.481', '5.682 not 5.963',
+    '6.043 by 6.183', '6.223 distance 6.644', '6.905 any 7.065', '7.125 of 7.185', '7.245 its 7.346', '7.406 marks. 7.727'
 ]
 
-demo_word_info = [
-    {'word': ' But', 'start': 0.0, 'end': 0.12},  {'word': ' when', 'start': 0.12, 'end': 0.26},
-    {'word': ' I', 'start': 0.26, 'end': 0.44}, {'word': ' had', 'start': 0.44, 'end': 0.6},
-    {'word': ' approached', 'start': 0.6, 'end': 0.94}, {'word': ' so', 'start': 0.94, 'end': 1.42},
-    {'word': ' near', 'start': 1.42, 'end': 1.78}, {'word': ' to', 'start': 1.78, 'end': 2.02},
-    {'word': ' them,', 'start': 2.02, 'end': 2.24}, {'word': ' the', 'start': 2.52, 'end': 2.58},
-    {'word': ' common', 'start': 2.58, 'end': 2.9}, {'word': ' object,', 'start': 2.9, 'end': 3.3},
-    {'word': ' which', 'start': 3.72, 'end': 3.78}, {'word': ' the', 'start': 3.78, 'end': 3.98},
-    {'word': ' sense', 'start': 3.98, 'end': 4.18}, {'word': ' deceives,', 'start': 4.18, 'end': 4.88},
-    {'word': ' lost', 'start': 5.06, 'end': 5.26}, {'word': ' not', 'start': 5.26, 'end': 5.74},
-    {'word': ' by', 'start': 5.74, 'end': 6.08}, {'word': ' distance', 'start': 6.08, 'end': 6.36},
-    {'word': ' any', 'start': 6.36, 'end': 6.92}, {'word': ' of', 'start': 6.92, 'end': 7.12},
-    {'word': ' its', 'start': 7.12, 'end': 7.26}, {'word': ' marks.', 'start': 7.26, 'end': 7.54}
+demo_words_info = [
+    {'word': 'But', 'start': 0.029, 'end': 0.149, 'score': 0.834}, {'word': 'when', 'start': 0.189, 'end': 0.33, 'score': 0.879},
+    {'word': 'I', 'start': 0.43, 'end': 0.49, 'score': 0.984}, {'word': 'had', 'start': 0.53, 'end': 0.65, 'score': 0.998},
+    {'word': 'approached', 'start': 0.711, 'end': 1.152, 'score': 0.822}, {'word': 'so', 'start': 1.352, 'end': 1.593, 'score': 0.822},
+    {'word': 'near', 'start': 1.693, 'end': 1.933, 'score': 0.752}, {'word': 'to', 'start': 1.994, 'end': 2.074, 'score': 0.924},
+    {'word': 'them,', 'start': 2.134, 'end': 2.354, 'score': 0.914}, {'word': 'the', 'start': 2.535, 'end': 2.655, 'score': 0.818},
+    {'word': 'common', 'start': 2.695, 'end': 3.016, 'score': 0.971}, {'word': 'object,', 'start': 3.196, 'end': 3.577, 'score': 0.823},
+    {'word': 'which', 'start': 3.717, 'end': 3.898, 'score': 0.701}, {'word': 'the', 'start': 3.958, 'end': 4.058, 'score': 0.798},
+    {'word': 'sense', 'start': 4.098, 'end': 4.359, 'score': 0.797}, {'word': 'deceives,', 'start': 4.419, 'end': 4.92, 'score': 0.802},
+    {'word': 'lost', 'start': 5.101, 'end': 5.481, 'score': 0.71}, {'word': 'not', 'start': 5.682, 'end': 5.963, 'score': 0.781},
+    {'word': 'by', 'start': 6.043, 'end': 6.183, 'score': 0.834}, {'word': 'distance', 'start': 6.223, 'end': 6.644, 'score': 0.899},
+    {'word': 'any', 'start': 6.905, 'end': 7.065, 'score': 0.893}, {'word': 'of', 'start': 7.125, 'end': 7.185, 'score': 0.772},
+    {'word': 'its', 'start': 7.245, 'end': 7.346, 'score': 0.778}, {'word': 'marks.', 'start': 7.406, 'end': 7.727, 'score': 0.955}
 ]
 
 
@@ -342,21 +436,24 @@ with gr.Blocks() as app:
             with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
                     voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
+                                                    choices=[None, "base.en", "small.en", "medium.en", "large"])
+                    align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
 
     with gr.Row():
         with gr.Column(scale=2):
             input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
             with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
-                                                 info="Use whisper model to get the transcript. Fix it if necessary.")
+                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
+                                                 info="Use whisper model to get the transcript. Fix and align it if necessary.")
                 with gr.Accordion("Word start time", open=False):
                     transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
                 with gr.Accordion("Word end time", open=False):
                     transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
 
                 transcribe_btn = gr.Button(value="Transcribe")
+                align_btn = gr.Button(value="Align")
             
         with gr.Column(scale=3):
             with gr.Group():
@@ -375,15 +472,15 @@ with gr.Blocks() as app:
                 
                 with gr.Group() as tts_mode_controls:
                     prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
-                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
+                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
 
                 with gr.Group(visible=False) as edit_mode_controls:
                     with gr.Row():
                         edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
                         edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
                     with gr.Row():
-                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.01, value=0.35)
-                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.01, value=3.75)
+                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
+                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
 
                 run_btn = gr.Button(value="Run")
 
@@ -418,7 +515,7 @@ with gr.Blocks() as app:
 
     
     audio_tensors = gr.State()
-    word_info = gr.State(value=demo_word_info)
+    transcribe_state = gr.State(value={"words_info": demo_words_info})
 
     
     mode.change(fn=update_demo,
@@ -432,7 +529,7 @@ with gr.Blocks() as app:
                             outputs=[transcript, edit_from_word, edit_to_word])
     
     load_models_btn.click(fn=load_models,
-                          inputs=[whisper_model_choice, voicecraft_model_choice],
+                          inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
                           outputs=[models_selector])
     
     input_audio.upload(fn=update_input_audio,
@@ -441,7 +538,11 @@ with gr.Blocks() as app:
     transcribe_btn.click(fn=transcribe,
                          inputs=[seed, input_audio],
                          outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
-                                  prompt_to_word, edit_from_word, edit_to_word, word_info])
+                                  prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+    align_btn.click(fn=align,
+                    inputs=[seed, original_transcript, input_audio],
+                    outputs=[transcript_with_start_time, transcript_with_end_time,
+                             prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
 
     mode.change(fn=change_mode,
                 inputs=[mode],
@@ -454,7 +555,7 @@ with gr.Blocks() as app:
                       top_k, top_p, temperature,
                       stop_repetition, sample_batch_size,
                       kvcache, silence_tokens,
-                      input_audio, word_info, transcript, smart_transcript,
+                      input_audio, transcribe_state, transcript, smart_transcript,
                       mode, prompt_end_time, edit_start_time, edit_end_time,
                       split_text, sentence_selector, audio_tensors
                   ],
@@ -470,7 +571,7 @@ with gr.Blocks() as app:
                         top_k, top_p, temperature,
                         stop_repetition, sample_batch_size,
                         kvcache, silence_tokens,
-                        input_audio, word_info, transcript, smart_transcript,
+                        input_audio, transcribe_state, transcript, smart_transcript,
                         gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
diff --git a/gradio_requirements.txt b/gradio_requirements.txt
index 949acc7..967b3d7 100644
--- a/gradio_requirements.txt
+++ b/gradio_requirements.txt
@@ -1,3 +1,5 @@
 gradio==3.50.2
 nltk>=3.8.1
-openai-whisper>=20231117
\ No newline at end of file
+openai-whisper>=20231117
+aeneas>=1.7.3.0
+whisperx>=3.1.1
\ No newline at end of file

From 40887bd0e34e3e50db6ca554519b00ce5906ab22 Mon Sep 17 00:00:00 2001
From: pgosar <pgosarsteam@gmail.com>
Date: Thu, 4 Apr 2024 19:26:28 -0500
Subject: [PATCH 06/40] add Colab to readme

---
 README.md | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index d15654d..59cc368 100644
--- a/README.md
+++ b/README.md
@@ -17,21 +17,31 @@ To clone or edit an unseen voice, VoiceCraft needs only a few seconds of referen
 - [x] Training guidance
 - [x] RealEdit dataset and training manifest
 - [x] Model weights (both 330M and 830M, the former seems to be just as good)
-- [ ] Write colab notebooks for better hands-on experience
+- [x] Write colab notebooks for better hands-on experience
 - [ ] HuggingFace Spaces demo
 - [ ] Better guidance on training/finetuning
 
 ## How to run TTS inference
-There are two ways:
-1. with docker. see [quickstart](#quickstart)
-2. without docker. see [envrionment setup](#environment-setup)
+There are three ways:
+
+1. with Google Colab. see [quickstart colab](#quickstart-colab)
+2. with docker. see [quickstart docker](#quickstart-docker)
+3. without docker. see [environment setup](#environment-setup)
 
 When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).
 
 If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
 
-## QuickStart
-:star: To try out TTS inference with VoiceCraft, the best way is using docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen.
+## QuickStart Colab
+
+:star: To try out speech editing or TTS Inference with VoiceCraft, the simplest way is using Google Colab.
+Instructions to run are on the Colab itself.
+
+1. To try [Speech Editing](https://colab.research.google.com/drive/1FV7EC36dl8UioePY1xXijXTMl7X47kR_?usp=sharing)
+2. To try [TTS Inference](https://colab.research.google.com/drive/1lch_6it5-JpXgAQlUTRRI2z2_rk5K67Z?usp=sharing)
+
+## QuickStart Docker
+:star: To try out TTS inference with VoiceCraft, you can also use docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen.
 
