mirror of
https://github.com/jasonppy/VoiceCraft.git
synced 2024-12-13 00:56:15 +01:00
619 lines
32 KiB
Python
619 lines
32 KiB
Python
import os
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import gradio as gr
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import torch
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import torchaudio
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from data.tokenizer import (
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AudioTokenizer,
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TextTokenizer,
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)
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from models import voicecraft
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import io
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import numpy as np
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import random
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import uuid
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DEMO_PATH = os.getenv("DEMO_PATH", "./demo")
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TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
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MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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whisper_model, align_model, voicecraft_model = None, None, None
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def get_random_string():
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return "".join(str(uuid.uuid4()).split("-"))
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def seed_everything(seed):
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if seed != -1:
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os.environ['PYTHONHASHSEED'] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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class WhisperxAlignModel:
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def __init__(self):
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from whisperx import load_align_model
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self.model, self.metadata = load_align_model(language_code="en", device=device)
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def align(self, segments, audio_path):
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from whisperx import align, load_audio
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audio = load_audio(audio_path)
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return align(segments, self.model, self.metadata, audio, device, return_char_alignments=False)["segments"]
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class WhisperModel:
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def __init__(self, model_name):
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from whisper import load_model
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self.model = load_model(model_name, device)
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from whisper.tokenizer import get_tokenizer
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tokenizer = get_tokenizer(multilingual=False)
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self.supress_tokens = [-1] + [
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i
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for i in range(tokenizer.eot)
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if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
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]
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def transcribe(self, audio_path):
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return self.model.transcribe(audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True)["segments"]
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class WhisperxModel:
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def __init__(self, model_name, align_model: WhisperxAlignModel):
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from whisperx import load_model
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self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
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self.align_model = align_model
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def transcribe(self, audio_path):
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segments = self.model.transcribe(audio_path, batch_size=8)["segments"]
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return self.align_model.align(segments, audio_path)
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def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
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global transcribe_model, align_model, voicecraft_model
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if voicecraft_model_name == "giga330M_TTSEnhanced":
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voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
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if alignment_model_name is not None:
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align_model = WhisperxAlignModel()
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if whisper_model_name is not None:
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if whisper_backend_name == "whisper":
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transcribe_model = WhisperModel(whisper_model_name)
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else:
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if align_model is None:
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raise gr.Error("Align model required for whisperx backend")
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transcribe_model = WhisperxModel(whisper_model_name, align_model)
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voicecraft_name = f"{voicecraft_model_name}.pth"
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ckpt_fn = f"{MODELS_PATH}/{voicecraft_name}"
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encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
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if not os.path.exists(ckpt_fn):
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os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
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os.system(f"mv {voicecraft_name}\?download\=true {MODELS_PATH}/{voicecraft_name}")
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if not os.path.exists(encodec_fn):
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os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
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os.system(f"mv encodec_4cb2048_giga.th {MODELS_PATH}/encodec_4cb2048_giga.th")
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ckpt = torch.load(ckpt_fn, map_location="cpu")
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model = voicecraft.VoiceCraft(ckpt["config"])
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model.load_state_dict(ckpt["model"])
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model.to(device)
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model.eval()
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voicecraft_model = {
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"ckpt": ckpt,
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"model": model,
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"text_tokenizer": TextTokenizer(backend="espeak"),
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"audio_tokenizer": AudioTokenizer(signature=encodec_fn)
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}
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return gr.Accordion()
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def get_transcribe_state(segments):
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words_info = [word_info for segment in segments for word_info in segment["words"]]
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return {
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"segments": segments,
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"transcript": " ".join([segment["text"] for segment in segments]),
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"words_info": words_info,
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"transcript_with_start_time": " ".join([f"{word['start']} {word['word']}" for word in words_info]),
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"transcript_with_end_time": " ".join([f"{word['word']} {word['end']}" for word in words_info]),
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"word_bounds": [f"{word['start']} {word['word']} {word['end']}" for word in words_info]
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}
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def transcribe(seed, audio_path):
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if transcribe_model is None:
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raise gr.Error("Transcription model not loaded")
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seed_everything(seed)
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segments = transcribe_model.transcribe(audio_path)
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state = get_transcribe_state(segments)
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return [
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state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
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gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
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gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
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gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
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state
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]
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def align_segments(transcript, audio_path):
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from aeneas.executetask import ExecuteTask
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from aeneas.task import Task
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import json
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config_string = 'task_language=eng|os_task_file_format=json|is_text_type=plain'
