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README.md
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# 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
@ -31,12 +32,13 @@ If you want to do model development such as training/finetuning, I recommend fol
- [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
- [x] Colab notebooks
- [x] HuggingFace Spaces demo
- [ ] Command line
- [ ] Improve efficiency
## QuickStart Colab
:star: To try out speech editing or TTS Inference with VoiceCraft, the simplest way is using Google Colab.
@ -112,6 +114,46 @@ 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
### Run in colab
[![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:
```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
```
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
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

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{
"cells": [
{
"cell_type": "markdown",
"id": "9b6a0c92",
"metadata": {},
"source": [
"### Only do the below if you are using docker"
]
},
{
"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",
" 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
}

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import os
import gradio as gr
import torch
import torchaudio
from data.tokenizer import (
AudioTokenizer,
TextTokenizer,
)
from models import voicecraft
import io
import numpy as np
import random
import uuid
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
def get_random_string():
return "".join(str(uuid.uuid4()).split("-"))
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
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)
from whisper.tokenizer import get_tokenizer
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, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
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
if voicecraft_model_name == "giga330M_TTSEnhanced":
voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
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_name}.pth"
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 {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 {MODELS_PATH}/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 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 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)
return [
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)
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
]
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(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, 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 (transcribe_state 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')
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 transcribe_state["words_info"]:
if word["end"] < prompt_end_time:
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"] + (" " if word["word"][-1] != " " else "")
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 transcribe_state["words_info"]:
if word["start"] < edit_start_time:
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"] + (" " if word["word"][-1] != " " else "")
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(choices=sentences, value=sentences[0])
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(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 [
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(mode):
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"),
gr.Radio(visible=mode == "Edit"),
gr.Radio(visible=mode == "Long TTS"),
gr.Group(visible=mode == "Long TTS"),
]
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, selected_word, edit_word_mode):
if selected_word is None:
return None
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(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 = """
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_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 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 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 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 the model 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 = [
'0.029 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_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}
]
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]
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"],
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,
]
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():
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():
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:
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})
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])
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])
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,
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__":
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.queue().launch(share=args.share, server_port=args.port)

5
gradio_requirements.txt Normal file
View File

@ -0,0 +1,5 @@
gradio==3.50.2
nltk>=3.8.1
openai-whisper>=20231117
aeneas>=1.7.3.0
whisperx>=3.1.1

1
start-jupyter.bat
View File

@ -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%" ^

1
start-jupyter.sh
View File

@ -8,6 +8,7 @@ docker run -it \
-d \
--gpus all \
-p 8888:8888 \
-p 7860:7860 \
--name jupyter \
--user root \
-e NB_USER="$USER" \

125
voicecraft-gradio-colab.ipynb Normal file
View File

@ -0,0 +1,125 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"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>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Y87ixxsUVIhM"
},
"outputs": [],
"source": [
"!git clone https://github.com/jasonppy/VoiceCraft"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-w3USR91XdxY"
},
"outputs": [],
"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_requirements.txt\""
]
},
{
"cell_type": "markdown",
"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",
"***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 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",
"* 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)"
]
},
{
"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."
]
},
{
"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`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NDt4r4DiXAwG"
},
"outputs": [],
"source": [
"!python /content/VoiceCraft/gradio_app.py --demo-path=/content/VoiceCraft/demo --tmp-path=/content/VoiceCraft/demo/temp --models-path=/content/VoiceCraft/pretrained_models --share"
]
}
],
"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
}