 Tested on Linux and Windows and should work with any host with docker installed.
 ```bash

From 97b1f519478f39702e910d3fde86c8a88bac4d8e Mon Sep 17 00:00:00 2001
From: Puyuan Peng <47729801+jasonppy@users.noreply.github.com>
Date: Thu, 4 Apr 2024 20:26:28 -0500
Subject: [PATCH 07/40] install whisperx deps

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 2a5e247..5f587db 100644
--- a/README.md
+++ b/README.md
@@ -86,6 +86,10 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 
 # to run ipynb
 conda install -n voicecraft ipykernel --no-deps --force-reinstall
+
+# below is only needed if you want to run gradio_app.py
+sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
+sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.

From 0d19fa5d0334eb527de642d15b2ae21fe6ba4bfc Mon Sep 17 00:00:00 2001
From: Puyuan Peng <47729801+jasonppy@users.noreply.github.com>
Date: Thu, 4 Apr 2024 20:31:07 -0500
Subject: [PATCH 08/40] fix whisperx loading issue, update generation
 instruction

---
 gradio_app.py | 41 ++++++++++++++++++++---------------------
 1 file changed, 20 insertions(+), 21 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 4321a11..15f77e1 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,3 +1,6 @@
+import os
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -6,7 +9,6 @@ from data.tokenizer import (
     TextTokenizer,
 )
 from models import voicecraft
-import os
 import io
 import numpy as np
 import random
@@ -64,7 +66,7 @@ class WhisperModel:
 class WhisperxModel:
     def __init__(self, model_name, align_model: WhisperxAlignModel):
         from whisperx import load_model
-        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True})
+        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
         self.align_model = align_model
 
     def transcribe(self, audio_path):
@@ -75,9 +77,6 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -443,7 +442,7 @@ with gr.Blocks() as app:
 
     with gr.Row():
         with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath")
+            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
             with gr.Group():
                 original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
                                                  info="Use whisper model to get the transcript. Fix and align it if necessary.")
@@ -496,22 +495,22 @@ with gr.Blocks() as app:
                 rerun_btn = gr.Button(value="Rerun")
 
     with gr.Row():
-        with gr.Accordion("VoiceCraft config", open=False):
-            seed = gr.Number(label="seed", value=-1, precision=0)
-            left_margin = gr.Number(label="left_margin", value=0.08)
-            right_margin = gr.Number(label="right_margin", value=0.08)
-            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
-            codec_sr = gr.Number(label="codec_sr", value=50)
-            top_k = gr.Number(label="top_k", value=0)
-            top_p = gr.Number(label="top_p", value=0.8)
-            temperature = gr.Number(label="temperature", value=1)
-            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
-                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
-            sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
-                                          info="generate this many samples and choose the shortest one")
+        with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
+            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
+                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
+            sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
+                                          info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
+            seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
             kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
                                 info="set to 0 to use less VRAM, but with slower inference")
-            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
+            left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
+            right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
+            top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
+            temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
+            top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
+            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
+            codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
+            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
     
     audio_tensors = gr.State()
@@ -592,4 +591,4 @@ with gr.Blocks() as app:
 
 
 if __name__ == "__main__":
-    app.launch()
\ No newline at end of file
+    app.launch()

From 94e9f9bd4233b6510f4ff45c2cd291a9476957ce Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 04:40:57 +0300
Subject: [PATCH 09/40] README update, gradio_app.ipynb update, debug print
 removed

---
 README.md        |  5 +++
 gradio_app.ipynb | 88 +++++++++---------------------------------------
 gradio_app.py    |  4 ---
 3 files changed, 21 insertions(+), 76 deletions(-)

diff --git a/README.md b/README.md
index 2a5e247..41f1654 100644
--- a/README.md
+++ b/README.md
@@ -96,6 +96,11 @@ Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) an
 ## Gradio
 After environment setup install additional dependencies:
 ```bash
+apt-get install -y espeak espeak-data libespeak1 libespeak-dev
+apt-get install -y festival*
+apt-get install -y build-essential
+apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools
+apt-get install -y libxml2-dev libxslt-dev zlib1g-dev
 pip install -r gradio_requirements.txt
 ```
 
diff --git a/gradio_app.ipynb b/gradio_app.ipynb
index 0d3f946..7b13660 100644
--- a/gradio_app.ipynb
+++ b/gradio_app.ipynb
@@ -8,84 +8,28 @@
     "### Only do the below if you are using docker"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "270aa2cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install OS deps\n",
-    "!sudo apt-get update && sudo apt-get install -y \\\n",
-    "    git-core \\\n",
-    "    ffmpeg \\\n",
-    "    espeak-ng"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8ba5f452",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update and setup Conda voicecraft environment\n",
-    "!conda update -y -n base -c conda-forge conda\n",
-    "!conda create -y -n voicecraft python=3.9.16 && \\\n",
-    "    conda init bash"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ef2935c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install conda and pip stuff in the activated conda above context\n",
-    "!echo -e \"Grab a cup a coffee and a slice of pizza...\\n\\n\"\n",
-    "\n",
-    "# make sure $HOME and $USER are setup so this will source the conda environment\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    conda install -y -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068 && \\\n",
-    "    pip install torch==2.0.1 && \\\n",
-    "    pip install tensorboard==2.16.2 && \\\n",
-    "    pip install phonemizer==3.2.1 && \\\n",
-    "    pip install torchaudio==2.0.2 && \\\n",
-    "    pip install datasets==2.16.0 && \\\n",
-    "    pip install torchmetrics==0.11.1\n",
-    "\n",
-    "# do this one last otherwise you'll get an error about torch compiler missing due to xformer mismatch\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fca57eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# okay setup the conda environment such that jupyter notebook can find the kernel\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    conda install -y -n voicecraft ipykernel --update-deps --force-reinstall\n",
-    "\n",
-    "# installs the Jupyter kernel into /home/myusername/.local/share/jupyter/kernels/voicecraft\n",
-    "!source ~/.bashrc && \\\n",
-    "    conda activate voicecraft && \\\n",
-    "    python3 -m ipykernel install --user --name=voicecraft"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "961faa43",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "!source ~/.bashrc && \\\n",
+    "    apt-get update && \\\n",
+    "    apt-get install -y espeak espeak-data libespeak1 libespeak-dev && \\\n",
+    "    apt-get install -y festival* && \\\n",
+    "    apt-get install -y build-essential && \\\n",
+    "    apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools && \\\n",
+    "    apt-get install -y libxml2-dev libxslt-dev zlib1g-dev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "598d75cf",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!source ~/.bashrc && \\\n",
     "    conda activate voicecraft && \\\n",
diff --git a/gradio_app.py b/gradio_app.py
index 4321a11..5e349fe 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -75,9 +75,6 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -178,7 +175,6 @@ def align(seed, transcript, audio_path):
     } for fragment in fragments["fragments"]]
     segments = align_model.align(segments, audio_path)
     state = get_transcribe_state(segments)
-    print(state)
 
     return [
         state["transcript_with_start_time"], state["transcript_with_end_time"],

From 6f71fa65fb8d6efaf54cde474009e9d78bebfe94 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 07:00:07 +0300
Subject: [PATCH 10/40] smart transcript fix

---
 gradio_app.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 310e6f8..d14433f 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -235,10 +235,10 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                     elif (word["start"] + word["end"]) / 2 < prompt_end_time:
                         # include part of the word it it's big, but adjust prompt_end_time
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                         prompt_end_time = word["end"]
                         break
                     else:
@@ -263,13 +263,13 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["start"] < edit_start_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                     else:
                         break
                 target_transcript += f" {sentence}"
                 for word in transcribe_state["words_info"]:
                     if word["end"] > edit_end_time:
-                        target_transcript += word["word"]
+                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
             else:
                 target_transcript = sentence
 

From 21b69ad676235c1ee6f0004cc17349896ef5940e Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Fri, 5 Apr 2024 07:42:11 +0300
Subject: [PATCH 11/40] configurable tmp path

---
 README.md     | 1 +
 gradio_app.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0626bbd..1f7ca11 100644
--- a/README.md
+++ b/README.md
@@ -111,6 +111,7 @@ pip install -r gradio_requirements.txt
 Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
 ```bash
 python gradio_app.py
+TMP_PATH=/tmp python gradio_app.py # if you want to change tmp folder path
 ```
 It is ready to use on [default url](http://127.0.0.1:7860).
 
diff --git a/gradio_app.py b/gradio_app.py
index d14433f..32b3e72 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -15,7 +15,7 @@ import random
 import uuid
 
 
-TMP_PATH = "./demo/temp"
+TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None
 

From 1141775763e7e2399bc57ed2613a926b37aafa53 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:38:22 +0800
Subject: [PATCH 12/40] Update README.md

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 1f7ca11..c0dbf31 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,8 @@
+# VoiceCraft Gradio Colab
+Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
+
+COLAB LINK : HOLD ON I AM UPDATING
+
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
 

From b9ffda940503cf07c1d5f370eef1f3611f03a5d5 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:40:48 +0800
Subject: [PATCH 13/40] Adding share=True

---
 gradio_app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gradio_app.py b/gradio_app.py
index 32b3e72..4b816a2 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -590,4 +590,4 @@ with gr.Blocks() as app:
 
 
 if __name__ == "__main__":
-    app.launch()
+    app.launch(share=True)

From 5c92b2864f5492399961ed99a36eab3a891d0feb Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:49:00 +0800
Subject: [PATCH 14/40] Update gradio_app.py

---
 gradio_app.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gradio_app.py b/gradio_app.py
index 4b816a2..11203fe 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -14,6 +14,8 @@ import numpy as np
 import random
 import uuid
 
+os.chdir("/content/VoiceCraft-gradio-colab")
+os.environ['USER'] = 'aaa'
 
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"

From fcdd1d30af11da333d8cf57e7b8cda78b159578f Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:53:38 +0800
Subject: [PATCH 15/40] Update gradio_app.py

---
 gradio_app.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 11203fe..9183e66 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,8 @@
 import os
-# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-# os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
+ os.chdir("/content/VoiceCraft-gradio-colab")
+ os.environ['USER'] = 'aaa'
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -14,9 +16,6 @@ import numpy as np
 import random
 import uuid
 
-os.chdir("/content/VoiceCraft-gradio-colab")
-os.environ['USER'] = 'aaa'
-
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None

From 0f5451bf4ef2a33766a45d6436767e95b68c62ad Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:54:24 +0800
Subject: [PATCH 16/40] Update gradio_app.py

---
 gradio_app.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 9183e66..d0b442a 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,4 @@
 import os
- os.chdir("/content/VoiceCraft-gradio-colab")
- os.environ['USER'] = 'aaa'
  os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
  os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
@@ -16,6 +14,9 @@ import numpy as np
 import random
 import uuid
 
+os.chdir("/content/VoiceCraft-gradio-colab")
+os.environ['USER'] = 'aaa'
+
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None

From 38e8fa0776fa7ce277813947d5870cdff03178a7 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 17:54:59 +0800
Subject: [PATCH 17/40] Update gradio_app.py

---
 gradio_app.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index d0b442a..72df9e1 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -1,6 +1,4 @@
 import os
- os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
- os.environ["CUDA_VISIBLE_DEVICES"] = "0" # for local use
 import gradio as gr
 import torch
 import torchaudio
@@ -14,6 +12,8 @@ import numpy as np
 import random
 import uuid
 
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 os.chdir("/content/VoiceCraft-gradio-colab")
 os.environ['USER'] = 'aaa'
 

From 47937fb0dcb4ab04caeb572899fcc3a3924cc460 Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 19:15:10 +0800
Subject: [PATCH 18/40] April 5 Colab

---
 voicecraft.ipynb | 122 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 voicecraft.ipynb

diff --git a/voicecraft.ipynb b/voicecraft.ipynb
new file mode 100644
index 0000000..f8b9c85
--- /dev/null
+++ b/voicecraft.ipynb
@@ -0,0 +1,122 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyPEhMt0mIcJv2BbaCwogF07",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y87ixxsUVIhM"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/Sewlell/VoiceCraft-gradio-colab"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install tensorboard\n",
+        "!pip install phonemizer\n",
+        "!pip install datasets\n",
+        "!pip install torchmetrics\n",
+        "\n",
+        "!apt-get install -y espeak espeak-data libespeak1 libespeak-dev\n",
+        "!apt-get install -y festival*\n",
+        "!apt-get install -y build-essential\n",
+        "!apt-get install -y flac libasound2-dev libsndfile1-dev vorbis-tools\n",
+        "!apt-get install -y libxml2-dev libxslt-dev zlib1g-dev\n",
+        "\n",
+        "!pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft\n",
+        "\n",
+        "!pip install -r \"/content/VoiceCraft-gradio-colab/gradio_requirements.txt\""
+      ],
+      "metadata": {
+        "id": "-w3USR91XdxY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Let it restarted, it won't let your entire installation be gone."
+      ],
+      "metadata": {
+        "id": "jNuzjrtmv2n1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Note before launching the `gradio_app.py`\n",
+        "\n",
+        "***You will get JSON warning if you move anything beside `sample_batch_size`, `stop_repetition` and `seed`.*** Which for most advanced setting, `kvache` and `temperature` unable to set in different value.\n",
+        "\n",
+        "You will get fp16 compatibility issue if you set `whisper backend` to `whisperX`, for whatever reason, setting `forced alignment model` to `whisperX` doesn't do anything.\n",
+        "\n",
+        "You will download a .file File when you download the Output Audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
+        "\n",
+        "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
+        "\n",
+        "# **To those who want to voice cloning**\n",
+        "![Screenshot (438).png](data:image/png;base64,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)\n",
+        "\n",
+        "Don't make your input audio too long like in the screenshot, 20s-30s is fine. Or else it will raise up JSON issue. This one is due to how VoiceCraft worked so probably unfixable. It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short."
+      ],
+      "metadata": {
+        "id": "qQ-And_w2vJV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "My tip on voice cloning is just \"get a good dataset\" that contain plausible amount of variety of tones. I guess that's it, you could always try experimenting with other voice that are hard to be clone.\n",
+        "\n",
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-10s on 6s-8s `prompt end time`.\n",
+        "\n",
+        "I haven't test the Edit mode yet as those are not my focus, but you can try it."
+      ],
+      "metadata": {
+        "id": "nnu2cY4t8P6X"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python \"/content/VoiceCraft-gradio-colab/gradio_app.py\""
+      ],
+      "metadata": {
+        "id": "NDt4r4DiXAwG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 8b21333bef69a42f0db7ea0f735aa9243012256d Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Fri, 5 Apr 2024 19:21:47 +0800
Subject: [PATCH 19/40] Release access for the Colab

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index c0dbf31..5d3a3f5 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,8 @@
 # VoiceCraft Gradio Colab
-Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
 
-COLAB LINK : HOLD ON I AM UPDATING
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb)
+
+Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
 
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)

From 0cbe08e84757dae38cbd437c87a14030ab77b6f6 Mon Sep 17 00:00:00 2001
From: Ganesh Krishnan <github@alphasigma.com.au>
Date: Fri, 5 Apr 2024 12:12:01 -0400
Subject: [PATCH 20/40] Update inference_speech_editing.ipynb

enable one alignment
---
 inference_speech_editing.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference_speech_editing.ipynb b/inference_speech_editing.ipynb
index 95efa12..59b7469 100644
--- a/inference_speech_editing.ipynb
+++ b/inference_speech_editing.ipynb
@@ -99,7 +99,7 @@
     "# run MFA to get the alignment\n",
     "align_temp = f\"{temp_folder}/mfa_alignments\"\n",
     "os.makedirs(align_temp, exist_ok=True)\n",
-    "# os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp}\")\n",
+    "os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp}\")\n",
     "# if it fail, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue\n",
     "# os.system(f\"mfa align -j 1 --output_format csv {temp_folder} english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000\")\n",
     "audio_fn = f\"{temp_folder}/{filename}.wav\"\n",

From e2e598d900a5ff4025e3d4bb58f63ea04a23a436 Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Fri, 5 Apr 2024 12:52:28 -0700
Subject: [PATCH 21/40] add RealEdit.txt; fixed masking warning to be
 compatible with torch 2.1.0

---
 RealEdit.txt         | 311 +++++++++++++++++++++++++++++++++++++++++++
 models/voicecraft.py |   4 +
 2 files changed, 315 insertions(+)
 create mode 100644 RealEdit.txt

diff --git a/RealEdit.txt b/RealEdit.txt
new file mode 100644
index 0000000..9dc21ec
--- /dev/null
+++ b/RealEdit.txt
@@ -0,0 +1,311 @@
+wav_fn	orig_transcript	new_transcript	orig_masked_span	new_masked_span	type
+YOU1000000102_S0000137.wav	if i had never dropped out. i would have never dropped in on that calligraphy class and personal computers might not have the wonderful typography that they do.	if i had never dropped out. i would have never stopped by that calligraphy class and personal computers might not have the wonderful typography that they do.	10,12	10,11	substitution
+YOU1000000124_S0000174.wav	so people in symbolic era. i mean we all agree that symbols come in and symbols come out, okay.	so people in symbolic era. i mean we all agree that signals go in and symbols come out, okay.	11,12	11,12	substitution
+YOU1000000006_S0000016.wav	and then we can actually go through and generate a lead. so we've got this first call to action here and this is sending them to a landing page.	and then we can actually go through and generate a lead. so we've got this first action here and this is sending them to a landing page.	16,17	15,16	deletion
+YOU1000000149_S0000172.wav	the dry shampoo honestly like weirdly styles it enough to where i'm like satisfied with it but i do like to add a little bit of hair spray.	the dry shampoo honestly like weirdly styles it enough to where i'm like satisfied with it but sometimes it needs a little extra i do like to add a little bit of hair spray.	16,17	17,22	insertion
+YOU1000000102_S0000031.wav	steve also cofounded pixar animation studios. which has revolutionized the film industry in it short history with brilliant use of technology.	steve also cofounded pixar animation studios. which has revolutionized the film industry in it short history with films like toy story that showcase brilliant use of technology.	16,17	17,22	insertion
+YOU1000000148_S0000041.wav	so i was actually happy to wear such a heavy dress and to be able to wear proper wool, you know mountain underwear ha ha ha.	so i was actually happy to wear such a rugged coat and boots and to be able to wear proper wool, you know mountain underwear ha ha ha.	9,10	9,12	substitution
+YOU1000000153_S0000000.wav	some times i really feel that the world around us continues to be more hectic and more complicated and so many of us are truly craving to find simplicity.	some times i really feel that the world around us continues to be more hectic, more impersonal, and more uncaring and so many of us are truly craving to find simplicity.	14,17	14,19	substitution
+YOU1000000163_S0000051.wav	and finally, pressing on the crown opens up the app menu so this is where you can access your third party apps and system settings.	and finally, pressing on the crown opens up the app menu where you can access your third party apps and system settings.	11,13	10,11	deletion
+YOU1000000115_S0000057.wav	in the future when the borrower repays the loan plus interest, the asset and the liability disappear and the transaction is settled.	in the future when the borrower repays the lender the loan plus interest, the asset and the liability disappear and the transaction is settled.	7,8	8,9	insertion
+YOU1000000103_S0000113.wav	with an election firmly behind us, voters are taking a new measure of joe biden and what they believe he should deliver as president.	with an election firmly behind us, voters are taking a new measure of joe biden and the rest of his administration and reconsidering what they believe he should deliver as president.	15,16	16,22	insertion
+YOU1000000019_S0000015.wav	causing a lock up. although this mostly happens when i'm spamming tps in the last layer. so i've had to learn to control my speed to help with overshooting.	causing a lock up. although this mostly happens when i'm tapping the pedal faster than i should be so i've had to learn to control my speed to help with overshooting.	10,15	10,17	substitution
+YOU1000000045_S0000205.wav	and said, you know, we need to start a process ah in order to figure out how we can protect the kurds who have been our allies.	and said, you know, we need to start a process ah in order to figure out how we can provide aid to all the groups that have helped us including the kurds who have been our allies.	19	19,29	substitution
+YOU1000000155_S0000027.wav	in a case like this, i probably wouldn't spend any more time looking at the deal if i was only interested in the cash flow.	in a case like this, i probably wouldn't spend any more time looking at all the details and the fine print if i was only interested in the cash flow.	14,15	14,20	substitution
+YOU1000000118_S0000018.wav	the reason will often comeback to how you created the machine learning dataset. so give yourself time to absorb the lessons.	the reason will often boil down to the quality of the machine learning dataset. so give yourself time to absorb the lessons.	4,8	4,9	substitution
+YOU1000000115_S0000104.wav	the total amount of credit in the united states is about fifty trillion dollars and the total amount of money is only about three trillion dollars.	the total amount of credit is about fifty trillion dollars and the total amount of money is only about three trillion dollars.	5,8	4,5	deletion
+YOU1000000045_S0000187.wav	so, there's a huge difference. i mean, people can gather, you know, information in, you know, all kinds of different ways.	so, there's a huge difference. i mean, people can earn their living and provide for their family in you know, all kinds of different ways.	9,13	9,17	substitution
+YOU1000000101_S0000060.wav	they knew that governments don't control things. a government can't control the economy without controlling people.	they knew that governments don't control money directly. a government can't control the economy without controlling people.	6	6,7	substitution
+YOU1000000106_S0000178.wav	okay so my little cousin julia wants to know what did you want to do with your life at age five?	okay so my little cousin julia has kind of a weird question for you. she wants to know what did you want to do with your life at age five?	5,6	6,14	insertion
+YOU1000000165_S0000039.wav	and one to watch over the course of the six seasons of girls. after rising to prominence on the series, he promptly began picking up increasingly significant roles.	and one to watch over the course of the six seasons of girls. after his dating scandal, he has lost some significant roles.	14,25	14,20	substitution
+YOU1000000181_S0000024.wav	around a large pot of vinegar crowd three men, these aren't any ordinary men in fact they're the three founders of the great asian philosophies.	around a large pot of vinegar crowd three men. they're cooking for the three founders of the great asian philosophies.	8,16	8,11	substitution
+YOU1000000116_S0000006.wav	which was no part of his intention and this term invisible hand is famous led by the invisible hand to promote an end.	which was no part of his intention and this term is famous led by the invisible hand to promote an end.	10,11	9,10	deletion
+YOU1000000015_S0000044.wav	or press control c or command c to copy that link. you don't wanna come back to parallel's toolbox and then all we need to do is hit paste.	or press control c or command c to copy that paragraph, then go back to the original document and then all we need to do is hit paste.	10,18	10,17	substitution
+YOU1000000167_S0000004.wav	you could tell christopher robin had something important to say from the way he clasped his knees tightly and wriggled his toes.	you could tell christopher was really eager to get out of his seat from the way he clasped his knees tightly and wriggled his toes.	4,9	4,12	substitution
+YOU1000000119_S0000017.wav	at the end of this course, you will be able to explain the three fundamental characteristic that define the blockchain using bitcoin blockchain.	at the end of this course, you will be able to explain to your friends what blockchain is, and why they should be using bitcoin blockchain.	12,19	12,22	substitution
+YOU1000000170_S0000103.wav	it it felt like, at the time like, an incredible milestone. we we needed to post it to use net.	it it felt like, we needed to post it to use net.	4,11	3,4	deletion
+YOU1000000167_S0000107.wav	he hadn't expected london to have quite so many legs.	he hadn't expected the new furniture to have quite so many legs.	3	3,5	substitution
+YOU1000000183_S0000073.wav	and then when you actually do sit down to learn about some universities look at the programs that are really relevant to you.	and then when you actually do make searches you have to make sure that you find recommendations that are really relevant to you.	6,16	6,16	substitution
+YOU1000000120_S0000023.wav	coconut milk some peeled tomatoes i wanna zip this up now.	coconut milk some tomatoes i wanna zip this up now.	3	2,3	deletion
+YOU1000000187_S0000089.wav	there is beautiful things in life so when you're suffering just you know its part of the package you know you look at it we're born.	there is beautiful things in life so you should always remember when you're suffering just you know its part of the package you know you look at it we're born.	6,7	7,10	insertion
+YOU1000000122_S0000033.wav	we should not be finding objects all of a sudden that are spitting distance away from us that we've been missing all this time.	we should not be finding objects all of a sudden spitting distance away from us that we've been missing all this time.	10,11	9,10	deletion
+YOU1000000045_S0000141.wav	because of how you know toxic politics in america has become.	because of how toxic politics in america has become.	3,4	2,3	deletion
+YOU1000000185_S0000003.wav	and boys did you pick a great week to tune in. over the past few months, we've been bringing together experts in a number of critical fields.	and boys did you pick a great week to tune in. We've got an amazing episode lined up for you today. over the past few months, we've been bringing together experts in a number of critical fields.	10,11	11,20	insertion