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tmp_transcript_path = os.path.join(TMP_PATH, f"{get_random_string()}.txt")
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tmp_sync_map_path = os.path.join(TMP_PATH, f"{get_random_string()}.json")
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with open(tmp_transcript_path, "w") as f:
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f.write(transcript)
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task = Task(config_string=config_string)
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task.audio_file_path_absolute = os.path.abspath(audio_path)
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task.text_file_path_absolute = os.path.abspath(tmp_transcript_path)
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task.sync_map_file_path_absolute = os.path.abspath(tmp_sync_map_path)
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ExecuteTask(task).execute()
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task.output_sync_map_file()
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with open(tmp_sync_map_path, "r") as f:
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return json.load(f)
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def align(seed, transcript, audio_path):
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if align_model is None:
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raise gr.Error("Align model not loaded")
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seed_everything(seed)
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fragments = align_segments(transcript, audio_path)
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segments = [{
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"start": float(fragment["begin"]),
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"end": float(fragment["end"]),
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"text": " ".join(fragment["lines"])
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} for fragment in fragments["fragments"]]
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segments = align_model.align(segments, audio_path)
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state = get_transcribe_state(segments)
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return [
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state["transcript_with_start_time"], state["transcript_with_end_time"],
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gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
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gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
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gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
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state
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]
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def get_output_audio(audio_tensors, codec_audio_sr):
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result = torch.cat(audio_tensors, 1)
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buffer = io.BytesIO()
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torchaudio.save(buffer, result, int(codec_audio_sr), format="wav")
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buffer.seek(0)
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return buffer.read()
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def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
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stop_repetition, sample_batch_size, kvcache, silence_tokens,
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audio_path, transcribe_state, transcript, smart_transcript,
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mode, prompt_end_time, edit_start_time, edit_end_time,
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split_text, selected_sentence, previous_audio_tensors):
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if voicecraft_model is None:
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raise gr.Error("VoiceCraft model not loaded")
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if smart_transcript and (transcribe_state is None):
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raise gr.Error("Can't use smart transcript: whisper transcript not found")
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seed_everything(seed)
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if mode == "Long TTS":
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if split_text == "Newline":
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sentences = transcript.split('\n')
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else:
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from nltk.tokenize import sent_tokenize
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sentences = sent_tokenize(transcript.replace("\n", " "))
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elif mode == "Rerun":
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colon_position = selected_sentence.find(':')
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selected_sentence_idx = int(selected_sentence[:colon_position])
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sentences = [selected_sentence[colon_position + 1:]]
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else:
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sentences = [transcript.replace("\n", " ")]
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info = torchaudio.info(audio_path)
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audio_dur = info.num_frames / info.sample_rate
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audio_tensors = []
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inference_transcript = ""
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for sentence in sentences:
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decode_config = {"top_k": top_k, "top_p": top_p, "temperature": temperature, "stop_repetition": stop_repetition,
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"kvcache": kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr,
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"silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
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if mode != "Edit":
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from inference_tts_scale import inference_one_sample
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if smart_transcript:
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target_transcript = ""
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for word in transcribe_state["words_info"]:
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if word["end"] < prompt_end_time:
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target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
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elif (word["start"] + word["end"]) / 2 < prompt_end_time:
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# include part of the word it it's big, but adjust prompt_end_time
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target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
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prompt_end_time = word["end"]
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break
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else:
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break
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target_transcript += f" {sentence}"
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else:
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target_transcript = sentence
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inference_transcript += target_transcript + "\n"
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prompt_end_frame = int(min(audio_dur, prompt_end_time) * info.sample_rate)
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_, gen_audio = inference_one_sample(voicecraft_model["model"],
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voicecraft_model["ckpt"]["config"],
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voicecraft_model["ckpt"]["phn2num"],
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voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
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audio_path, target_transcript, device, decode_config,
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prompt_end_frame)
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else:
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from inference_speech_editing_scale import inference_one_sample
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if smart_transcript:
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target_transcript = ""
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for word in transcribe_state["words_info"]:
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if word["start"] < edit_start_time:
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target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
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else:
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break
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target_transcript += f" {sentence}"
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for word in transcribe_state["words_info"]:
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if word["end"] > edit_end_time:
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target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
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else:
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target_transcript = sentence
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inference_transcript += target_transcript + "\n"
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morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
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mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
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mask_interval = torch.LongTensor(mask_interval)
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_, gen_audio = inference_one_sample(voicecraft_model["model"],