+YOU1000000127_S0000064.wav	economic development remains one of the most effective ways to increase the capacity to adapt to climate change.	economic development remains one of the most promising options that we have left on the table to increase the capacity to adapt to climate change.	7,8	7,15	substitution
+YOU1000000105_S0000131.wav	and all deservedly so, but we have something for you. in fact, guillermo, bring this in.	and all deservedly so, but we have to show you something that just arrived this morning. in fact, guillermo, bring this in.	7,9	7,15	substitution
+YOU1000000122_S0000012.wav	jackie has spent fifteen years searching our solar neighborhood for new neighbors.	jackie has spent the last three decades searching our solar neighborhood for new neighbors.	3,4	3,6	substitution
+YOU1000000138_S0000114.wav	he, interestingly so, absolutely no reason to feel any type of remorse, and although he's quite pleasant to me he's pleasant to us, he's a very very dangerous individual.	he, interestingly so, absolutely no reason to feel any type of remorse, and although he might seem like a nice person, be careful, because he's a very very dangerous individual.	14,22	14,23	substitution
+YOU1000000027_S0000026.wav	because i'm gonna be doing more live chats here and there and i'm just trying to post more videos on facebook and be more active there.	because i'm gonna be doing more live chats here and there and i'm just trying to post more overall content and more videos on facebook and be more active there.	17,18	18,21	insertion
+YOU1000000123_S0000009.wav	i wrote the title of the course many years ago, ah, when i created this course.	i wrote the title when i created this course.	4,10	3,4	deletion
+YOU1000000001_S0000037.wav	i'm gonna try to keep my vehicle on the road without going on the lawn over there like i did on the last ah pull in.	i'm gonna try to avoid any unintentional mishaps like last time and keep my vehicle on the road without going on the lawn over there like i did on the last ah pull in.	3,4	4,11	insertion
+YOU1000000019_S0000011.wav	but qiyi then went on to post some more photos and explained some more of the subtle differences between the two cubes.	but qiyi then went on to post some more really high quality pictures and videos and explained some more of the subtle differences between the two cubes.	9	9,14	substitution
+YOU1000000126_S0000185.wav	when we started coursera, we had no idea that over the next several years, it will blossom to such a large movement.	when we started coursera, we were absolutely confident that with enough hard work it will blossom to such a large movement.	5,13	5,12	substitution
+YOU1000000171_S0000052.wav	currently fifteen to twenty countries and ah another.	currently fifteen to seventeen states and ah another.	3,4	3,4	substitution
+YOU1000000184_S0000015.wav	we we both had a fair amount of experience in real estate and charlie made his early money in real estate um.	we we both had a fair amount of experience in investing but warren actually made all his early money in real estate um.	10,14	10,15	substitution
+YOU1000000111_S0000109.wav	and you gotta make sure you in nobody's area to allow them to knock down a three and you get contact.	and you gotta make sure to allow them to knock down a three and you get contact.	5,8	4,5	deletion
+YOU1000000005_S0000035.wav	and then the campaign content i think this one is really key to use as well.	and then the campaign content is super detailed so this one is really key to use as well.	5,6	5,8	substitution
+YOU1000000004_S0000033.wav	and you are gonna be looking to copy and pasting some things here now this gives us clear instruction.	and you are gonna be looking to copy things here now this gives us clear instruction.	8,10	7,8	deletion
+YOU1000000163_S0000001.wav	and what was really unique about this smartwatch was that it actually came with not one but two displays.	and what was really unique about this smartwatch was that since it can flip open it actually came with not one but two displays.	9,10	10,14	insertion
+YOU1000000043_S0000180.wav	ah so let's ignore that and go back to the software. so that is the last thing.	ah so let's ignore that and go back to the beginning. so that is the last thing.	10	10	substitution
+YOU1000000101_S0000273.wav	to keep the goldwater crusade on the air, send one, ten, fifty dollars.	to keep the goldwater, send one, ten, fifty dollars.	3,6	2,3	deletion
+YOU1000000108_S0000265.wav	reflect it's just a place that i got to go.	reflect it's just a place that we all got to go.	6	6,7	substitution
+YOU1000000128_S0000041.wav	that's a question nobody can answer because the future depends on decisions that have not yet been taken.	that's a question nobody can answer with confidence since the future depends on decisions that have not yet been taken.	6	6,8	substitution
+YOU1000000191_S0000014.wav	ten standing up and over hundred and ten are chilling out, bellies full of grass.	ten standing up and over hundred and ten are just laying around chilling out, bellies full of grass.	8,9	9,11	insertion
+YOU1000000174_S0000066.wav	we have done a lot of work around vaccine planning but also realistically it's not gonna be available to community members right away.	we have done a lot of work but also realistically it's not gonna be available to community members right away.	7,9	6,7	deletion
+YOU1000000133_S0000039.wav	when the c e o of blockbuster heard that, he promptly had a kitchen sink delivered to the netflix office, a fairly creative way of declaring war.	when the c e o of blockbuster heard that, he promptly had five hundred pounds of glitter divided into five thousand manilla envelopes delivered to the netflix office, a fairly creative way of declaring war.	12,14	12,22	substitution
+YOU1000000118_S0000004.wav	we end with a discussion of two link, of how to do machine learning at scale using python notebooks and server less data processing components.	we end with a discussion of two link, of how to do machine learning at scale using python notebooks and energy efficient data processing components.	20,21	20,21	substitution
+YOU1000000007_S0000039.wav	we just come in here and create a custom app okay now we're just gonna give this a name we're gonna say this is the seller leads campaign.	we just come in here and give this a name we're gonna say this is the seller leads campaign.	6,14	5,6	deletion
+YOU1000000113_S0000034.wav	that's a bomb and that's a good sign from him. he got fully extended on it. knew it as soon as that ball left.	that's a bomb and that's a good sign from him. he clearly signalled and made the play happen as soon as that ball left.	11,17	11,17	substitution
+YOU1000000169_S0000188.wav	they're being trained to detect the early warning signals for severe allergic reactions, epileptic fits and narcolepsy.	they're being trained to detect the early warning signals for epileptic fits and narcolepsy.	10,12	9,10	deletion
+YOU1000000155_S0000070.wav	an investor flipping houses at this level might require far less than seventy percent maybe a fifty percent or even lower.	an investor flipping houses at this level might require lower margins than seventy percent maybe a fifty percent or even lower.	9,10	9,10	substitution
+YOU1000000119_S0000043.wav	what is a blockchain? blockchain is about enabling peer to peer transaction in a decentralized network.	what is a blockchain? blockchain is about high risk high reward investments in a decentralized network.	7,11	7,11	substitution
+YOU1000000139_S0000064.wav	but then queen actually changes its direction attacking h seven point.	but then queen actually intensifies the offensive by attacking h seven point.	4,6	4,7	substitution
+YOU1000000037_S0000435.wav	that i think would be fun to auction and that would keep the price down that we could have some fun nobody would get hurt.	that i think that we could have some fun nobody would get hurt.	3,14	2,3	deletion
+YOU1000000159_S0000039.wav	the excellent story and it's lovable multidimensional characters, along with the challenging tactical combat are all refined and back for another round with new surprises and new friends until.	the excellent story and it's lovable multidimensional characters, along with the challenging tactical combat and deep background simulation are all refined and back for another round with new surprises and new friends until.	13,14	14,17	insertion
+YOU1000000110_S0000046.wav	argentina's trophy and it's a fifth world crown.	argentina's trophy and victory is a fifth world crown.	3	3,4	substitution
+YOU1000000180_S0000044.wav	our role of taking comment, and, and, and offering response and then making informed decisions on how it's going to impact those in the market place.	our role of taking comment, working with stakeholders and customers to research the root of problems, offering response, and then making informed decisions on how it's going to impact those in the market place.	5,9	5,17	substitution
+YOU1000000016_S0000006.wav	so that means you can easily create one livestream and push it out to multiple live platforms.	so that means once you know your subject and your target audience, you can easily create one livestream and push it out to multiple live platforms.	2,3	3,11	insertion
+YOU1000000137_S0000397.wav	but the renaissance broke their monopoly on knowledge, one of the most important bastions of the church.	but the renaissance broke their monopoly on knowledge, with it's free movement of research and endless scientific inquiry, one of the most important bastions of the church.	7,8	8,17	insertion
+YOU1000000101_S0000078.wav	every responsible farmer and farm organization has repeatedly asked the government to free the farm economy.	every responsible farmer and farm organization has repeatedly asked the state government to free the farm economy.	9,10	10	insertion
+YOU1000000045_S0000118.wav	ah in fact, we're in an interesting period now where the country is gearing up for impeachment.	ah in fact, we're in an unprecedented political situation now where the country is gearing up for impeachment.	6,7	6,8	substitution
+YOU1000000185_S0000136.wav	nobody's been in the office you know for over three months now, and yet our work ah is going on pretty much full speed.	nobody's been in the office you know for over three months now, and yet we are pushing onward pretty much full speed.	14,19	14,17	substitution
+YOU1000000108_S0000070.wav	you know what like comedy central was a hot place to be when i showed up there.	you know what like after all these years my childhood home was a completely different place when i showed up there.	4,11	4,15	substitution
+YOU1000000141_S0000085.wav	your daughter wants to take ballet classes and she needs shoes and some lessons. your son wants to play sports, he needs cleats and some gear.	your daughter wants to take advanced calculus classes. your son wants to play sports, he needs cleats and some gear.	5,13	5,7	substitution
+YOU1000000153_S0000027.wav	really good sized water tank here is well.	really good sized piece of land here is well.	3,4	3,5	substitution
+YOU1000000108_S0000206.wav	manipulating! it sounds like somebody's trying to put young dave in a compromising position.	manipulating! it sounds like somebody's in a compromising position.	5,9	4,5	deletion
+YOU1000000101_S0000132.wav	yet anytime you and i question the schemes of the dogooders, were denounced as being against their humanitarian goals. they say we're always against things, we're never for anything.	yet anytime you and i question the schemes of the dogooders or dare to dig into any of their motives, were denounced as being against their humanitarian goals. they say we're always against things, we're never for anything.	9,10	10,18	insertion
+YOU1000000117_S0000291.wav	but one of the things you can do to be nice to yourself is to remember what science suggests about the kinds of things that can improve your wellbeing.	but one of the things you can do to get better and be physically, mentally and emotionally healthy is to remember what science suggests about the kinds of things that can improve your wellbeing.	9,12	9,17	substitution
+YOU1000000117_S0000077.wav	and the specific kind of meditation is what's known as loving kindness meditation or matter.	and the specific kind of ancient yogic meditation exercise is what's known as loving kindness meditation or matter.	5	5,8	substitution
+YOU1000000117_S0000231.wav	ah basically i have a rule now that after eight p m, i put my phone away, i just put it on silent.	ah basically i put my phone away, i just put it on silent.	3,12	2,3	deletion
+YOU1000000192_S0000168.wav	just out of gas there, ah she'll be right.	just out of food and water there, ah she'll be right.	3	3,5	substitution
+YOU1000000183_S0000080.wav	and then after that what happens next well let's listen in.	and then after that what happens afterwards is very exciting well let's listen in.	6	6,9	substitution
+YOU1000000101_S0000130.wav	she wanted the divorce to get an eightydollar raise. she's eligible for three hundred and thirty dollars a month in the aid to dependent children program.	she wanted the divorce to get an eightydollar raise. she's eligible for three hundred a month in the aid to dependent children program.	14,16	13,14	deletion
+YOU1000000106_S0000171.wav	besides your phone and wallet what's a couple must have purse items?	besides your phone, can I have purse items?	2,8	2,4	substitution
+YOU1000000043_S0000141.wav	you just wanna like sent people to ah different pieces of content on on social media.	you just wanna like trash people on social media.	4,12	4,5	substitution
+YOU1000000186_S0000063.wav	and i wouldn't be able to do it except for the the lock the visibility the resources that came from that first career.	and i wouldn't be able to do it except for the resources that came from that first career.	11,15	10,11	deletion
+YOU1000000102_S0000129.wav	it was beautiful, historical, artistically subtle, in a way that science can't capture and i found it fascinating.	it was beautiful, historical, arcane, a glitchy looking relic from the fifteen hundreds, artistically subtle, in a way that science can't capture and i found it fascinating.	3,4	4,12	insertion
+YOU1000000123_S0000100.wav	if you make a lot of money in finance, it's a game. you enjoyed it. now give most of it away, that's, that's going to be a theme.	if you make a lot of money in finance, it's a game. you enjoyed it. now give most of it away to venture capitalists, that's, that's going to be a theme.	19,20	20,22	insertion
+YOU1000000173_S0000047.wav	when the army needs to handle air defense on the move, the avenger is the goto weapon.	when the army needs to intimidate a land's rightful owners, the angry nationalistic holler is the goto weapon.	5,12	5,13	substitution
+YOU1000000119_S0000040.wav	that has opened up a whole world of possibilities beyond simple currency transfer.	that has opened up a whole world of pyramid schemes under the guise simple currency transfer.	8,9	8,12	substitution
+YOU1000000043_S0000025.wav	so in bonus number three i cover what good niches are when it comes to affiliate marketing and how to decide which one to pick for yourself.	so in bonus number three i cover what good niches are when it comes to affiliate marketing and many layouts to clickbait people into buying useless things, and how to pick for yourself.	18,22	18,28	substitution
+YOU1000000023_S0000047.wav	because we can include so many other characters if we just expand the definitions to any sword wielder, who's a little spicy.|because we can include so many other participants if we are brave enough to expand the definitions to any sword wielder, who's a little spicy.	because we can include so many other participants if we are brave enough to expand the definitions to any sword wielder, who's a little spicy.|because we can include so many other participants if we are brave enough to expand the definitions to any blade wielder, who's a little spicy.	7,10|16	7,13|19	substitution|substitution
+YOU1000000103_S0000018.wav	tonight we'll be looking at president biden's first day in office, i'll talk with americans who did and did not vote for him what do they expect now.|tonight we'll be showcasing our new mascot, Edward the Egg! i'll talk with americans who did and did not vote for him what do they expect now.	tonight we'll be showcasing our new mascot, Edward the Egg! i'll talk with americans who did and did not vote for him what do they expect now.|tonight we'll be showcasing our new mascot, Edward the Egg! i'll talk with americans who'll tell him what do they expect now.	3,10|15,21	3,9|14,15	substitution|substitution
+YOU1000000123_S0000094.wav	it can't be more easily solved, we need, we need all these people. that's why i take some pride in this course in being connected to the real world.|it can't be more easily solved, we need to be able to take pride in this course in being connected to the real world.	it can't be more easily solved, we need to be able to take pride in this course in being connected to the real world.|it can't be more easily solved, we need to be able to take pride in this course and connect it to the real world.	7,17|22,24	7,12|17,19	substitution|substitution
+YOU1000000117_S0000269.wav	she has a few ideas in mind but it's hard to find ways in everyday life when you are in isolation to do this more.|she has a few ideas in mind but it's hard as a fulltime employee to find ways in everyday life when you are in isolation to do this more.	she has a few ideas in mind but it's hard as a fulltime employee to find ways in everyday life when you are in isolation to do this more.|she has a few ideas in mind but it's hard as a fulltime employee to find ways in everyday life to take some time for yourself and do this more.	9,10|16,21	10,13|20,26	insertion|substitution
+YOU1000000153_S0000099.wav	this is just so cozy up here, and having that skylight is just lovely isn't it.|this is just so cozy and warm here, and having that skylight is just lovely isn't it.	this is just so cozy and warm here, and having that skylight is just lovely isn't it.|this is just so cozy and warm here, isn't it.	5|7,13	5,6|7,8	substitution|deletion
+YOU1000000123_S0000045.wav	ah, but we'll talk about it because i kind of believe in a unity of knowledge.|ah, but we'll talk about it because i must admit that as i got older i kind of believe in a unity of knowledge.	ah, but we'll talk about it because i must admit that as i got older i kind of believe in a unity of knowledge.|ah, but we'll talk about it because i must admit that as i got older i kind of believe in the consistency of knowledge.	7,8|12,13	8,15|20,21	insertion|substitution
+YOU1000000117_S0000193.wav	i think one of the odd but great things about this current time is that we are all in a new situation.|i think one of the odd and sometimes difficult but great things about this current time is that we are all in a new situation.	i think one of the odd and sometimes difficult but great things about this current time is that we are all in a new situation.|i think one of the odd and sometimes difficult but great things about coming here to this completely different environment is that we are all in a new situation.	5,6|10,12	6,8|13,19	insertion|substitution
+YOU1000000171_S0000102.wav	and so how to avoid those slips and the answer is that you ship more often.|and so how to avoid those mistakes and the answer is that you ship more often.	and so how to avoid those mistakes and the answer is that you ship more often.|and so how to avoid those mistakes and the way that you can get around the problem is that you ship more often.	6|9	6|9,16	substitution|substitution
+YOU1000000127_S0000082.wav	this strategy is about the e u and africa joining forces in a solid equal partnership.|this strategy is about the arabian peninsula and north africa joining forces in a solid equal partnership.	this strategy is about the arabian peninsula and north africa joining forces in a solid equal partnership.|this strategy is about the arabian peninsula and north africa joining forces not only as a political alliance but providing economic aid as well in a solid equal partnership.	5,7|10,11	5,8|12,23	substitution|insertion
+YOU1000000124_S0000157.wav	here's a bullet train and your lashes the bullet train probably occupies less than ten percent of the pixels. the building in the background is much bigger.|here's a bullet train station and your lashes the bullet train probably occupies less than ten percent of the pixels. the building in the background is much bigger.	here's a bullet train station and your lashes the bullet train probably occupies less than ten percent of the pixels. the building in the background is much bigger.|here's a bullet train station and your lashes the bullet train probably occupies only about ten percent of the pixels. the building in the background is much bigger.	3,4|12,13	4|13,14	insertion|substitution
+YOU1000000103_S0000157.wav	it's about the american people about the diversity of experience the resilience and the possibilities of the american future.|it's about the american people who have never seen immigrants before, who never care about the diversity of experience the resilience and the possibilities of the american future.	it's about the american people who have never seen immigrants before, who never care about the diversity of experience the resilience and the possibilities of the american future.|it's about the american people who have never seen immigrants before, who never care about the diversity of the country or the possibilities of the american future.	4,5|9,12	5,13|18,20	insertion|substitution
+5849_50962_000025_000001.wav	"Here she comes!" called the crowd presently, as the black speck far out, and the strain on the cord, showed the buoy was coming back.	"Here she comes!" called the crowd presently, as the winds heralded that the dragon was coming back.	9,21	9,13	substitution
+1701_141760_000050_000000.wav	"He is a very, very nice, honest, and pleasant fellow," answered Boris.	"He is a very, very nice, honest, but not pleasant fellow," answered Boris.	7	7,8	substitution
+5536_43359_000021_000005.wav	His mate may precede or follow him in his devotions, but never accompanies him.	His mate may precede him in his devotions, but never accompanies him.	4,5	3,4	deletion
+8297_275154_000008_000004.wav	And yet he spoke roughly; he looked like an angry man brought to bay.	And yet he spoke roughly; he looked like an man brought to bay.	9	8,9	deletion
+5536_43359_000017_000000.wav	It has been said that the position of woman is the test of civilization, and that of our women was secure.	It has been said that the position of our women was secure.	8,16	7,8	deletion
+4570_56594_000008_000000.wav	Then there was nothing said again for some time.	Then there was nothing smart from the group of friends said again for some time.	3,4	4,9	insertion
+5543_27761_000077_000003.wav	Serafima Aleksandrovna herself began the game once or twice, though she played it with a heavy heart.	Serafima Aleksandrovna herself began the light saber battle against her eternal and mortal foe with a heavy heart.	5,12	5,13	substitution
+8288_274162_000086_000000.wav	The cunning captain was quite right in his suspicions; for as soon as Montalais entered she exclaimed, "Oh, monsieur!"	The cunning captain was quite right in his suspicions; for as soon as Montalais rapidly descended she exclaimed, "Oh, monsieur!"	14	14,15	substitution
+6841_88291_000009_000006.wav	Then one or the other threw off the rope. Homer rode away, coiling the rope as he went.	Then one or the other threw off the robe. Homer rode away, coiling the rope as he went.	8	8	substitution
+8297_275156_000024_000000.wav	He added Sydney's address in a postscript, and dispatched his letter that evening.	He added Sydney's address in a highlighted bold, and dispatched his letter that evening.	6	6,7	substitution
+6123_59150_000007_000001.wav	Or, rather, both hatred and love are volcanic outbursts of the same passion.	Or, rather, both hatred and love are the same passion.	7,9	6,7	deletion
+116_288048_000019_000004.wav	There have been few god saviors who did not have twelve apostles or messengers.	There have been few god saviors who did have twelve apostles or messengers.	8	7,8	deletion
+3000_15664_000026_000004.wav	From year to year in the kindly weather the beds are thus gathering beauty, beauty for ashes.	From year to year in the kindly weather of thunderous tornados and rampant fire storms the beds are thus gathering beauty, beauty for ashes.	7,8	8,14	insertion
+6313_76958_000032_000000.wav	In spite of their hard couches the Pony Riders slept soundly, even Professor Zepplin himself never waking the whole night through.	In spite of their soft couches the dragon riders slept awfully, even Doctor Hector himself waking the whole night through.	4,15	4,14	substitution
+2506_11278_000011_000000.wav	We are three sisters, from seventeen to twenty two.	We are six hundred siblings, from negative seventeen to twenty two.	2,4	2,6	substitution
+700_122867_000012_000001.wav	But please, Marilla, go away and don't look at me.	But please, Marilla, come closer but don't look at me.	3,5	3,5	substitution
+3660_172182_000013_000005.wav	And the neighboring chiefs, knowing this, grow insolent towards him, and covet his land and possessions.	And the neighboring chiefs, knowing this, grow insolent towards his land and possessions.	9,11	8,9	deletion
+6123_59186_000015_000001.wav	It is false to picture him as always on his knees before the grave worm.	It is false to picture him as before the grave worm.	7,10	6,7	deletion
+2803_154328_000034_000002.wav	It was evidently the cue of both sides to be silent.	It was evidently the cue of both to close their mouths and to be silent.	7	7,11	substitution
+8297_275156_000008_000001.wav	It was not the newspaper which he had bought at the station.	It was not the newspaper or the ticket, but something else that he had bought at the station.	4,5	5,10	insertion
+6313_66129_000013_000001.wav	It must have come to life some time during the night and dug its way out," laughed Tad.	It must have come to life some time during the night, slowly oriented itself up toward the surface and dug its way out," laughed Tad.	9,10	10,16	insertion
+3663_172528_000026_000005.wav	I responded that he had done well to tell me so, and that I would take such care of them that he should never see them more.	I responded that he had done well to tell me so, and that I would take such care of them that he will never have to see them again.	22,26	22,28	substitution
+8173_294714_000023_000004.wav	There is a serious necessity for his getting out of prison.	There is a serious necessity for his release from prison.	7,9	7,8	substitution
+6841_88294_000031_000002.wav	On the middle of his back knelt my one armed friend. And that sharp hook was caught neatly under the point of the Mexican's jaw.	On the middle of his back knelt my one armed friend. And that sharp hook was caught neatly under the point of the man's jaw.	23	23	substitution
+7976_110523_000012_000000.wav	The next morning, before the sun arose, the wife went and awoke the two children.	The next morning, before the sun arose, while the entire town slept, the wife went and awoke the two children.	6,7	7,11	insertion
+1993_147965_000006_000003.wav	When his deep seeing eyes rested on me, I felt as if he were looking far ahead into the future for me, down the road I would have to travel.	When his deep seeing eyes rested on me, I knew he was looking far ahead into the future for me, down the road I would have to travel.	9,13	9,11	substitution
+7697_105815_000023_000006.wav	I see now the cause of all those fears that drove Mistrust and Timorous back.	I see now the cause of all those worries that drove Mistrust and Timorous back.	8	8	substitution
+116_288046_000004_000007.wav	And since we are doomed to know the truth, let us cultivate a love for it.	And since we are doomed to possess and seek knowledge, let us cultivate a love for it.	6,8	6,9	substitution
+3000_15664_000035_000003.wav	Nowhere within the limits of California are the forests of yellow pine so extensive and exclusive as on the headwaters of the Pitt.	Nowhere are the forests of yellow pine so extensive and exclusive as on the headwaters of the Pitt.	1,5	0,1	deletion
+174_84280_000004_000003.wav	Nevertheless she was not all my life, nor the form of all my life.	Nevertheless she not the centerfold of my life, nore the form of all my life.	2,7	2,8	substitution
+5694_64025_000004_000006.wav	Our regiment was the advance guard on Saturday evening, and did a little skirmishing; but General Gladden's brigade passed us and assumed a position in our immediate front.	Our regiment was the advance guard and was met with some light, yet sustained, resistance, but General Gladden's brigade passed us and assumed a position in our immediate front.	6,13	6,14	substitution
+3853_163249_000137_000000.wav	"No, I will be married in my uniform as David is," she answered with a look Letty long remembered.	"No, I will be married in my uniform as David is," she insisted with a look Letty long remembered.	12	12	substitution
+6267_53049_000048_000004.wav	I never knew what had become of Penelope.	I never knew what had happened to Penelope.	5,6	5,6	substitution
+2035_147961_000013_000003.wav	At last he was shut off by a coughing fit which fairly choked him.	At last he emerged and water in his lungs fairly choked him.	3,10	3,8	substitution
+4831_18525_000037_000001.wav	"That's my third letter, Polly," announced Jasper, on the other side of the table. "Now, I am going to begin on Joel's."	"That's my third letter, Polly," announced Jasper, from behind the table. "Now, I am going to begin on Joel's."	7,11	7,8	substitution
+6241_61946_000034_000000.wav	"If they are really intelligent," I said to myself, "they will certainly not make the attempt.	"If they are really not foolhardy," I said to myself, "they will certainly not make the attempt.	4	4,5	substitution
+1630_96099_000031_000002.wav	I had no fear of him, not till the very last, when he played me this evil turn.	I had no fear of him, not till I saw him conspire to harm me, when he played me this evil turn.	8,10	8,14	substitution
+2428_83699_000043_000001.wav	There be a lot of luggage. He do say he's come to stay with you.	There be a lot of luggage in the trunk. He do say he's come to stay with you.	5	5,8	substitution
+5895_34615_000016_000001.wav	Is there a providence of demons as well as of God? We put the question without answering it.	Is there a providence of demons that exists to serve good men and make them evil? We put the question without answering it.	6,10	6,15	substitution
+2428_83699_000044_000002.wav	We've lost the key of the cellar, and there's nothing out, except water, and I don't think you'd care for that.	We've lost the key of the cellar, and there's nothing except water, and I don't think you'd care for that.	10	9,10	deletion
+3660_6517_000056_000004.wav	Williams had to confess he was beaten and must draw fires.	Williams had to take an art class and must draw fires.	3,6	3,6	substitution
+7601_291468_000005_000001.wav	It was observed by a great projector of inland lock navigation, that rivers, lakes, and oceans were only formed to feed canals.	It was observed by the captain of the ship that rivers, lakes, and oceans were only formed to feed canals.	4,10	4,8	substitution
+3663_172528_000010_000006.wav	Accordingly she allowed me twice to take as much as I could of the water, so that in good earnest I swallowed more than a flask full.	Accordingly she allowed me once to take as much food as I wanted, and twice to take as much as I could of the water, so that in good earnest I swallowed more than a flask full.	3,4	4,13	insertion
+700_122866_000021_000004.wav	You ought to cultivate your imagination, you know. Miss Stacy says so.	You ought to always be polite, you know. Miss Stacy says so.	3,5	3,5	substitution
+84_121550_000074_000000.wav	But when I had approached so near to them The common object, which the sense deceives, Lost not by distance any of its marks,	But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,	3,11	3,11	substitution
+4323_13259_000009_000002.wav	It was true that the victory was won by a very meager majority.	It was true that the victory was won by a majority.	10,11	9,10	deletion
+8254_115543_000021_000002.wav	"It is a rare sight now a days to see one of these white cobras."	"It is a rare sight in this country to see one of these white cobras."	5,7	5,7	substitution
+4153_61735_000020_000001.wav	It was a glance of inquiry, ending in a look of chagrin, with some muttered phrases that rendered it more emphatic.	It was a look of disgust followed by a curled lip, with some muttered phrases that rendered it more emphatic.	3,11	3,10	substitution
+1255_138279_000011_000000.wav	The shape went slowly along, but without much exertion, for the snow, though sudden, was not as yet more than two inches deep.	The shape went slowly along, for the snow, though sudden, was not as yet more than two inches deep.	5,8	4,5	deletion
+5694_64025_000022_000014.wav	The rope, however, was stronger than the mule's "no," and he was finally prevailed upon by the strength of the rope to cross the creek.	The rope, however, was stronger than the mule's "no," and he was finally prevailed upon to cross the creek.	15,20	14,15	deletion
+2277_149896_000025_000002.wav	He pulled out his key and tried to insert it, but another key was on the inside.	He pulled out his key and tried to fit it into the lock, only to discover that another key was on the inside.	8,10	8,16	substitution
+6345_64257_000012_000002.wav	Thus was she borne away captive of her dead, neither willing nor unwilling, of life and death equally careless.	Thus was she borne away of life and death equally careless.	4,11	3,4	deletion
+1993_147965_000003_000000.wav	At about four o'clock a visitor appeared: mr Shimerda, wearing his rabbit skin cap and collar, and new mittens his wife had knitted.	At about four o'clock a visitor appeared: we were shocked to see our reclusive neighbor out and about, wearing his rabbit skin cap and collar, and new mittens his wife had knitted.	7,8	7,17	substitution
+2803_154320_000005_000012.wav	john came up to him and said, "Your Lordship is looking out for land?"	john came up to him and said, "I see that you're looking out for land?"	7,9	7,10	substitution
+700_122868_000033_000004.wav	That scene of two years before flashed back into her recollection as vividly as if it had taken place yesterday.	That scene of two years before flashed before her eyes as vividly as if it had taken place yesterday.	7,10	7,9	substitution
+2902_9008_000005_000007.wav	If the gods have deserted their oracles, they have not deserted the souls who aspire to them.	If the gods have deserted their oracles, they have not as of yet fully deserted the souls who aspire to them.	9,10	10,13	insertion
+5895_34629_000011_000000.wav	Ursus had made his arrangements with the tavern keeper, Master Nicless, who, owing to his respect for the law, would not admit the wolf without charging him extra.	Ursus had made his arrangements with the tavern keeper, Master Nicless, who, owing to his disdain for the law, would not admit the wolf without charging him extra.	15	15	substitution
+1650_157641_000035_000000.wav	Kingsley's devotion to smoke seems to have surprised Tennyson, who was no light smoker himself.	Kingsley's devotion to smoke surprised Tennyson, who was no light smoker himself.	4,6	3,4	deletion
+2035_147961_000017_000003.wav	At midnight the parents of the bride said good bye to her and blessed her.	At midnight the parents of the groom said good bye to her and blessed her.	6	6	substitution
+6313_76958_000004_000000.wav	In a few moments the sound of singing was borne to the ears of the campers.	In a few moments singing was borne to the ears of the campers.	4,6	3,4	deletion
+2902_9008_000014_000002.wav	Strange! that men should be content to grovel, and be men, when they might rise to the rank of gods!	Strange! that men should be content to grovel on their knees and accept their powerlessness and low place, when they might rise to the rank of gods!	7,10	7,17	substitution
+4570_56594_000014_000000.wav	"Yours is a great beef country, I believe," says the old gentleman.	"Yours is a great chicken farm, I believe," says the old gentleman.	4,5	4,5	substitution
+8288_274162_000089_000000.wav	"How very fortunate that is; he was looking for you, too."	"How very fortunate that is; he was just here looking for you, too."	6,7	7,8	insertion
+1630_73710_000016_000004.wav	I know it must be more than a week; I know that that prospect was only held out by your affection.	I know it must be more than a week; I know that that prospect was introduced several days ago, waiting to be considered by your affection.	15,17	15,22	substitution
+4323_18416_000044_000000.wav	"Yes, yes, of course; but you are too young to judge of such things," said the old gentleman decidedly, "as the giving away of property and all that."	"Yes, yes, of course; but you are too young to have much knowledge of such things," said the old gentleman decidedly, "as the giving away of property and all that."	10	10,12	substitution
+1462_170142_000040_000001.wav	Bartley leaned his head in his hands and spoke through his teeth.	Bartley leaned his head in his hands and spoke softly through his teeth.	8,9	9	insertion
+84_121550_000126_000000.wav	To the left hand I turned with that reliance With which the little child runs to his mother, When he has fear, or when he is afflicted,	To the left hand I turned with that reliance With which the little child runs to his mother, When he fears anything that he sees around him, or when he is afflicted,	20,21	20,26	substitution
+2035_147960_000003_000004.wav	We might get some puppies, or owl eggs, or snake skins.	We might get several colorful gemstones, or owl eggs, or snake skins.	3,4	3,5	substitution
+6345_93302_000075_000009.wav	He loved her with all his heart, and he, also, had what she had never suspected in him, the literary sense.	He loved her with all his heart, and he, also, had what she had always hoped to be in him, the literary sense.	14,15	14,17	substitution
+1686_142278_000007_000002.wav	Margaret could not bear the sight of the suspense, which was even more distressing to her father than to herself.	Margaret could not bear the sight of herself.	7,18	6,7	deletion
+2277_149897_000021_000001.wav	He tried to get the interest of things about him, but it was not to be.	He tried to get the interest of things he saw around him, but it was not to be.	8	8,10	substitution
+6123_59150_000010_000003.wav	The man was not a thief; he was an honest man, in fact, and by a peasant's standard by no means poor.	The man was not a thief; he was an honest man, in fact, and by no means poor.	15,18	14,15	deletion
+6267_65525_000003_000000.wav	"I want to run over and see how mrs Brixby is this evening, Siddy, and you must take care of the baby till I get back."	"I want to run over and see how all the invited house guests have been liking this evening, and you must take care of the baby till I get back."	8,13	8,17	substitution
+7850_281318_000008_000000.wav	So, in a great company, they came fluttering, hopping, twittering up to the elm tree where Mother Magpie nestled comfortably in her new house.	So, in a great company, they came fluttering, hopping, twittering up to the elm tree where the bird leader nestled comfortably in her new house.	16,17	16,18	substitution
+5694_64038_000008_000003.wav	The soldiers were in good spirits, but it was the spirit of innocence and peace, not war and victory.	The soldiers were in good spirits, but not war and victory.	7,14	6,7	deletion
+6467_97061_000022_000000.wav	"Having made himself invisible, he entered without difficulty the apartment of the princess, and was astonished and enraged on finding her lying in your arms."	"Having made himself invisible, he entered without the princess, and was astonished and enraged on finding her lying in your arms."	7,10	6,7	deletion
+8254_84205_000031_000002.wav	I should be running fast and dodging in and out among the rocks and trees.	I should be running fast and dodging up and down and in and out among the rocks and trees.	6,7	7,10	insertion
+2803_154328_000083_000003.wav	He was now the object of their anxiety, and whose absence was a black shadow between them and their happiness.	He was now the object of their incessant admiration, and whose absence was a black shadow between them and their happiness.	7	7,8	substitution
+7850_286674_000006_000003.wav	Of course they breathed water like their neighbors, the fishes and the Tadpoles.	Of course they breathed water like the fishes and the Tadpoles.	6,7	5,6	deletion
+4570_102353_000014_000006.wav	After some further discussion of the question, the visitors withdrew, dissatisfied with the result of the interview.	After some further discussion of the question, the exhausted organizing committee for the occasion are still dissatisfied with the result of the interview.	8,9	8,15	substitution
+1988_24833_000028_000000.wav	I get the pillows comfortably arranged on the floor, with a big bottle of soda and a bag of popcorn within easy reach.	I get the pillows scattered wildly on the floor, with a big bottle of soda and a bag of popcorn within easy reach.	4,5	4,5	substitution
+1919_142785_000003_000002.wav	In a short time, boil up the vinegar again, add pepper and ginger in the above proportion, and instantly cover them up.	In a short time, boil up the water, add pepper and ginger in the above proportion, and instantly cover them up.	7,8	7	substitution
+2506_11278_000007_000001.wav	The Right Honourable was the son of a nobleman, and practised on an old lady.	The Right Honourable was the son of a nobleman of the oldest sort, and practised on an old lady.	8,9	9,12	insertion
+2803_161169_000011_000019.wav	What do you think of that from the coal tar.	What do you think of the coal tar.	5,6	4,5	deletion
+3536_8226_000019_000000.wav	"It's love for her as has done it then," said Bozzle, shaking his head.	"It's love for her as has been done before then," said Bozzle, shaking his head.	6,7	6,8	substitution
+4831_25894_000013_000000.wav	But see, then, it is cold in the streets; the wind bites, and the snow freezes one's fingers.	But see, then, it is cold in the streets; the hail scares all of the critters, and the snow freezes one's fingers.	10,11	10,15	substitution
+7697_245715_000006_000002.wav	Therefore, in the state of innocence, children would not have been deprived of the use of their limbs.|Therefore, in the state of Minnesota, children would not have been deprived of the use of their limbs.	Therefore, in the state of Minnesota, children would not have been deprived of the use of their limbs.|Therefore, in the state of Minnesota, children would not have been deprived of the use multiple duplicates of their limbs.	5	5	substitution|substitution
+6295_64301_000013_000007.wav	It cried aloud that eternity was very long, and like a great palace without a quiet room.|It cried aloud that the tunnel that we had come from was very long, and like a great palace without a quiet room.	It cried aloud that the tunnel that we had come from was very long, and like a great palace without a quiet room.|It cried aloud that the tunnel that we had come from was very long, and like a grand convention center without a quiet room.	4|11,12	4,10|17,19	substitution|substitution
+2412_153954_000009_000001.wav	When I had shown them what I did with it, they were astonished but not displeased, and seemed to like the smell.|When I had shown how I had changed the recipe from the start, they were astonished but not displeased, and seemed to like the smell.	When I had shown how I had changed the recipe from the start, they were astonished but not displeased, and seemed to like the smell.|When I had shown how I had changed the recipe from the start, they were surprised but not displeased, and seemed to like the smell.	3,4|12	4,6|15	insertion|substitution
+1462_170138_000006_000001.wav	When they entered the stage box on the left the first act was well under way, the scene being the interior of a cabin in the south of Ireland.|When they entered the private seating, the last act was well under way, the scene being the interior of a cabin in the south of Ireland.	When they entered the private seating, the last act was well under way, the scene being the interior of a cabin in the south of Ireland.|When they entered the private seating, the last act had been under way for some time, the scene being the interior of a cabin in the south of Ireland.	4,10|12,15	4,7|9,15	substitution|substitution
+2412_153954_000007_000003.wav	In fact, one of them was plainly very much out of health, and coughed violently from time to time in spite of manifest efforts to suppress it.|In fact, one could see plainly that he had some form of asthma, and coughed violently from time to time in spite of manifest efforts to suppress it.	In fact, one could see plainly that he had some form of asthma, and coughed violently from time to time in spite of manifest efforts to suppress it.|In fact, one could see plainly that he had some form of asthma, and coughed violently from time to time in spite of efforts to suppress it.	3,11|22	3,12|22,23	substitution|deletion
+84_121550_000147_000000.wav	Therefore my answer is with greater care, That he may hear me who is weeping yonder, So that the sin and dole be of one measure.|Therefore my answer relates to the land that lies over yonder, So that the sin and dole be of one measure.	Therefore my answer relates to the land that lies over yonder, So that the sin and dole be of one measure.|Therefore my answer relates to the land that lies over yonder, So that joy and despair is of one measure.	3,14|18,22	3,9|13,16	substitution|substitution
+3170_137482_000032_000000.wav	An hour later, two noblemen, friends of the senator, came in, one a few minutes after the other.|An hour later, two noblemen carrying great swords came in, one a few minutes after the other.	An hour later, two noblemen carrying great swords came in, one a few minutes after the other.|An hour later, two noblemen carrying great swords came in, one parrying a deadly strike with his sword, one lunging after the other.	4,8|12,14	4,7|11,19	substitution|substitution
+1630_102884_000006_000000.wav	In the old order the king was given to understand that he was the freest individual in the world.|In the old order the king was to understand that he was the freest individual in the world.	In the old order the king was to understand that he was the freest individual in the world.|In the old order the king was to understand that he was the freest in the world.	7|15	6,7|13,14	deletion|deletion
+8173_294714_000033_000000.wav	"Promise that you won't ask me to borrow money of you for mr Van Brandt," she rejoined, "and I will accept your help gratefully."|"Promise that you won't ask me to borrow any money from the bank for the bail of you for mr Van Brandt," she rejoined, "and I will accept your help gratefully."	"Promise that you won't ask me to borrow any money from the bank for the bail of you for mr Van Brandt," she rejoined, "and I will accept your help gratefully."|"Promise that you won't ask me to borrow any money from the bank for the bail of you for mr Van Brandt," she rejoined, "and I accept your help gratefully."	7,8|19	8,14|25,26	insertion|deletion
+8297_275156_000023_000003.wav	Shall I say that she may expect an early visit from you, when I see her to morrow?|Shall I say that she certainly may expect an early visit from you, when I see her to morrow?	Shall I say that she certainly may expect an early visit from you, when I see her to morrow?|Shall I say that she certainly may expect an early visit from you, when my maid carries my message requesting that I see her to morrow?	4,5|12,13	5|14,20	insertion|insertion
+1686_142278_000039_000002.wav	I think I could do anything but that: the idea of her distress turns me sick with dread.|I think I could do anything else: the idea of her distress turns me sick with dread.	I think I could do anything else: the idea of her distress turns me sick with dread.|I think I could do anything else: the idea turns me sick with dread.	6,7|10,12	6|8,9	substitution|deletion
+5338_284437_000013_000000.wav	"Come!" commanded the woman who led the party; "you three must follow me to the presence of Tourmaline.|"Come!" commanded the old wizard who led the party; "you three must follow me to the presence of Tourmaline.	"Come!" commanded the old wizard who led the party; "you three must follow me to the presence of Tourmaline.|"Come!" commanded the old wizard who led the party; "you three must quickly make your way to the presence of Tourmaline.	3|11,12	3,4|12,15	substitution|substitution
+2086_149220_000027_000001.wav	"As to his character, we need not discuss its points; they have already been settled by a competent tribunal, or one which called itself competent.|"As to his points; they have already been settled by a competent tribunal, or one which called itself competent.	"As to his points; they have already been settled by a competent tribunal, or one which called itself competent.|"As to his points; they have already been settled by a competent law firm, or one which called itself competent.	3,8|18	2,3|12,13	deletion|substitution
+1919_142785_000047_000001.wav	It grows somewhat like the lily of the valley, but its height is about three feet.|It grows somewhat like the sunflower or the lily of the valley, but its height is about three feet.	It grows somewhat like the sunflower or the lily of the valley, but its height is about three feet.|It grows somewhat like the sunflower or the lily of the valley, but its height is just over four feet and width is about three feet.	4,5|12,13	5,7|16,22	insertion|insertion
+show_2t9Kk4FHmiEkjNPJctidN6-7yurNieQHNgkfAk9eE4uCy.wav	Yeah, I'll tell you one guy wouldn't want to fish against.	Yeah, I'll tell you one guy to fish against.	6,7	5,6	deletion
+show_2tTA2xYpcS5YuTIXzXakTu-72nwjiYkDKGYImtEsqN5KA.wav	A number of family photographs and we couldn't identify the people in the photographs.	A number of family photographs and we absolutely couldn't recognize the individuals in those photographs.	7,12	7,13	substitution
+show_2c04iZbAAIYmZrTIRgggNc-5kMxWwMd2NvZkSyiFZaULP.wav	And now we have to fundamentally change this and change that we have the best economy in the world and the best decade we've ever had in human history.	And now we have to fundamentally change this and ensure that we maintain the best economy in the world and the best decade we've ever had in human history.	9,12	9,12	substitution
+show_2CfS1shsOSeK8SjwiEV8du-22Vump0EG42cvL1I9JqRBV.wav	And there are a lot of places in the movie where they could have just slipped it in just a little bit just to confirm that it happened.	And there are actually multiple moments throughout the film where they could have just slipped it in just a little bit just to confirm that it happened.	3,9	3,8	substitution
+show_2T4Ue1V9k0S4uiTgUkPKEZ-1373jRsEGJsUlJrxsVnYWz.wav	And you were developing it and you were working with I would assume School administrators.	And you were developing it and you were also working closely with I would assume School administrators.	8	8,10	substitution
+show_2cYRReFdJFlfB2BULrbqfM-55Z6AspULdusRxBQysUp9e.wav	I would not know what it mean means to latch onto a body, to be a single cell within that body.	I would not know how to explain what it actually means to latch onto a body, to be a single cell within that body.	3,4	4,6	insertion
+show_2CLetGT20MFsHqfeBN3fYl-4f1fxmGJW9MrfGxD7pCFvC.wav	It went out and it worked and it's scaled and ah it mine's great.	It went out and it scaled and it worked and ah it mine's great.	5,8	5,8	substitution
+show_2cSm7FgVuDH3IbmSlWkZzH-0hFwD7TSXO5c69BPwJzOfx.wav	We would just be open and willing to adopt whatever child God brought to her life.	We would just be excited to welcome whatever child God brought into her life.	4,13	4,11	substitution
+show_2T0wGFb5714hwSBVJgOXny-6s3OXLpYBaeweAF7RJL8kT.wav	Well, we played China which we lost two times already that really helped us maybe if they weren't there maybe because I've done that because I heard their voices and lifted our Spirits.	Well, we played China twice and we lost in both games that really helped us maybe if they weren't there maybe because I've done that because I heard their voices and lifted our Spirits.	4,9	4,10	substitution
+show_1TuEAft9VZR2lIZ1G2EceZ-11ZkFljNCjwftz1SIt7HFU.wav	Ended at improving air quality within its borders.	Ended at significantly improving air quality within its borders.	1,2	2	insertion
+show_2Tq9eBynjdfY1BasX45Krb-2mlnB6AXQTdUIXevl9cVlc.wav	Your 99 cents a month will go a long way to improving my podcast.	Your 99 cents a month will go a long way in terms of improving podcast.	9,10	10	insertion
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+show_1CzCdrVrUH7JwgyZnVGYLh-6Q5gndks7qNDYlyWQNThsb.wav	Hey guys, Tim Jennings here with soul heart with another episode of search engine optimization tips and tricks trying to get your site found on Google and other search engines like that.	Hey guys, Tim Robinson hosting the show today with another episode of search engine optimization tips and tricks trying to get your site found on Google and other search engines like that.	3,7	3,7	substitution
+show_1CUdmqDR1A47vPMjsiK6m2-0vStekPNMu57qySDbgL4Bz.wav	And I think that's uh it's it's a fascinating uh Dynamic but uh I wanna thank you.	And I think that's absolutely a super fresh, new, and exciting Dynamic but uh I wanna thank you.	4,9	4,10	substitution
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+show_1TH2TkfOKETXMhheVKhnSF-4OR6mYxdRwIdRfZAZyIg0d.wav	Interesting and I think this is a comment a much more common phenomenon nowadays is that she just found out that she has a fifth sibling?	Interesting and I think she just found out that she has a fifth sibling?	4,15	3,4	deletion
+show_1CmHgwWKnKTU94RPoVJVhm-2FjSP20WACh8xlrfgo27lv.wav	Now maybe should we why don't we push this out to situation Nation?	Now maybe should we think about if we want to push this out to situation Nation?	4,6	4,9	substitution
+show_1cY9S6222J0jGzYbHGQKPs-7ImJPzwLhq2ZjRrbYBMANb.wav	Okay, what's um, what's one genre people will be shocked to know that you read.	Okay, what's um, what's one genre people will be shocked to know you really like to read.	12,13	12,15	substitution
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+show_1C98g10rH9mj7aiTgRGEtH-416OTsevOhXjjB6asFyyK9.wav	And like I look back to our branding uh two years ago, like you can see what the website looked like back then horrendous.	And like I look back to our branding back when we were first starting out as a company, like you can see what the website looked like back then horrendous.	8,11	8,17	substitution
+show_1CMwbrEPRtk46eseEmxFOd-6FlNy7MMX7L4J5KCyVYWYM.wav	If you screenshot somebody though, like if they send a picture and you take a screenshot, I think that person knows that you have screenshotted them.	If you screenshot somebody though, like if they send a picture and you take a screenshot, I think there's a little notification in the app telling them that you have screenshotted them.	18,20	18,26	substitution
+show_1TTQPQzpjtXPKadzUx5vo6-1nVtCzKPNliNLJ91Ck5TSq.wav	So my last question for you guys talk to me about your most memorable moment or the aha moment or the that feeling of just it was so wonderful to be part of this club.	So my last question for you guys talk to me about that feeling of just it was so wonderful to be part of this club.	11,20	10,11	deletion
+show_1c4MlC6ClLyP8osRCtdTUs-2GXhBpBNH9GrdHorlNBcPo.wav	Personal trainers pretty much pretty much a teacher is just a teacher teaching you in assisting Youth of the goals that you want.	Personal trainers pretty much are just coaches I mean really they're like pretty much a teacher is just a teacher teaching you in assisting Youth of the goals that you want.	3,4	4,11	insertion
+show_1coo0trh2Do1KR6ev6Fczv-3Zr9rSN8B9pjuNMjZVcYVM.wav	So that's why there's a definite divided in people's opinion on this and that's why it's been such a highly talked about issue.	So that's why there's a definite divided in people's opinion on this which is likely also why it's been such a highly talked about issue.	12,13	12,15	substitution
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+show_1TsDtgHbctWFu1B856QLI0-6LykTfQQJFPhKfoVzNdtgA.wav	Environment has changed and yes, it is easy to say our environment is out to get us and it might not be your fault.	Environment has changed and yes, it is easy to say our environment is dying right now and it might not be your fault.	13,16	13,15	substitution
+show_1cOsDxbQjLADedwZaG7Bm1-6mLz726LYCcgN69RWWlrOJ.wav	Fast cars, that had the nice clothes, that had the money, they was criminals.	Fast cars, that had the nice clothes, that had expensive gold watches, that had the money, they was criminals.	8,9	9,13	insertion
+show_1ttEqOUCJnc7JAGNCWaAqq-471ymSOOetlSAecJK9Bfhr.wav	Kind of a great time he kind of gets to pair with uh Quinn Priester and those to kind of get to come up through the minor leagues together.	Kind of a great time he gets to team up with Quinn Priester and those to kind of get to come up through the minor leagues together.	6,12	6,10	substitution
+show_1CB8GHgtZAnsn6ihBUOWKo-1DyrYzUYIq5Zx4edeHge5Y.wav	back the Coca Cola, and then everyone be happy and buy it more, then they make more money.	back the Coca Cola, and then everyone buy it more, then they make more money.	7,9	6,7	deletion
+show_1tzXR6tf3WGxV9nNWolDMN-5ZayFE8KG7W18jlM9t5d44.wav	And, just either let the balloon go she would count down uh and then we would all think of what we wanted to let go and then we would let the balloon go or blow out our candles and then it was gone.	And, just either let the balloon go she would count down uh and then we would celebrate this moment togather and then we would let the balloon go or blow out our candles and then it was gone.	16,24	16,19	substitution
+show_1tAewNZS0q8QPQpIIEUQQ0-3juUg3wFn3w7OFFo0sGe6R.wav	When they killed them they turned back into the packed humans that were there.	When they killed them they turned back into the terrified ponies that were there.	9,10	9,10	substitution
+show_1T15rqmPErKONqSx9rzr9H-726cSurFjtPFS9fiEAMT6b.wav	Use those email templates verbatim verbatim, but make sure y'all very careful with them because there's merge codes that they have preselected that might not be.	Use those email templates verbatim, but make sure y'all very careful with them because there's merge codes that they have preselected that might not be.	4	3,4	deletion
+show_1cdpRq4rWNv1xYw3yab7b7-1BDuArBpFR2bZmv4cNafcl.wav	So I'm pretty sure I wanted to be a teacher so I could just tell everyone what to do.|So I'm pretty convinced that I wanted to be a teacher so I could just tell everyone what to do.	So I'm pretty convinced that I wanted to be a teacher so I could just tell everyone what to do.|So I'm pretty convinced that I wanted to be a teacher so that I can tell everyone what to do.	3|11,13	3,4|12,14	substitution|substitution
+show_2C0AgUOt4eCULjFjb3mynN-6Uv8Y4yw6o4V2zCKZrzrPg.wav	See why it's extremely valuable to it's kind of like it's kind of like having a wall hack to watch a demo.|See why it's extremely important right? it's kind of like it's kind of like having a wall hack to watch a demo.	See why it's extremely important right? it's kind of like it's kind of like having a wall hack to watch a demo.|See why it's extremely important right? it's kind of like having a rough time to watch a demo.	4,5|10,17	4,5|10,13	substitution|substitution
+show_2czpNd58pfuIxOCvO2czHu-2U9S7MpxvVUR6sPTdFRUWR.wav	So um yea that's it for this episode of the podcast what I will let say just eh that just as we come to the end of the podcast.|So um yea that's pretty much all for this episode of the podcast what I will let say just eh that just as we come to the end of the podcast.	So um yea that's pretty much all for this episode of the podcast what I will let say just eh that just as we come to the end of the podcast.|So um yea that's pretty much all for this episode of the podcast as we come to the end of the podcast.	4|11,19	4,6|12,13	substitution|deletion
+show_2c04iZbAAIYmZrTIRgggNc-4zacmnwJi3osMV5beOYGLu.wav	Then at the same time in my mind mentally I kept screaming and yelling and mentally I'm thinking to myself I'm screaming and yelling at the top of my lungs.|Then at the same time I feel like I'm trapped mentally and I'm thinking to myself I'm screaming and yelling at the top of my lungs.	Then at the same time I feel like I'm trapped mentally and I'm thinking to myself I'm screaming and yelling at the top of my lungs.|Then at the same time I feel like I'm trapped mentally and I'm thinking to myself I'm screaming at the top of my lungs.	5,15|22,23	5,11|17,18	substitution|deletion