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voicecraft_model["ckpt"]["config"],
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voicecraft_model["ckpt"]["phn2num"],
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voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
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audio_path, target_transcript, mask_interval, device, decode_config)
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gen_audio = gen_audio[0].cpu()
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audio_tensors.append(gen_audio)
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if mode != "Rerun":
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output_audio = get_output_audio(audio_tensors, codec_audio_sr)
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sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
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component = gr.Dropdown(choices=sentences, value=sentences[0])
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return output_audio, inference_transcript, component, audio_tensors
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else:
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previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
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output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
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sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
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return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
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def update_input_audio(audio_path):
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if audio_path is None:
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return 0, 0, 0
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info = torchaudio.info(audio_path)
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max_time = round(info.num_frames / info.sample_rate, 2)
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return [
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gr.Slider(maximum=max_time, value=max_time),
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gr.Slider(maximum=max_time, value=0),
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gr.Slider(maximum=max_time, value=max_time),
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]
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def change_mode(mode):
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# tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
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return [
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gr.Group(visible=mode != "Edit"),
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gr.Group(visible=mode == "Edit"),
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gr.Radio(visible=mode == "Edit"),
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gr.Radio(visible=mode == "Long TTS"),
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gr.Group(visible=mode == "Long TTS"),
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]
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def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
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if selected_sentence is None:
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return None
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colon_position = selected_sentence.find(':')
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selected_sentence_idx = int(selected_sentence[:colon_position])
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return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
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def update_bound_word(is_first_word, selected_word, edit_word_mode):
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if selected_word is None:
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return None
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word_start_time = float(selected_word.split(' ')[0])
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word_end_time = float(selected_word.split(' ')[-1])
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if edit_word_mode == "Replace half":
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bound_time = (word_start_time + word_end_time) / 2
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elif is_first_word:
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bound_time = word_start_time
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else:
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bound_time = word_end_time
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return bound_time
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def update_bound_words(from_selected_word, to_selected_word, edit_word_mode):
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return [
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update_bound_word(True, from_selected_word, edit_word_mode),
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update_bound_word(False, to_selected_word, edit_word_mode),
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]
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smart_transcript_info = """
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If enabled, the target transcript will be constructed for you:</br>
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- In TTS and Long TTS mode just write the text you want to synthesize.</br>
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- In Edit mode just write the text to replace selected editing segment.</br>
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If disabled, you should write the target transcript yourself:</br>
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- In TTS mode write prompt transcript followed by generation transcript.</br>
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- In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
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- In Edit mode write full prompt</br>
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"""
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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."
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demo_text = {
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"TTS": {
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"smart": "I cannot believe that the same model can also do text to speech synthesis too!",
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"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!"
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},
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"Edit": {
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"smart": "saw the mirage of the lake in the distance,",
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"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,"
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},
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"Long TTS": {
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"smart": "You can run the model on a big text!\n"
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"Just write it line-by-line. Or sentence-by-sentence.\n"
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"If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
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"regular": "But when I had approached so near to them, the common You can run the model on a big text!\n"
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"But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
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"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!"
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}
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}
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all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
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demo_words = [
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'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',
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'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',
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'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',
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'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'
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]
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demo_words_info = [
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{'word': 'But', 'start': 0.029, 'end': 0.149, 'score': 0.834}, {'word': 'when', 'start': 0.189, 'end': 0.33, 'score': 0.879},
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{'word': 'I', 'start': 0.43, 'end': 0.49, 'score': 0.984}, {'word': 'had', 'start': 0.53, 'end': 0.65, 'score': 0.998},
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{'word': 'approached', 'start': 0.711, 'end': 1.152, 'score': 0.822}, {'word': 'so', 'start': 1.352, 'end': 1.593, 'score': 0.822},
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{'word': 'near', 'start': 1.693, 'end': 1.933, 'score': 0.752}, {'word': 'to', 'start': 1.994, 'end': 2.074, 'score': 0.924},
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{'word': 'them,', 'start': 2.134, 'end': 2.354, 'score': 0.914}, {'word': 'the', 'start': 2.535, 'end': 2.655, 'score': 0.818},
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{'word': 'common', 'start': 2.695, 'end': 3.016, 'score': 0.971}, {'word': 'object,', 'start': 3.196, 'end': 3.577, 'score': 0.823},
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{'word': 'which', 'start': 3.717, 'end': 3.898, 'score': 0.701}, {'word': 'the', 'start': 3.958, 'end': 4.058, 'score': 0.798},
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{'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]
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|
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,
|
|
]
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|
|
|
|
|
def get_app():
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|
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)
|