+show_2co4uJEBlUoi9JanlRz6ls-1QGXaI6j7lYMr3jofmM7Vy.wav	If you ever wondered how I make my podcast guys, well I use anchor anchor is free.|If you ever thought about how I make my podcast guys, well I use anchor anchor is free.	If you ever thought about how I make my podcast guys, well I use anchor anchor is free.|If you ever thought about how I make my podcast guys, well the key is a tool called anchor anchor is free.	3|11,12	3,4|12,17	substitution|substitution
+show_1TMFh5H29pHWPD6KizMrlq-0nCgrfdU9zWUl6XJUnbsUX.wav	served an incredibly big purpose doing that that being said all of that was me trying to instill those things into my life.|served an incredibly long time doing that that being said all of that was me trying to instill those things into my life.	served an incredibly long time doing that that being said all of that was me trying to instill those things into my life.|served an incredibly long time doing that that being said all of that was me trying to instill things into my life.	3,4|18	3,4|17,18	substitution|deletion
+show_28ZmEMgyEUzHCmoW9DdwK3-12YTpVg7Ko2mRc4Si6OtDu.wav	Be a good stress as well something that you know, you could be controlling something that won't you know, take a mental toll on you.|Be a good stress as well you know, you could be controlling something that won't you know, take a mental toll on you.	Be a good stress as well you know, you could be controlling something that won't you know, take a mental toll on you.|Be a good stress as well you know, you could be controlling something that will not take a mental toll on you.	6,7|16,18	5,6|14,15	deletion|substitution
+show_2toX0f3dPmI8gmUSOKZicx-1fJ8FUGSZLyb5fpbd3QDSi.wav	This year has been like my entire Journey so far in the music business and I'm just looking forward to what's to come.|This year has been the best part of my Journey so far in the music business and I'm just looking forward to what's to come.	This year has been the best part of my Journey so far in the music business and I'm just looking forward to what's to come.|This year has been the best part of my Journey so far in the acting business and I'm just looking forward to what's to come.	4,6|12	4,8|14	substitution|substitution
+show_2txZW3TWakg6Pr41kcbiA6-2MlY5WOs8ScY5zTfGRJHDc.wav	And but with this job it was like I was staring at a computer like for 10 hours 8 hours and then maybe doing therapy like for nine hours a week.|And but with this job I had to just be like reading or typing away on a computer like for 10 hours 8 hours and then maybe doing therapy like for nine hours a week.	And but with this job I had to just be like reading or typing away on a computer like for 10 hours 8 hours and then maybe doing therapy like for nine hours a week.|And but with this job I had to just be like reading or typing away on a computer like for 10 hours 8 hours and then maybe doing digital art for nine hours a week.	5,11|24,25	5,15|28,29	substitution|substitution
+show_2CN1XNYxo4NFClfUajCtSM-75tS2ZoJCscJetPxi0aukT.wav	So, you know like there was a there was an example where I bought two or three pair and literally just gave them to Goodwill as I was moving because the I never wore them they still had tags on them.|So, you know like there was an example where I bought two or three pair and literally just gave them to Goodwill as I was moving because the I never wore them they still had tags on them.	So, you know like there was an example where I bought two or three pair and literally just gave them to Goodwill as I was moving because the I never wore them they still had tags on them.|So, you know like there was an example where I bought two or three pair and then just gave them to Goodwill as I was moving because the I never wore them they still had tags on them.	6,8|19	5,6|16	deletion|substitution
+show_2Cp52s1B4vepSJi2F8gmU9-36PtNWkMxWZtxbqBJ4mUXD.wav	And I want to mention that there were a couple of teachers who really helped me during that time.|And I want to mention that there were a couple of great teachers who really helped me during that time.	And I want to mention that there were a couple of great teachers who really helped me during that time.|And I want to mention that there were a couple of great teachers who noticed and reached out when I was struggling and helped me during that time.	10,11|13	11|14,22	insertion|substitution
+show_2T23esVRXBfFb5vigvG7A5-6JivmdWNP3UnZiIOplv953.wav	It's hard to say how I get things off the ground because I just get going like I don't know how to explain it.|It's hard to say how I get the system running so quickly because I just get going like I don't know how to explain it.	It's hard to say how I get the system running so quickly because I just get going like I don't know how to explain it.|It's hard to say how I get the system running so quickly because I just get going I don't know how to explain it.	7,10|16	7,11|16,17	substitution|deletion
+show_2T3Pjyw6MJEwPE9uixrYak-7M5817WSsGaldlQfZptcyf.wav	I know now how extremely lucky that truly is what a blessing Not only was I going to be a mom but besides nausea all my other symptoms went away.|I know now how incredibly lucky I really was, what a blessing Not only was I going to be a mom but besides nausea all my other symptoms went away.	I know now how incredibly lucky I really was, what a blessing Not only was I going to be a mom but besides nausea all my other symptoms went away.|I know now how incredibly lucky I really was, what a blessing Not only was I going to be a mom but except for nausea all my other symptoms went away.	4,8|22	4,8|22,23	substitution|substitution
+show_1C5B3zuyd67j7v9XRKNb2L-4Az7OglwsgYR94ikvOZdCf.wav	Past something and I have so many things that I have planned for this podcast, but first before we get into any of that I'm going to introduce myself.|Past something and I have so many things that I have planned to talk about today, but first before we get into any of that I'm going to introduce myself.	Past something and I have so many things that I have planned to talk about today, but first before we get into any of that I'm going to introduce myself.|Past something and I have so many things that I have planned to talk about today, but first before we get into any of that I would like to introduce myself.	12,14|24,25	12,15|25,27	substitution|substitution
+show_1c8f0MS5LcfbSvwexFC9mn-1MY1u1xOyiFAWXGRqyPJj7.wav	You know with hesitation everything is counted to the T and says if every drink is measured, how are you going to give a regular an honest poor?|You know with hesitation everything is counted to the T and says if you're calculating the nutritional information, how are you going to give a regular an honest poor?	You know with hesitation everything is counted to the T and says if you're calculating the nutritional information, how are you going to give a regular an honest poor?|You know with hesitation everything is counted to the T and says if you're calculating the nutritional information, how are you going to provide the same service to an honest poor?	13,16|22,24	13,17|23,27	substitution|substitution
\ No newline at end of file
diff --git a/models/voicecraft.py b/models/voicecraft.py
index 8d83729..e3852c4 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -409,6 +409,10 @@ class VoiceCraft(nn.Module):
                 .expand(-1, self.args.nhead, -1, -1)
                 .reshape(bsz * self.args.nhead, 1, src_len)
             )
+            # Check shapes and resize or broadcast as necessary
+            if xy_attn_mask.shape != _xy_padding_mask.shape:
+                # Assuming _xy_padding_mask has the correct shape and xy_attn_mask needs adjustment
+                xy_attn_mask = xy_attn_mask.expand_as(_xy_padding_mask)  # Example approach
             xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
 
             new_attn_mask = torch.zeros_like(xy_attn_mask)

From 0face202d8351be9e29c2daff6c38a970b580946 Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Fri, 5 Apr 2024 13:33:03 -0700
Subject: [PATCH 22/40] fixed masking to be compatible with torch 2.1.0; fix
 crop ops

---
 models/voicecraft.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index e3852c4..ab3cf37 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -409,10 +409,10 @@ class VoiceCraft(nn.Module):
                 .expand(-1, self.args.nhead, -1, -1)
                 .reshape(bsz * self.args.nhead, 1, src_len)
             )
-            # Check shapes and resize or broadcast as necessary
+            # Check shapes and resize+broadcast as necessary
             if xy_attn_mask.shape != _xy_padding_mask.shape:
-                # Assuming _xy_padding_mask has the correct shape and xy_attn_mask needs adjustment
-                xy_attn_mask = xy_attn_mask.expand_as(_xy_padding_mask)  # Example approach
+                assert xy_attn_mask.ndim + 1 == _xy_padding_mask.ndim, f"xy_attn_mask.shape: {xy_attn_mask.shape}, _xy_padding_mask: {_xy_padding_mask.shape}"
+                xy_attn_mask = xy_attn_mask.unsqueeze(0).repeat(_xy_padding_mask.shape[0], 1, 1)  # Example approach
             xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
 
             new_attn_mask = torch.zeros_like(xy_attn_mask)
@@ -459,7 +459,7 @@ class VoiceCraft(nn.Module):
         """
         x, x_lens, y, y_lens = batch["x"], batch["x_lens"], batch["y"], batch["y_lens"]
         x = x[:, :x_lens.max()] # this deal with gradient accumulation, where x_lens.max() might not be longer than the length of the current slice of x
-        y = y[:, :y_lens.max()]
+        y = y[:, :, :y_lens.max()]
         assert x.ndim == 2, x.shape
         assert x_lens.ndim == 1, x_lens.shape
         assert y.ndim == 3 and y.shape[1] == self.args.n_codebooks, y.shape

From 142772c3df64ebbeb12e852b33330b13f763ac86 Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Fri, 5 Apr 2024 16:42:59 -0700
Subject: [PATCH 23/40] upload TTS finetuned 330M model

---
 README.md           | 33 ++++++++++++++++++---------------
 inference_tts.ipynb |  6 +++---
 2 files changed, 21 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 59cc368..8b7f984 100644
--- a/README.md
+++ b/README.md
@@ -7,21 +7,7 @@ VoiceCraft is a token infilling neural codec language model, that achieves state
 
 To clone or edit an unseen voice, VoiceCraft needs only a few seconds of reference.
 
-## News
-:star: 03/28/2024: Model weights are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
-
-## TODO
-- [x] Codebase upload
-- [x] Environment setup
-- [x] Inference demo for speech editing and TTS
-- [x] Training guidance
-- [x] RealEdit dataset and training manifest
-- [x] Model weights (both 330M and 830M, the former seems to be just as good)
-- [x] Write colab notebooks for better hands-on experience
-- [ ] HuggingFace Spaces demo
-- [ ] Better guidance on training/finetuning
-
-## How to run TTS inference
+## How to run inference
 There are three ways:
 
 1. with Google Colab. see [quickstart colab](#quickstart-colab)
@@ -32,6 +18,23 @@ When you are inside the docker image or you have installed all dependencies, Che
 
 If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
 
+## News
+:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
+
+:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight, the model outperforms giga830M on TTS. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data)
+
+## TODO
+- [x] Codebase upload
+- [x] Environment setup
+- [x] Inference demo for speech editing and TTS
+- [x] Training guidance
+- [x] RealEdit dataset and training manifest
+- [x] Model weights (giga330M.pth, giga830M.pth, and gigaHalfLibri330M_TTSEnhanced_max16s.pth)
+- [x] Write colab notebooks for better hands-on experience
+- [ ] HuggingFace Spaces demo
+- [ ] Better guidance on training/finetuning
+
+
 ## QuickStart Colab
 
 :star: To try out speech editing or TTS Inference with VoiceCraft, the simplest way is using Google Colab.
diff --git a/inference_tts.ipynb b/inference_tts.ipynb
index 3cce38d..f18270c 100644
--- a/inference_tts.ipynb
+++ b/inference_tts.ipynb
@@ -63,7 +63,7 @@
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "from models import voicecraft\n",
     "#import models.voicecraft as voicecraft\n",
-    "voicecraft_name=\"giga830M.pth\" # or giga330M.pth\n",
+    "voicecraft_name=\"gigaHalfLibri330M_TTSEnhanced_max16s.pth\" # or giga330M.pth, giga830M.pth\n",
     "ckpt_fn =f\"./pretrained_models/{voicecraft_name}\"\n",
     "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
     "if not os.path.exists(ckpt_fn):\n",
@@ -141,14 +141,14 @@
     "codec_audio_sr = 16000\n",
     "codec_sr = 50\n",
     "top_k = 0\n",
-    "top_p = 0.8\n",
+    "top_p = 0.9 # can also try 0.8, but 0.9 seems to work better\n",
     "temperature = 1\n",
     "silence_tokens=[1388,1898,131]\n",
     "kvcache = 1 # NOTE if OOM, change this to 0, or try the 330M model\n",
     "\n",
     "# NOTE adjust the below three arguments if the generation is not as good\n",
     "stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n",
-    "sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
+    "sample_batch_size = 2 # for gigaHalfLibri330M_TTSEnhanced_max16s.pth, 1 or 2 should be fine since the model is trained to do TTS, for the other two models, might need a higher number. NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
     "seed = 1 # change seed if you are still unhappy with the result\n",
     "\n",
     "def seed_everything(seed):\n",

From 1e79d9032e8e0ed3fa338fd78ea8bfff599e91dc Mon Sep 17 00:00:00 2001
From: Forkoz <59298527+Ph0rk0z@users.noreply.github.com>
Date: Sat, 6 Apr 2024 00:05:06 +0000
Subject: [PATCH 24/40] Empty cuda cache between inferences

---
 inference_tts_scale.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/inference_tts_scale.py b/inference_tts_scale.py
index 2ebb78c..9915b22 100644
--- a/inference_tts_scale.py
+++ b/inference_tts_scale.py
@@ -98,7 +98,9 @@ def inference_one_sample(model, model_args, phn2num, text_tokenizer, audio_token
     gen_sample = audio_tokenizer.decode(
         [(gen_frames, None)]
     )
-
+    #Empty cuda cache between runs
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
     # return
     return concat_sample, gen_sample
 
@@ -187,4 +189,4 @@ if __name__ == "__main__":
         seg_save_fn_concat = f"{args.output_dir}/concat_{new_audio_fn[:-4]}_{i}_seed{args.seed}.wav"        
 
         torchaudio.save(seg_save_fn_gen, gen_audio, args.codec_audio_sr)
-        torchaudio.save(seg_save_fn_concat, concated_audio, args.codec_audio_sr)
\ No newline at end of file
+        torchaudio.save(seg_save_fn_concat, concated_audio, args.codec_audio_sr)

From 9ce26becea9f198ce9e74aab24d66f9e6e496555 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Sun, 7 Apr 2024 00:21:28 +0300
Subject: [PATCH 25/40] gradio: added giga330M_TTSEnhanced model, changed
 default top_p to 0.9

---
 gradio_app.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 32b3e72..564af54 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -77,6 +77,9 @@ class WhisperxModel:
 def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
     global transcribe_model, align_model, voicecraft_model
 
+    if voicecraft_model_name == "giga330M_TTSEnhanced":
+        voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
+
     if alignment_model_name is not None:
         align_model = WhisperxAlignModel()
 
@@ -433,7 +436,8 @@ with gr.Blocks() as app:
         with gr.Column(scale=5):
             with gr.Accordion("Select models", open=False) as models_selector:
                 with gr.Row():
-                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
+                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
+                                                       choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
                     whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
                     whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
                                                     choices=[None, "base.en", "small.en", "medium.en", "large"])
@@ -498,13 +502,15 @@ with gr.Blocks() as app:
             stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
                                        info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
             sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
-                                          info="The higher the number, the faster the output will be. Under the hood, the model will generate this many samples and choose the shortest one")
+                                          info="The higher the number, the faster the output will be. "
+                                               "Under the hood, the model will generate this many samples and choose the shortest one. "
+                                               "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
             seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
             kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
                                 info="set to 0 to use less VRAM, but with slower inference")
             left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
             right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
-            top_p = gr.Number(label="top_p", value=0.8, info="0.8 is a good value, 0.9 is also good")
+            top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
             temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
             top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
             codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')

From 9148e6020e265b81f8f946d4e1f86e3310fdb7ce Mon Sep 17 00:00:00 2001
From: pgosar <pgosarsteam@gmail.com>
Date: Sat, 6 Apr 2024 16:42:50 -0500
Subject: [PATCH 26/40] remove trailing whitespace

---
 gradio_app.py | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/gradio_app.py b/gradio_app.py
index 564af54..80d96ec 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -90,7 +90,7 @@ def load_models(whisper_backend_name, whisper_model_name, alignment_model_name,
             if align_model is None:
                 raise gr.Error("Align model required for whisperx backend")
             transcribe_model = WhisperxModel(whisper_model_name, align_model)
-    
+
     voicecraft_name = f"{voicecraft_model_name}.pth"
     ckpt_fn = f"./pretrained_models/{voicecraft_name}"
     encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
@@ -132,7 +132,7 @@ def transcribe(seed, audio_path):
     if transcribe_model is None:
         raise gr.Error("Transcription model not loaded")
     seed_everything(seed)
-    
+
     segments = transcribe_model.transcribe(audio_path)
     state = get_transcribe_state(segments)
 
@@ -234,7 +234,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
         if mode != "Edit":
             from inference_tts_scale import inference_one_sample
 
-            if smart_transcript:                
+            if smart_transcript:
                 target_transcript = ""
                 for word in transcribe_state["words_info"]:
                     if word["end"] < prompt_end_time:
@@ -281,7 +281,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
             morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
             mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
             mask_interval = torch.LongTensor(mask_interval)
-            
+
             _, gen_audio = inference_one_sample(voicecraft_model["model"],
                                                 voicecraft_model["ckpt"]["config"],
                                                 voicecraft_model["ckpt"]["phn2num"],
@@ -300,12 +300,12 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
         output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
         sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
         return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
-    
-    
+
+
 def update_input_audio(audio_path):
     if audio_path is None:
         return 0, 0, 0
-    
+
     info = torchaudio.info(audio_path)
     max_time = round(info.num_frames / info.sample_rate, 2)
     return [
@@ -314,7 +314,7 @@ def update_input_audio(audio_path):
         gr.Slider(maximum=max_time, value=max_time),
     ]
 
-    
+
 def change_mode(mode):
     tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
     return [
@@ -416,7 +416,7 @@ demo_words_info = [
 def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
     if transcript not in all_demo_texts:
         return transcript, edit_from_word, edit_to_word
-    
+
     replace_half = edit_word_mode == "Replace half"
     change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
     change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
@@ -456,7 +456,7 @@ with gr.Blocks() as app:
 
                 transcribe_btn = gr.Button(value="Transcribe")
                 align_btn = gr.Button(value="Align")
-            
+
         with gr.Column(scale=3):
             with gr.Group():
                 transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
@@ -471,7 +471,7 @@ with gr.Blocks() as app:
                                           info="Split text into parts and run TTS for each part.", visible=False)
                     edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
                                               info="What to do with first and last word", visible=False)
-                
+
                 with gr.Group() as tts_mode_controls:
                     prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
                     prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
@@ -517,11 +517,11 @@ with gr.Blocks() as app:
             codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
             silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
-    
+
     audio_tensors = gr.State()
     transcribe_state = gr.State(value={"words_info": demo_words_info})
 
-    
+
     mode.change(fn=update_demo,
                 inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                 outputs=[transcript, edit_from_word, edit_to_word])
@@ -531,11 +531,11 @@ with gr.Blocks() as app:
     smart_transcript.change(fn=update_demo,
                             inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                             outputs=[transcript, edit_from_word, edit_to_word])
-    
+
     load_models_btn.click(fn=load_models,
                           inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
                           outputs=[models_selector])
-    
+
     input_audio.upload(fn=update_input_audio,
                        inputs=[input_audio],
                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
@@ -564,7 +564,7 @@ with gr.Blocks() as app:
                       split_text, sentence_selector, audio_tensors
                   ],
                   outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
-    
+
     sentence_selector.change(fn=load_sentence,
                              inputs=[sentence_selector, codec_audio_sr, audio_tensors],
                              outputs=[sentence_audio])
@@ -580,7 +580,7 @@ with gr.Blocks() as app:
                         split_text, sentence_selector, audio_tensors
                     ],
                     outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
-    
+
     prompt_to_word.change(fn=update_bound_word,
                           inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
                           outputs=[prompt_end_time])

From a11e1b8f3c5053b78014dcb9d1e9ac926418a9ed Mon Sep 17 00:00:00 2001
From: Niels <niels.rogge1@gmail.com>
Date: Sun, 7 Apr 2024 19:38:45 +0200
Subject: [PATCH 27/40] Add HF

---
 models/voicecraft.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index ab3cf37..f090c66 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -18,6 +18,9 @@ from .modules.transformer import (
 )
 from .codebooks_patterns import DelayedPatternProvider
 
+from huggingface_hub import PyTorchModelHubMixin
+
+
 def top_k_top_p_filtering(
     logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
 ):
@@ -82,7 +85,7 @@ def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
 
 
 
-class VoiceCraft(nn.Module):
+class VoiceCraft(nn.Module, PyTorchModelHubMixin):
     def __init__(self, args):
         super().__init__()
         self.args = copy.copy(args)

From 92b283c741d4110ff989e679405d805128299331 Mon Sep 17 00:00:00 2001
From: Niels <niels.rogge1@gmail.com>
Date: Sun, 7 Apr 2024 20:17:52 +0200
Subject: [PATCH 28/40] Add class

---
 models/voicecraft.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index f090c66..8f87264 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -85,7 +85,7 @@ def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
 
 
 
-class VoiceCraft(nn.Module, PyTorchModelHubMixin):
+class VoiceCraft(nn.Module):
     def __init__(self, args):
         super().__init__()
         self.args = copy.copy(args)
@@ -1410,4 +1410,9 @@ class VoiceCraft(nn.Module, PyTorchModelHubMixin):
             res = res - int(self.args.n_special)
             flatten_gen = flatten_gen - int(self.args.n_special)
 
-        return res, flatten_gen[0].unsqueeze(0)
\ No newline at end of file
+        return res, flatten_gen[0].unsqueeze(0)
+    
+
+class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin):
+    def __init__(self, config: dict):
+        super().__init__(config)
\ No newline at end of file

From 8ec653db9d5b05d186e7ccc28f9737499d9bbbdf Mon Sep 17 00:00:00 2001
From: Niels <niels.rogge1@gmail.com>
Date: Sun, 7 Apr 2024 20:21:39 +0200
Subject: [PATCH 29/40] Add class

---
 models/voicecraft.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index 8f87264..8ea85ad 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -18,6 +18,7 @@ from .modules.transformer import (
 )
 from .codebooks_patterns import DelayedPatternProvider
 
+from argparse import Namespace
 from huggingface_hub import PyTorchModelHubMixin
 
 
@@ -1415,4 +1416,5 @@ class VoiceCraft(nn.Module):
 
 class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin):
     def __init__(self, config: dict):
-        super().__init__(config)
\ No newline at end of file
+        args = Namespace(**config)
+        super().__init__(args)
\ No newline at end of file

From c44df99d499679c8e51f5ac0a7681a40ae76af50 Mon Sep 17 00:00:00 2001
From: Niels <niels.rogge1@gmail.com>
Date: Sun, 7 Apr 2024 20:34:27 +0200
Subject: [PATCH 30/40] Add tags

---
 models/voicecraft.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index 8ea85ad..ee8e5a7 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -1414,7 +1414,9 @@ class VoiceCraft(nn.Module):
         return res, flatten_gen[0].unsqueeze(0)
     
 
-class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin):
+class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin,
+                   repo_url="https://github.com/jasonppy/VoiceCraft",
+                   tags=["Text-to-Speech, VoiceCraft"]):
     def __init__(self, config: dict):
         args = Namespace(**config)
         super().__init__(args)
\ No newline at end of file

From 0f79429b0d82c41190e8ad03bbe5b20112236417 Mon Sep 17 00:00:00 2001
From: Niels <niels.rogge1@gmail.com>
Date: Sun, 7 Apr 2024 20:36:35 +0200
Subject: [PATCH 31/40] Add tags

---
 models/voicecraft.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/models/voicecraft.py b/models/voicecraft.py
index ee8e5a7..3c22105 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -1416,7 +1416,7 @@ class VoiceCraft(nn.Module):
 
 class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin,
                    repo_url="https://github.com/jasonppy/VoiceCraft",
-                   tags=["Text-to-Speech, VoiceCraft"]):
+                   tags=["Text-to-Speech", "VoiceCraft"]):
     def __init__(self, config: dict):
         args = Namespace(**config)
         super().__init__(args)
\ No newline at end of file

From 445376ffcc88dfa77b98fa746e5dbf6e91c18d7a Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Mon, 8 Apr 2024 12:04:22 -0700
Subject: [PATCH 32/40] better instruction

---
 Dockerfile            |  2 ++
 README.md             | 22 +++++++-------
 z_scripts/e830M_ft.sh | 69 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 83 insertions(+), 10 deletions(-)
 create mode 100644 z_scripts/e830M_ft.sh

diff --git a/Dockerfile b/Dockerfile
index 561b4ec..3fbe052 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,8 @@ RUN apt-get update && apt-get install -y git-core ffmpeg espeak-ng && \
 RUN conda update -y -n base -c conda-forge conda && \
     conda create -y -n voicecraft python=3.9.16 && \
     conda run -n voicecraft conda install -y -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068 && \
+    conda run -n voicecraft mfa model download dictionary english_us_arpa && \
+    conda run -n voicecraft mfa model download acoustic english_us_arpa && \
     conda run -n voicecraft pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft && \
     conda run -n voicecraft pip install xformers==0.0.22 && \
     conda run -n voicecraft pip install torch==2.0.1 && \
diff --git a/README.md b/README.md
index 8b7f984..e292536 100644
--- a/README.md
+++ b/README.md
@@ -30,9 +30,11 @@ If you want to do model development such as training/finetuning, I recommend fol
 - [x] Training guidance
 - [x] RealEdit dataset and training manifest
 - [x] Model weights (giga330M.pth, giga830M.pth, and gigaHalfLibri330M_TTSEnhanced_max16s.pth)
+- [x] Better guidance on training/finetuning
 - [x] Write colab notebooks for better hands-on experience
 - [ ] HuggingFace Spaces demo
-- [ ] Better guidance on training/finetuning
+- [ ] Command line
+- [ ] Improve efficiency
 
 
 ## QuickStart Colab
@@ -95,6 +97,9 @@ pip install datasets==2.16.0
 pip install torchmetrics==0.11.1
 # install MFA for getting forced-alignment, this could take a few minutes
 conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068
+# install MFA english dictionary and model
+mfa model download dictionary english_us_arpa
+mfa model download acoustic english_us_arpa
 # conda install pocl # above gives an warning for installing pocl, not sure if really need this
 
 # to run ipynb
@@ -145,19 +150,16 @@ cd ./z_scripts
 bash e830M.sh
 ```
 
+It's the same procedure to prepare your own custom dataset. Make sure that if 
+
+## Finetuning
+You also need to do step 1-4 as Training, and I recommend to use AdamW for optimization if you finetune a pretrained model for better stability. checkout script `/home/pyp/VoiceCraft/z_scripts/e830M_ft.sh`.
+
+If your dataset introduce new phonemes (which is very likely) that doesn't exist in the giga checkpoint, make sure you combine the original phonemes with the phoneme from your data when construction vocab. And you need to adjust `--text_vocab_size` and `--text_pad_token` so that the former is bigger than or equal to you vocab size, and the latter has the same value as `--text_vocab_size` (i.e. `--text_pad_token` is always the last token). Also since the text embedding are now of a different size, make sure you modify the weights loading part so that I won't crash (you could skip loading `text_embedding` or only load the existing part, and randomly initialize the new)
 
 ## License
 The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License.
 
-<!-- How to use g2p to convert english text into IPA phoneme sequence
-first install it with `pip install g2p`
-```python
-from g2p import make_g2p
-transducer = make_g2p('eng', 'eng-ipa')
-transducer("hello").output_string
-# it will output: 'hʌloʊ'
-``` -->
-
 ## Acknowledgement
 We thank Feiteng for his [VALL-E reproduction](https://github.com/lifeiteng/vall-e), and we thank audiocraft team for open-sourcing [encodec](https://github.com/facebookresearch/audiocraft).
 
diff --git a/z_scripts/e830M_ft.sh b/z_scripts/e830M_ft.sh
new file mode 100644
index 0000000..9226e5f
--- /dev/null
+++ b/z_scripts/e830M_ft.sh
@@ -0,0 +1,69 @@
+#!/bin/bash
+source ~/miniconda3/etc/profile.d/conda.sh
+conda activate voicecraft
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+export WORLD_SIZE=4
+
+dataset=gigaspeech
+mkdir -p ./logs/${dataset}
+
+exp_root="path/to/store/exp_results"
+exp_name=e830M_ft
+dataset_dir="path/to/stored_extracted_codes_and_phonemes/xl" # xs if you only extracted xs in previous step
+encodec_codes_folder_name="encodec_16khz_4codebooks"
+load_model_from="/home/pyp/VoiceCraft/pretrained_models/giga830M.pth"
+
+# export CUDA_LAUNCH_BLOCKING=1 # for debugging
+
+torchrun --nnodes=1 --rdzv-backend=c10d --rdzv-endpoint=localhost:41977 --nproc_per_node=${WORLD_SIZE} \
+../main.py \
+--load_model_from ${load_model_from} \
+--reduced_eog 1 \
+--drop_long 1 \
+--eos 2051 \
+--n_special 4 \
+--pad_x 0 \
+--codebook_weight "[3,1,1,1]" \
+--encodec_sr 50 \
+--num_steps 500000 \
+--lr 0.00001 \
+--warmup_fraction 0.1 \
+--optimizer_name "AdamW" \
+--d_model 2048 \
+--audio_embedding_dim 2048 \
+--nhead 16 \
+--num_decoder_layers 16 \
+--max_num_tokens 20000 \
+--gradient_accumulation_steps 20 \
+--val_max_num_tokens 6000 \
+--num_buckets 6 \
+--audio_max_length 20 \
+--audio_min_length 2 \
+--text_max_length 400 \
+--text_min_length 10 \
+--mask_len_min 1 \
+--mask_len_max 600 \
+--tb_write_every_n_steps 10 \
+--print_every_n_steps 400 \
+--val_every_n_steps 1600 \
+--text_vocab_size 100 \
+--text_pad_token 100 \
+--phn_folder_name "phonemes" \
+--manifest_name "manifest" \
+--encodec_folder_name ${encodec_codes_folder_name} \
+--audio_vocab_size 2048 \
+--empty_token 2048 \
+--eog 2049 \
+--audio_pad_token 2050 \
+--n_codebooks 4 \
+--max_n_spans 3 \
+--shuffle_mask_embedding 0 \
+--mask_sample_dist poisson1 \
+--max_mask_portion 0.9 \
+--min_gap 5 \
+--num_workers 8 \
+--dynamic_batching 1 \
+--dataset $dataset \
+--exp_dir "${exp_root}/${dataset}/${exp_name}" \
+--dataset_dir ${dataset_dir}
+#  >> ./logs/${dataset}/${exp_name}.log 2>&1
\ No newline at end of file

From d363743fbd4fe58d54ffe218113a14c999d13a9c Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Mon, 8 Apr 2024 15:08:59 -0700
Subject: [PATCH 33/40] hf model

---
 README.md            | 1 +
 models/voicecraft.py | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 8b7f984..a464833 100644
--- a/README.md
+++ b/README.md
@@ -95,6 +95,7 @@ pip install datasets==2.16.0
 pip install torchmetrics==0.11.1
 # install MFA for getting forced-alignment, this could take a few minutes
 conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=5.5.1068
+pip install huggingface_hub
 # conda install pocl # above gives an warning for installing pocl, not sure if really need this
 
 # to run ipynb
diff --git a/models/voicecraft.py b/models/voicecraft.py
index 3c22105..cda380a 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -1414,9 +1414,9 @@ class VoiceCraft(nn.Module):
         return res, flatten_gen[0].unsqueeze(0)
     
 
-class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin,
-                   repo_url="https://github.com/jasonppy/VoiceCraft",
-                   tags=["Text-to-Speech", "VoiceCraft"]):
+class VoiceCraftHF(VoiceCraft, PyTorchModelHubMixin):
+    repo_url="https://github.com/jasonppy/VoiceCraft",
+    tags=["Text-to-Speech", "VoiceCraft"]
     def __init__(self, config: dict):
         args = Namespace(**config)
         super().__init__(args)
\ No newline at end of file

From 778db3443d51dd52ca40de13406422c2519dd052 Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Mon, 8 Apr 2024 15:12:51 -0700
Subject: [PATCH 34/40] finetune 830M

---
 README.md             | 4 ++--
 z_scripts/e830M_ft.sh | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 9e37326..81b6c1c 100644
--- a/README.md
+++ b/README.md
@@ -100,7 +100,7 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 # install MFA english dictionary and model
 mfa model download dictionary english_us_arpa
 mfa model download acoustic english_us_arpa
-pip install huggingface_hub
+# pip install huggingface_hub
 # conda install pocl # above gives an warning for installing pocl, not sure if really need this
 
 # to run ipynb
@@ -154,7 +154,7 @@ bash e830M.sh
 It's the same procedure to prepare your own custom dataset. Make sure that if 
 
 ## Finetuning
-You also need to do step 1-4 as Training, and I recommend to use AdamW for optimization if you finetune a pretrained model for better stability. checkout script `/home/pyp/VoiceCraft/z_scripts/e830M_ft.sh`.
+You also need to do step 1-4 as Training, and I recommend to use AdamW for optimization if you finetune a pretrained model for better stability. checkout script `./z_scripts/e830M_ft.sh`.
 
 If your dataset introduce new phonemes (which is very likely) that doesn't exist in the giga checkpoint, make sure you combine the original phonemes with the phoneme from your data when construction vocab. And you need to adjust `--text_vocab_size` and `--text_pad_token` so that the former is bigger than or equal to you vocab size, and the latter has the same value as `--text_vocab_size` (i.e. `--text_pad_token` is always the last token). Also since the text embedding are now of a different size, make sure you modify the weights loading part so that I won't crash (you could skip loading `text_embedding` or only load the existing part, and randomly initialize the new)
 
diff --git a/z_scripts/e830M_ft.sh b/z_scripts/e830M_ft.sh
index 9226e5f..83fefbb 100644
--- a/z_scripts/e830M_ft.sh
+++ b/z_scripts/e830M_ft.sh
@@ -11,7 +11,7 @@ exp_root="path/to/store/exp_results"
 exp_name=e830M_ft
 dataset_dir="path/to/stored_extracted_codes_and_phonemes/xl" # xs if you only extracted xs in previous step
 encodec_codes_folder_name="encodec_16khz_4codebooks"
-load_model_from="/home/pyp/VoiceCraft/pretrained_models/giga830M.pth"
+load_model_from="./pretrained_models/giga830M.pth"
 
 # export CUDA_LAUNCH_BLOCKING=1 # for debugging
 
@@ -34,7 +34,7 @@ torchrun --nnodes=1 --rdzv-backend=c10d --rdzv-endpoint=localhost:41977 --nproc_
 --nhead 16 \
 --num_decoder_layers 16 \
 --max_num_tokens 20000 \
---gradient_accumulation_steps 20 \
+--gradient_accumulation_steps 12 \
 --val_max_num_tokens 6000 \
 --num_buckets 6 \
 --audio_max_length 20 \

From 17061636f274f7d813763caa34510a7c114fb12f Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Tue, 9 Apr 2024 11:33:58 -0700
Subject: [PATCH 35/40] avoid zero size batch caused by grad_accumulation

---
 inference_tts.ipynb  | 44 +++++++++++++++++++++++++++++++-------------
 models/voicecraft.py |  2 ++
 steps/trainer.py     |  2 ++
 3 files changed, 35 insertions(+), 13 deletions(-)

diff --git a/inference_tts.ipynb b/inference_tts.ipynb
index f18270c..e54368c 100644
--- a/inference_tts.ipynb
+++ b/inference_tts.ipynb
@@ -32,6 +32,7 @@
     "import torchaudio\n",
     "import numpy as np\n",
     "import random\n",
+    "from argparse import Namespace\n",
     "\n",
     "from data.tokenizer import (\n",
     "    AudioTokenizer,\n",
@@ -45,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# install MFA models and dictionaries if you haven't done so already\n",
+    "# install MFA models and dictionaries if you haven't done so already, already done in the dockerfile or envrionment setup\n",
     "!source ~/.bashrc && \\\n",
     "    conda activate voicecraft && \\\n",
     "    mfa model download dictionary english_us_arpa && \\\n",
@@ -61,28 +62,38 @@
     "# load model, encodec, and phn2num\n",
     "# # load model, tokenizer, and other necessary files\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
+    "\n",
+    "# the old way of loading the model\n",
     "from models import voicecraft\n",
-    "#import models.voicecraft as voicecraft\n",
-    "voicecraft_name=\"gigaHalfLibri330M_TTSEnhanced_max16s.pth\" # or giga330M.pth, giga830M.pth\n",
     "ckpt_fn =f\"./pretrained_models/{voicecraft_name}\"\n",
-    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
     "if not os.path.exists(ckpt_fn):\n",
     "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\\?download\\=true\")\n",
     "    os.system(f\"mv {voicecraft_name}\\?download\\=true ./pretrained_models/{voicecraft_name}\")\n",
-    "if not os.path.exists(encodec_fn):\n",
-    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
-    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
-    "\n",
     "ckpt = torch.load(ckpt_fn, map_location=\"cpu\")\n",
     "model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
     "model.load_state_dict(ckpt[\"model\"])\n",
+    "phn2num = ckpt['phn2num']\n",
+    "config = vars(ckpt['config'])\n",
     "model.to(device)\n",
     "model.eval()\n",
     "\n",
-    "phn2num = ckpt['phn2num']\n",
+    "# # the new way of loading the model, with huggingface, this doesn't work yet\n",
+    "# from models.voicecraft import VoiceCraftHF\n",
+    "# model = VoiceCraftHF.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n",
+    "# phn2num = model.args.phn2num # or model.args['phn2num']?\n",
+    "# config = model.config\n",
+    "# model.to(device)\n",
+    "# model.eval()\n",
     "\n",
-    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
-    "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n"
+    "\n",
+    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
+    "if not os.path.exists(encodec_fn):\n",
+    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
+    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
+    "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n",
+    "\n",
+    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n"
    ]
   },
   {
@@ -148,7 +159,7 @@
     "\n",
     "# NOTE adjust the below three arguments if the generation is not as good\n",
     "stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n",
-    "sample_batch_size = 2 # for gigaHalfLibri330M_TTSEnhanced_max16s.pth, 1 or 2 should be fine since the model is trained to do TTS, for the other two models, might need a higher number. NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
+    "sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
     "seed = 1 # change seed if you are still unhappy with the result\n",
     "\n",
     "def seed_everything(seed):\n",
@@ -163,7 +174,7 @@
     "\n",
     "decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens, \"sample_batch_size\": sample_batch_size}\n",
     "from inference_tts_scale import inference_one_sample\n",
-    "concated_audio, gen_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
+    "concated_audio, gen_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
     "        \n",
     "# save segments for comparison\n",
     "concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu()\n",
@@ -190,6 +201,13 @@
     "\n",
     "# you are might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/models/voicecraft.py b/models/voicecraft.py
index cda380a..19b0a25 100644
--- a/models/voicecraft.py
+++ b/models/voicecraft.py
@@ -462,6 +462,8 @@ class VoiceCraft(nn.Module):
             before padding.
         """
         x, x_lens, y, y_lens = batch["x"], batch["x_lens"], batch["y"], batch["y_lens"]
+        if len(x) == 0:
+            return None
         x = x[:, :x_lens.max()] # this deal with gradient accumulation, where x_lens.max() might not be longer than the length of the current slice of x
         y = y[:, :, :y_lens.max()]
         assert x.ndim == 2, x.shape
diff --git a/steps/trainer.py b/steps/trainer.py
index 0cce5de..b312258 100644
--- a/steps/trainer.py
+++ b/steps/trainer.py
@@ -90,6 +90,8 @@ class Trainer:
                     cur_batch = {key: batch[key][cur_ind] for key in batch}
                     with torch.cuda.amp.autocast(dtype=torch.float16 if self.args.precision=="float16" else torch.float32):
                         out = self.model(cur_batch)
+                        if out == None:
+                            continue
 
                     record_loss = out['loss'].detach().to(self.rank) 
                     top10acc = out['top10acc'].to(self.rank)

From 623550f8081c6851d9ff59e0f3986affb0752bfa Mon Sep 17 00:00:00 2001
From: Rumah <93970226+Sewlell@users.noreply.github.com>
Date: Thu, 11 Apr 2024 11:43:11 +0800
Subject: [PATCH 36/40] April 11 Update

---
 voicecraft.ipynb | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/voicecraft.ipynb b/voicecraft.ipynb
index f8b9c85..78c816a 100644
--- a/voicecraft.ipynb
+++ b/voicecraft.ipynb
@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyPEhMt0mIcJv2BbaCwogF07",
+      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -66,7 +66,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Let it restarted, it won't let your entire installation be gone."
+        "# Let it restarted, it won't let your entire installation be aborted."
       ],
       "metadata": {
         "id": "jNuzjrtmv2n1"
@@ -79,27 +79,32 @@
         "\n",
         "***You will get JSON warning if you move anything beside `sample_batch_size`, `stop_repetition` and `seed`.*** Which for most advanced setting, `kvache` and `temperature` unable to set in different value.\n",
         "\n",
-        "You will get fp16 compatibility issue if you set `whisper backend` to `whisperX`, for whatever reason, setting `forced alignment model` to `whisperX` doesn't do anything.\n",
-        "\n",
-        "You will download a .file File when you download the Output Audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
+        "You will download a .file File when you download the output audio for some reason. You will need to **convert the file from .snd to .wav/.mp3 manually**. Or if you enable showing file type in the name in Windows or wherever you are, change the file name to \"xxx.wav\" or \"xxx.mp3\". (know the solution? pull request my repository)\n",
         "\n",
         "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
-        "\n",
-        "# **To those who want to voice cloning**\n",
-        "![Screenshot (438).png](data:image/png;base64,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)\n",
-        "\n",
-        "Don't make your input audio too long like in the screenshot, 20s-30s is fine. Or else it will raise up JSON issue. This one is due to how VoiceCraft worked so probably unfixable. It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short."
+        "* Nevermind, I have observed some weird usage on Colab's GPU Memory Monitor. It can spike up to 13.5GB VRAM even in WhisperX mode. (April 11)"
       ],
       "metadata": {
-        "id": "qQ-And_w2vJV"
+        "id": "AnqGEwZ4NxtJ"
       }
     },
     {
       "cell_type": "markdown",
       "source": [
-        "My tip on voice cloning is just \"get a good dataset\" that contain plausible amount of variety of tones. I guess that's it, you could always try experimenting with other voice that are hard to be clone.\n",
+        "Don't make your `prompt end time` too long, 6-9s is fine. Or else it will **either raise up JSON issue or cut off your generated audio**. This one is due to how VoiceCraft worked (so probably unfixable). It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short.\n",
         "\n",
-        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-10s on 6s-8s `prompt end time`.\n",
+        "Your total audio length (`prompt end time` + add-up audio) must not exceed 16 or 17s."
+      ],
+      "metadata": {
+        "id": "dE0W76cMN3Si"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For voice cloning, I suggest you to probably have a monotone input to feed the voice cloning. Of course you can always try input that have tons of tone variety, but I find that as per April 11 Update, it's much more easy to replicate in monotone rather than audio that have laugh, scream, crying inside.\n",
+        "\n",
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`.\n",
         "\n",
         "I haven't test the Edit mode yet as those are not my focus, but you can try it."
       ],

From 6f0aa2db577aa1b77ee1a87d50ea447a8999932f Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 11 Apr 2024 16:28:52 +0500
Subject: [PATCH 37/40] essential gradio app args added, colab notebook fix

---
 README.md                                     |  16 +-
 gradio_app.py                                 | 313 +++++++++---------
 ...aft.ipynb => voicecraft-gradio-colab.ipynb | 106 +++---
 3 files changed, 222 insertions(+), 213 deletions(-)
 rename voicecraft.ipynb => voicecraft-gradio-colab.ipynb (84%)

diff --git a/README.md b/README.md
index 3bad691..a9b06d7 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,3 @@
-# VoiceCraft Gradio Colab
-
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb)
-
-Made for those who lacked a dedicated GPU and those who wanted [the friendly GUI by @zuev-stepan](https://github.com/zuev-stepan/VoiceCraft-gradio). Potato programmer brain here so all code credits to @jasonppy, @zuev-stepan and others who contributed in their code. 
-
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
 [Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
 
@@ -105,10 +99,6 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
 
 # to run ipynb
 conda install -n voicecraft ipykernel --no-deps --force-reinstall
-
-# below is only needed if you want to run gradio_app.py
-sudo apt-get install espeak # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
-sudo apt-get install libespeak-dev # NOTE: only required if you want to use gradio_app, which is used by whisperx for forced alignment
 ```
 
 If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.
@@ -117,6 +107,11 @@ If you have encountered version issues when running things, checkout [environmen
 Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) and [`inference_tts.ipynb`](./inference_tts.ipynb)
 
 ## Gradio
+### Run in colab
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zuev-stepan/VoiceCraft-gradio/blob/feature/colab-notebook/voicecraft-gradio-colab.ipynb)
+
+### Run locally
 After environment setup install additional dependencies:
 ```bash
 apt-get install -y espeak espeak-data libespeak1 libespeak-dev
@@ -130,7 +125,6 @@ pip install -r gradio_requirements.txt
 Run gradio server from terminal or [`gradio_app.ipynb`](./gradio_app.ipynb):
 ```bash
 python gradio_app.py
-TMP_PATH=/tmp python gradio_app.py # if you want to change tmp folder path
 ```
 It is ready to use on [default url](http://127.0.0.1:7860).
 
diff --git a/gradio_app.py b/gradio_app.py
index 162b4b3..a632f11 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -12,12 +12,10 @@ import numpy as np
 import random
 import uuid
 
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-os.chdir("/content/VoiceCraft-gradio-colab")
-os.environ['USER'] = 'aaa'
 
+DEMO_PATH = os.getenv("DEMO_PATH", ".demo")
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
+MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 whisper_model, align_model, voicecraft_model = None, None, None
 
@@ -94,14 +92,14 @@ def load_models(whisper_backend_name, whisper_model_name, alignment_model_name,
             transcribe_model = WhisperxModel(whisper_model_name, align_model)
 
     voicecraft_name = f"{voicecraft_model_name}.pth"
-    ckpt_fn = f"./pretrained_models/{voicecraft_name}"
-    encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
+    ckpt_fn = f"{MODELS_PATH}/{voicecraft_name}"
+    encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
     if not os.path.exists(ckpt_fn):
         os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
-        os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
+        os.system(f"mv {voicecraft_name}\?download\=true {MODELS_PATH}/{voicecraft_name}")
     if not os.path.exists(encodec_fn):
         os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
-        os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
+        os.system(f"mv encodec_4cb2048_giga.th {MODELS_PATH}/encodec_4cb2048_giga.th")
 
     ckpt = torch.load(ckpt_fn, map_location="cpu")
     model = voicecraft.VoiceCraft(ckpt["config"])
@@ -431,146 +429,131 @@ def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_wo
     ]
 
 
-with gr.Blocks() as app:
-    with gr.Row():
-        with gr.Column(scale=2):
-            load_models_btn = gr.Button(value="Load models")
-        with gr.Column(scale=5):
-            with gr.Accordion("Select models", open=False) as models_selector:
-                with gr.Row():
-                    voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
-                                                       choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
-                    whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
-                    whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
-                                                    choices=[None, "base.en", "small.en", "medium.en", "large"])
-                    align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            input_audio = gr.Audio(value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
-            with gr.Group():
-                original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
-                                                 info="Use whisper model to get the transcript. Fix and align it if necessary.")
-                with gr.Accordion("Word start time", open=False):
-                    transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
-                with gr.Accordion("Word end time", open=False):
-                    transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
-
-                transcribe_btn = gr.Button(value="Transcribe")
-                align_btn = gr.Button(value="Align")
-
-        with gr.Column(scale=3):
-            with gr.Group():
-                transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
-                with gr.Row():
-                    smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
-                    with gr.Accordion(label="?", open=False):
-                        info = gr.Markdown(value=smart_transcript_info)
-
-                with gr.Row():
-                    mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
-                    split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
-                                          info="Split text into parts and run TTS for each part.", visible=False)
-                    edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
-                                              info="What to do with first and last word", visible=False)
-
-                with gr.Group() as tts_mode_controls:
-                    prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
-                    prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
-
-                with gr.Group(visible=False) as edit_mode_controls:
+def get_app():
+    with gr.Blocks() as app:
+        with gr.Row():
+            with gr.Column(scale=2):
+                load_models_btn = gr.Button(value="Load models")
+            with gr.Column(scale=5):
+                with gr.Accordion("Select models", open=False) as models_selector:
                     with gr.Row():
-                        edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
-                        edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                        voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
+                                                        choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
+                        whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
+                        whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
+                                                        choices=[None, "base.en", "small.en", "medium.en", "large"])
+                        align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                input_audio = gr.Audio(value=f"{DEMO_PATH}/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
+                with gr.Group():
+                    original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
+                                                    info="Use whisper model to get the transcript. Fix and align it if necessary.")
+                    with gr.Accordion("Word start time", open=False):
+                        transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
+                    with gr.Accordion("Word end time", open=False):
+                        transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
+
+                    transcribe_btn = gr.Button(value="Transcribe")
+                    align_btn = gr.Button(value="Align")
+
+            with gr.Column(scale=3):
+                with gr.Group():
+                    transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
                     with gr.Row():
-                        edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
-                        edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
+                        smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
+                        with gr.Accordion(label="?", open=False):
+                            info = gr.Markdown(value=smart_transcript_info)
 
-                run_btn = gr.Button(value="Run")
+                    with gr.Row():
+                        mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
+                        split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
+                                            info="Split text into parts and run TTS for each part.", visible=False)
+                        edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
+                                                info="What to do with first and last word", visible=False)
 
-        with gr.Column(scale=2):
-            output_audio = gr.Audio(label="Output Audio")
-            with gr.Accordion("Inference transcript", open=False):
-                inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
-                                                  info="Inference was performed on this transcript.")
-            with gr.Group(visible=False) as long_tts_sentence_editor:
-                sentence_selector = gr.Dropdown(label="Sentence", value=None,
-                                                info="Select sentence you want to regenerate")
-                sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
-                rerun_btn = gr.Button(value="Rerun")
+                    with gr.Group() as tts_mode_controls:
+                        prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
+                        prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
 
-    with gr.Row():
-        with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
-            stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
-                                       info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
-            sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
-                                          info="The higher the number, the faster the output will be. "
-                                               "Under the hood, the model will generate this many samples and choose the shortest one. "
-                                               "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
-            seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
-            kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
-                                info="set to 0 to use less VRAM, but with slower inference")
-            left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
-            right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
-            top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
-            temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
-            top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
-            codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
-            codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
-            silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
+                    with gr.Group(visible=False) as edit_mode_controls:
+                        with gr.Row():
+                            edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
+                            edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
+                        with gr.Row():
+                            edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
+                            edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
+
+                    run_btn = gr.Button(value="Run")
+
+            with gr.Column(scale=2):
+                output_audio = gr.Audio(label="Output Audio")
+                with gr.Accordion("Inference transcript", open=False):
+                    inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
+                                                    info="Inference was performed on this transcript.")
+                with gr.Group(visible=False) as long_tts_sentence_editor:
+                    sentence_selector = gr.Dropdown(label="Sentence", value=None,
+                                                    info="Select sentence you want to regenerate")
+                    sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
+                    rerun_btn = gr.Button(value="Rerun")
+
+        with gr.Row():
+            with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
+                stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
+                                        info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
+                sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
+                                            info="The higher the number, the faster the output will be. "
+                                                "Under the hood, the model will generate this many samples and choose the shortest one. "
+                                                "For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
+                seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
+                kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
+                                    info="set to 0 to use less VRAM, but with slower inference")
+                left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
+                right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
+                top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
+                temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
+                top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
+                codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
+                codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
+                silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
 
 
-    audio_tensors = gr.State()
-    transcribe_state = gr.State(value={"words_info": demo_words_info})
+        audio_tensors = gr.State()
+        transcribe_state = gr.State(value={"words_info": demo_words_info})
 
 
-    mode.change(fn=update_demo,
-                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
-                outputs=[transcript, edit_from_word, edit_to_word])
-    edit_word_mode.change(fn=update_demo,
-                          inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
-                          outputs=[transcript, edit_from_word, edit_to_word])
-    smart_transcript.change(fn=update_demo,
+        mode.change(fn=update_demo,
+                    inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                    outputs=[transcript, edit_from_word, edit_to_word])
+        edit_word_mode.change(fn=update_demo,
                             inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
                             outputs=[transcript, edit_from_word, edit_to_word])
+        smart_transcript.change(fn=update_demo,
+                                inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
+                                outputs=[transcript, edit_from_word, edit_to_word])
 
-    load_models_btn.click(fn=load_models,
-                          inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
-                          outputs=[models_selector])
+        load_models_btn.click(fn=load_models,
+                            inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
+                            outputs=[models_selector])
 
-    input_audio.upload(fn=update_input_audio,
-                       inputs=[input_audio],
-                       outputs=[prompt_end_time, edit_start_time, edit_end_time])
-    transcribe_btn.click(fn=transcribe,
-                         inputs=[seed, input_audio],
-                         outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
-                                  prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
-    align_btn.click(fn=align,
-                    inputs=[seed, original_transcript, input_audio],
-                    outputs=[transcript_with_start_time, transcript_with_end_time,
-                             prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+        input_audio.upload(fn=update_input_audio,
+                        inputs=[input_audio],
+                        outputs=[prompt_end_time, edit_start_time, edit_end_time])
+        transcribe_btn.click(fn=transcribe,
+                            inputs=[seed, input_audio],
+                            outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
+                                    prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
+        align_btn.click(fn=align,
+                        inputs=[seed, original_transcript, input_audio],
+                        outputs=[transcript_with_start_time, transcript_with_end_time,
+                                prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
 
-    mode.change(fn=change_mode,
-                inputs=[mode],
-                outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
+        mode.change(fn=change_mode,
+                    inputs=[mode],
+                    outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
 
-    run_btn.click(fn=run,
-                  inputs=[
-                      seed, left_margin, right_margin,
-                      codec_audio_sr, codec_sr,
-                      top_k, top_p, temperature,
-                      stop_repetition, sample_batch_size,
-                      kvcache, silence_tokens,
-                      input_audio, transcribe_state, transcript, smart_transcript,
-                      mode, prompt_end_time, edit_start_time, edit_end_time,
-                      split_text, sentence_selector, audio_tensors
-                  ],
-                  outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
-
-    sentence_selector.change(fn=load_sentence,
-                             inputs=[sentence_selector, codec_audio_sr, audio_tensors],
-                             outputs=[sentence_audio])
-    rerun_btn.click(fn=run,
+        run_btn.click(fn=run,
                     inputs=[
                         seed, left_margin, right_margin,
                         codec_audio_sr, codec_sr,
@@ -578,24 +561,58 @@ with gr.Blocks() as app:
                         stop_repetition, sample_batch_size,
                         kvcache, silence_tokens,
                         input_audio, transcribe_state, transcript, smart_transcript,
-                        gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                        mode, prompt_end_time, edit_start_time, edit_end_time,
                         split_text, sentence_selector, audio_tensors
                     ],
-                    outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
+                    outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
 
-    prompt_to_word.change(fn=update_bound_word,
-                          inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
-                          outputs=[prompt_end_time])
-    edit_from_word.change(fn=update_bound_word,
-                          inputs=[gr.State(True), edit_from_word, edit_word_mode],
-                          outputs=[edit_start_time])
-    edit_to_word.change(fn=update_bound_word,
-                        inputs=[gr.State(False), edit_to_word, edit_word_mode],
-                        outputs=[edit_end_time])
-    edit_word_mode.change(fn=update_bound_words,
-                          inputs=[edit_from_word, edit_to_word, edit_word_mode],
-                          outputs=[edit_start_time, edit_end_time])
+        sentence_selector.change(fn=load_sentence,
+                                inputs=[sentence_selector, codec_audio_sr, audio_tensors],
+                                outputs=[sentence_audio])
+        rerun_btn.click(fn=run,
+                        inputs=[
+                            seed, left_margin, right_margin,
+                            codec_audio_sr, codec_sr,
+                            top_k, top_p, temperature,
+                            stop_repetition, sample_batch_size,
+                            kvcache, silence_tokens,
+                            input_audio, transcribe_state, transcript, smart_transcript,
+                            gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
+                            split_text, sentence_selector, audio_tensors
+                        ],
+                        outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
+
+        prompt_to_word.change(fn=update_bound_word,
+                            inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
+                            outputs=[prompt_end_time])
+        edit_from_word.change(fn=update_bound_word,
+                            inputs=[gr.State(True), edit_from_word, edit_word_mode],
+                            outputs=[edit_start_time])
+        edit_to_word.change(fn=update_bound_word,
+                            inputs=[gr.State(False), edit_to_word, edit_word_mode],
+                            outputs=[edit_end_time])
+        edit_word_mode.change(fn=update_bound_words,
+                            inputs=[edit_from_word, edit_to_word, edit_word_mode],
+                            outputs=[edit_start_time, edit_end_time])
+    return app
 
 
 if __name__ == "__main__":
-    app.launch(share=True)
+    import argparse
+
+    parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
+    
+    parser.add_argument("--demo-path", default=".demo", help="Path to demo directory")
+    parser.add_argument("--tmp-path", default=".demo/temp", help="Path to tmp directory")
+    parser.add_argument("--models-path", default=".pretrained_models", help="Path to voicecraft models directory")
+    parser.add_argument("--port", default=7860, type=int, help="App port")
+    parser.add_argument("--share", action="store_true", help="Launch with public url")
+
+    os.environ["USER"] = os.getenv("USER", "user")
+    args = parser.parse_args()
+    DEMO_PATH = args.demo_path
+    TMP_PATH = args.tmp_path
+    MODELS_PATH = args.models_path
+
+    app = get_app()
+    app.launch(share=args.share, server_port=args.port)
diff --git a/voicecraft.ipynb b/voicecraft-gradio-colab.ipynb
similarity index 84%
rename from voicecraft.ipynb
rename to voicecraft-gradio-colab.ipynb
index 78c816a..a13fb2e 100644
--- a/voicecraft.ipynb
+++ b/voicecraft-gradio-colab.ipynb
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4",
-      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/Sewlell/VoiceCraft-gradio-colab/blob/master/voicecraft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -36,11 +18,16 @@
       },
       "outputs": [],
       "source": [
-        "!git clone https://github.com/Sewlell/VoiceCraft-gradio-colab"
+        "!git clone https://github.com/zuev-stepan/VoiceCraft-gradio"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-w3USR91XdxY"
+      },
+      "outputs": [],
       "source": [
         "!pip install tensorboard\n",
         "!pip install phonemizer\n",
@@ -55,25 +42,23 @@
         "\n",
         "!pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft\n",
         "\n",
-        "!pip install -r \"/content/VoiceCraft-gradio-colab/gradio_requirements.txt\""
-      ],
-      "metadata": {
-        "id": "-w3USR91XdxY"
-      },
-      "execution_count": null,
-      "outputs": []
+        "!pip install -r \"/content/VoiceCraft-gradio/gradio_requirements.txt\""
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Let it restarted, it won't let your entire installation be aborted."
-      ],
       "metadata": {
         "id": "jNuzjrtmv2n1"
-      }
+      },
+      "source": [
+        "# Let it restarted, it won't let your entire installation be aborted."
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "AnqGEwZ4NxtJ"
+      },
       "source": [
         "# Note before launching the `gradio_app.py`\n",
         "\n",
@@ -83,45 +68,58 @@
         "\n",
         "Frequency of VRAM spikes no longer exist as well in April 5 Update.\n",
         "* Nevermind, I have observed some weird usage on Colab's GPU Memory Monitor. It can spike up to 13.5GB VRAM even in WhisperX mode. (April 11)"
-      ],
-      "metadata": {
-        "id": "AnqGEwZ4NxtJ"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "dE0W76cMN3Si"
+      },
       "source": [
         "Don't make your `prompt end time` too long, 6-9s is fine. Or else it will **either raise up JSON issue or cut off your generated audio**. This one is due to how VoiceCraft worked (so probably unfixable). It will add those text you want to get audio from at the end of the input audio transcript. It was way too much word for application or code to handle as it added up with original transcript. So please keep it short.\n",
         "\n",
         "Your total audio length (`prompt end time` + add-up audio) must not exceed 16 or 17s."
-      ],
-      "metadata": {
-        "id": "dE0W76cMN3Si"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "nnu2cY4t8P6X"
+      },
       "source": [
         "For voice cloning, I suggest you to probably have a monotone input to feed the voice cloning. Of course you can always try input that have tons of tone variety, but I find that as per April 11 Update, it's much more easy to replicate in monotone rather than audio that have laugh, scream, crying inside.\n",
         "\n",
-        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`.\n",
-        "\n",
-        "I haven't test the Edit mode yet as those are not my focus, but you can try it."
-      ],
-      "metadata": {
-        "id": "nnu2cY4t8P6X"
-      }
+        "The inference speed is much stable. With sample text, T4 (Free Tier Colab GPU) can do 6-15s on 6s-8s of `prompt end time`."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!python \"/content/VoiceCraft-gradio-colab/gradio_app.py\""
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "NDt4r4DiXAwG"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "!python /content/VoiceCraft-gradio/gradio_app.py --demo-path=/content/VoiceCraft-gradio/demo --tmp-path=/content/VoiceCraft-gradio/demo/temp --models-path=/content/VoiceCraft-gradio/pretrained_models --share"
+      ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "authorship_tag": "ABX9TyPsqFhtOeQ18CXHnRkWAQSk",
+      "gpuType": "T4",
+      "include_colab_link": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From b818145ad90f848fe508d1a907755000d861a3a7 Mon Sep 17 00:00:00 2001
From: Stepan Zuev <zuev.step4n@gmail.com>
Date: Thu, 11 Apr 2024 17:22:41 +0500
Subject: [PATCH 38/40] queue added

---
 gradio_app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gradio_app.py b/gradio_app.py
index a632f11..0f82600 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -615,4 +615,4 @@ if __name__ == "__main__":
     MODELS_PATH = args.models_path
 
     app = get_app()
-    app.launch(share=args.share, server_port=args.port)
+    app.queue().launch(share=args.share, server_port=args.port)

From ad6c2cd836ab99e1c6c1b6dc508f9ebf151980af Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Thu, 11 Apr 2024 07:17:28 -0700
Subject: [PATCH 39/40] a little massage

---
 README.md                     |  26 +++++++++++++++-----------
 demo/pam.wav                  | Bin 0 -> 770082 bytes
 gradio_app.py                 |  20 ++++++++++----------
 voicecraft-gradio-colab.ipynb |   2 +-
 4 files changed, 26 insertions(+), 22 deletions(-)
 create mode 100644 demo/pam.wav

diff --git a/README.md b/README.md
index a9b06d7..629461e 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,5 @@
 # VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
-[Demo](https://jasonppy.github.io/VoiceCraft_web) [Paper](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)
-
+[![Paper](https://img.shields.io/badge/arXiv-2301.12503-brightgreen.svg?style=flat-square)](https://jasonppy.github.io/assets/pdfs/VoiceCraft.pdf)  [![githubio](https://img.shields.io/badge/GitHub.io-Audio_Samples-blue?logo=Github&style=flat-square)](https://jasonppy.github.io/VoiceCraft_web/)  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/pyp1/VoiceCraft_gradio)  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IOjpglQyMTO2C3Y94LD9FY0Ocn-RJRg6?usp=sharing)
 
 ### TL;DR
 VoiceCraft is a token infilling neural codec language model, that achieves state-of-the-art performance on both **speech editing** and **zero-shot text-to-speech (TTS)** on in-the-wild data including audiobooks, internet videos, and podcasts.
@@ -8,20 +7,22 @@ VoiceCraft is a token infilling neural codec language model, that achieves state
 To clone or edit an unseen voice, VoiceCraft needs only a few seconds of reference.
 
 ## How to run inference
-There are three ways:
+There are three ways (besides running Gradio in Colab):
 
-1. with Google Colab. see [quickstart colab](#quickstart-colab)
+1. More flexible inference beyond Gradio UI in Google Colab. see [quickstart colab](#quickstart-colab)
 2. with docker. see [quickstart docker](#quickstart-docker)
-3. without docker. see [environment setup](#environment-setup)
+3. without docker. see [environment setup](#environment-setup). You can also run gradio locally if you choose this option
 
 When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).
 
 If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
 
 ## News
-:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
+:star: 04/11/2024: VoiceCraft Gradio is now available on HuggingFace Spaces [here](https://huggingface.co/spaces/pyp1/VoiceCraft_gradio)! Major thanks to [@zuev-stepan](https://github.com/zuev-stepan), [@Sewlell](https://github.com/Sewlell), [@pgsoar](https://github.com/pgosar) [@Ph0rk0z](https://github.com/Ph0rk0z). 
 
-:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight, the model outperforms giga830M on TTS. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data)
+:star: 04/05/2024: I finetuned giga330M with the TTS objective on gigaspeech and 1/5 of librilight. Weights are [here](https://huggingface.co/pyp1/VoiceCraft/tree/main). Make sure maximal prompt + generation length <= 16 seconds (due to our limited compute, we had to drop utterances longer than 16s in training data). Even stronger models forthcomming, stay tuned!
+
+:star: 03/28/2024: Model weights for giga330M and giga830M are up on HuggingFace🤗 [here](https://huggingface.co/pyp1/VoiceCraft/tree/main)!
 
 ## TODO
 - [x] Codebase upload
@@ -30,9 +31,12 @@ If you want to do model development such as training/finetuning, I recommend fol
 - [x] Training guidance
 - [x] RealEdit dataset and training manifest
 - [x] Model weights (giga330M.pth, giga830M.pth, and gigaHalfLibri330M_TTSEnhanced_max16s.pth)
-- [x] Write colab notebooks for better hands-on experience
-- [ ] HuggingFace Spaces demo
-- [ ] Better guidance on training/finetuning
+- [x] Better guidance on training/finetuning
+- [x] Colab notebooks
+- [x] HuggingFace Spaces demo
+- [ ] Command line
+- [ ] Improve efficiency
+
 
 
 ## QuickStart Colab
@@ -109,7 +113,7 @@ Checkout [`inference_speech_editing.ipynb`](./inference_speech_editing.ipynb) an
 ## Gradio
 ### Run in colab
 
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/zuev-stepan/VoiceCraft-gradio/blob/feature/colab-notebook/voicecraft-gradio-colab.ipynb)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1IOjpglQyMTO2C3Y94LD9FY0Ocn-RJRg6?usp=sharing)
 
 ### Run locally
 After environment setup install additional dependencies:
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diff --git a/gradio_app.py b/gradio_app.py
index 0f82600..113f0ab 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -13,7 +13,7 @@ import random
 import uuid
 
 
-DEMO_PATH = os.getenv("DEMO_PATH", ".demo")
+DEMO_PATH = os.getenv("DEMO_PATH", "./demo")
 TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
 MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -371,20 +371,20 @@ demo_original_transcript = " But when I had approached so near to them, the comm
 
 demo_text = {
     "TTS": {
-        "smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
-        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis as well!"
+        "smart": "I cannot believe that the same model can also do text to speech synthesis too!",
+        "regular": "But when I had approached so near to them, the common I cannot believe that the same model can also do text to speech synthesis too!"
     },
     "Edit": {
         "smart": "saw the mirage of the lake in the distance,",
         "regular": "But when I saw the mirage of the lake in the distance, which the sense deceives, Lost not by distance any of its marks,"
     },
     "Long TTS": {
-        "smart": "You can run TTS on a big text!\n"
+        "smart": "You can run the model on a big text!\n"
                  "Just write it line-by-line. Or sentence-by-sentence.\n"
-                 "If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!",
-        "regular": "But when I had approached so near to them, the common You can run TTS on a big text!\n"
+                 "If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
+        "regular": "But when I had approached so near to them, the common You can run the model on a big text!\n"
                    "But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
-                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun TTS on them, no need to generate the whole text again!"
+                   "But when I had approached so near to them, the common If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!"
     }
 }
 
@@ -602,9 +602,9 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
     
-    parser.add_argument("--demo-path", default=".demo", help="Path to demo directory")
-    parser.add_argument("--tmp-path", default=".demo/temp", help="Path to tmp directory")
-    parser.add_argument("--models-path", default=".pretrained_models", help="Path to voicecraft models directory")
+    parser.add_argument("--demo-path", default="./demo", help="Path to demo directory")
+    parser.add_argument("--tmp-path", default="./demo/temp", help="Path to tmp directory")
+    parser.add_argument("--models-path", default="./pretrained_models", help="Path to voicecraft models directory")
     parser.add_argument("--port", default=7860, type=int, help="App port")
     parser.add_argument("--share", action="store_true", help="Launch with public url")
 
diff --git a/voicecraft-gradio-colab.ipynb b/voicecraft-gradio-colab.ipynb
index a13fb2e..4a5953c 100644
--- a/voicecraft-gradio-colab.ipynb
+++ b/voicecraft-gradio-colab.ipynb
@@ -18,7 +18,7 @@
       },
       "outputs": [],
       "source": [
-        "!git clone https://github.com/zuev-stepan/VoiceCraft-gradio"
+        "!git clone https://github.com/jasonppy/VoiceCraft"
       ]
     },
     {

From 3a8d5f4aab358814fbfce7f9055d54f70a7448d8 Mon Sep 17 00:00:00 2001
From: jason-on-salt-a40 <pyp@utexas.edu>
Date: Thu, 11 Apr 2024 08:45:57 -0700
Subject: [PATCH 40/40] small bug fix

---
 gradio_app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gradio_app.py b/gradio_app.py
index 113f0ab..907ffed 100644
--- a/gradio_app.py
+++ b/gradio_app.py
@@ -316,7 +316,7 @@ def update_input_audio(audio_path):
 
 
 def change_mode(mode):
-    tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
+    # tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
     return [
         gr.Group(visible=mode != "Edit"),
         gr.Group(visible=mode == "Edit"),