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Build error
Build error
jason-on-salt-a40
commited on
Commit
•
d63a00c
1
Parent(s):
ca27bc7
whisperx, more models, better instructions
Browse files- app.py +342 -251
- app_old.py +528 -0
app.py
CHANGED
@@ -1,6 +1,4 @@
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import os
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# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "1" # these are only used if developping locally
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import gradio as gr
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import torch
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import torchaudio
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@@ -12,10 +10,19 @@ 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 spaces
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@spaces.GPU(duration=30)
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def seed_everything(seed):
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torch.backends.cudnn.deterministic = True
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@spaces.GPU(duration=120)
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if whisper_model_choice is not None:
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import whisper
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from whisper.tokenizer import get_tokenizer
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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
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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
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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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@@ -67,32 +116,78 @@ def load_models(whisper_model_choice, voicecraft_model_choice):
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return gr.Accordion()
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@spaces.GPU(duration=60)
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def transcribe(seed, audio_path):
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if
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raise gr.Error("
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seed_everything(seed)
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]
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result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
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words = [word_info for segment in result["segments"] for word_info in segment["words"]]
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transcript = result["text"]
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transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
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transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
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return [
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gr.Dropdown(value=
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gr.Dropdown(value=
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gr.Dropdown(value=
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]
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@@ -106,12 +201,12 @@ def get_output_audio(audio_tensors, codec_audio_sr):
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@spaces.GPU(duration=90)
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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,
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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 (
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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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else:
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sentences = [transcript.replace("\n", " ")]
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device = "cuda" if torch.cuda.is_available() else "cpu"
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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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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
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if word["end"] < prompt_end_time:
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target_transcript += word["word"]
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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"]
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prompt_end_time = word["end"]
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break
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else:
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if smart_transcript:
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target_transcript = ""
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for word in
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if word["start"] < edit_start_time:
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target_transcript += word["word"]
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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
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if word["end"] > edit_end_time:
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target_transcript += word["word"]
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else:
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target_transcript = sentence
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@@ -188,7 +282,7 @@ def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p,
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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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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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]
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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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@@ -278,84 +372,52 @@ demo_original_transcript = " But when I had approached so near to them, the comm
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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
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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
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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
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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
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"regular": "But when I had approached so near to them, the common You can run
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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
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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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"0.64 approached 1.19",
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"1.22 so 1.58",
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"1.58 near 1.91",
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"1.91 to 2.07",
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"2.07 them 2.42",
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"2.53 the 2.61",
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"2.61 common 3.01",
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"3.05 object 3.62",
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"3.68 which 3.93",
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"3.93 the 4.02",
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"4.02 sense 4.34",
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"4.34 deceives 4.97",
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"5.04 lost 5.54",
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"5.54 not 6.00",
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"6.00 by 6.14",
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"6.14 distance 6.67",
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"6.79 any 7.05",
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"7.05 of 7.18",
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"7.18 its 7.34",
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"7.34 marks 7.87"
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]
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{"word": "which", "start": 3.68, "end": 3.93},
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{"word": "the", "start": 3.93, "end": 4.02},
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{"word": "sense", "start": 4.02, "end": 4.34},
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{"word": "deceives", "start": 4.34, "end": 4.97},
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{"word": "lost", "start": 5.04, "end": 5.54},
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{"word": "not", "start": 5.54, "end": 6.0},
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{"word": "by", "start": 6.0, "end": 6.14},
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{"word": "distance", "start": 6.14, "end": 6.67},
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{"word": "any", "start": 6.79, "end": 7.05},
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{"word": "of", "start": 7.05, "end": 7.18},
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{"word": "its", "start": 7.18, "end": 7.34},
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{"word": "marks", "start": 7.34, "end": 7.87}
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]
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def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
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if transcript not in all_demo_texts:
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return transcript, edit_from_word, edit_to_word
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replace_half = edit_word_mode == "Replace half"
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change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
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change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
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]
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with gr.
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with gr.
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with gr.
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with gr.
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voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
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whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
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choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
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with gr.Row():
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with gr.Column(scale=2):
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input_audio = gr.Audio(sources=["upload", "microphone"], value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
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with gr.Group():
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original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
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info="Use whisper model to get the transcript. Fix it if necessary.")
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with gr.Accordion("Word start time", open=False):
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transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
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with gr.Accordion("Word end time", open=False):
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transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
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transcribe_btn = gr.Button(value="Transcribe")
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with gr.Column(scale=3):
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with gr.Group():
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transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
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with gr.Row():
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smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
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with gr.Accordion(label="?", open=False):
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info = gr.Markdown(value=smart_transcript_info)
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with gr.Row():
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mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
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split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
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info="Split text into parts and run TTS for each part.", visible=False)
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edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
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info="What to do with first and last word", visible=False)
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with gr.Group() as tts_mode_controls:
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prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
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prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
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with gr.Group(visible=False) as edit_mode_controls:
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with gr.Row():
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with gr.Row():
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run_btn = gr.Button(value="Run")
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with gr.Column(scale=2):
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output_audio = gr.Audio(label="Output Audio")
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with gr.Accordion("Inference transcript", open=False):
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inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
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info="Inference was performed on this transcript.")
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with gr.Group(visible=False) as long_tts_sentence_editor:
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sentence_selector = gr.Dropdown(label="Sentence", value=None,
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info="Select sentence you want to regenerate")
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sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
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rerun_btn = gr.Button(value="Rerun")
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with gr.Row():
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with gr.Accordion("VoiceCraft config", open=False):
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seed = gr.Number(label="seed", value=-1, precision=0)
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left_margin = gr.Number(label="left_margin", value=0.08)
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right_margin = gr.Number(label="right_margin", value=0.08)
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codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
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codec_sr = gr.Number(label="codec_sr", value=50)
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top_k = gr.Number(label="top_k", value=0)
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top_p = gr.Number(label="top_p", value=0.8)
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temperature = gr.Number(label="temperature", value=1)
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stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
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info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
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sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
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info="generate this many samples and choose the shortest one")
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kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
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info="set to 0 to use less VRAM, but with slower inference")
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silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
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inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
466 |
outputs=[transcript, edit_from_word, edit_to_word])
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467 |
-
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-
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mode, prompt_end_time, edit_start_time, edit_end_time,
|
493 |
-
split_text, sentence_selector, audio_tensors
|
494 |
-
],
|
495 |
-
outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
|
496 |
-
|
497 |
-
sentence_selector.change(fn=load_sentence,
|
498 |
-
inputs=[sentence_selector, codec_audio_sr, audio_tensors],
|
499 |
-
outputs=[sentence_audio])
|
500 |
-
rerun_btn.click(fn=run,
|
501 |
inputs=[
|
502 |
seed, left_margin, right_margin,
|
503 |
codec_audio_sr, codec_sr,
|
504 |
top_k, top_p, temperature,
|
505 |
stop_repetition, sample_batch_size,
|
506 |
kvcache, silence_tokens,
|
507 |
-
input_audio,
|
508 |
-
|
509 |
split_text, sentence_selector, audio_tensors
|
510 |
],
|
511 |
-
outputs=[output_audio, inference_transcript,
|
512 |
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|
527 |
if __name__ == "__main__":
|
528 |
-
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|
1 |
import os
|
|
|
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|
2 |
import gradio as gr
|
3 |
import torch
|
4 |
import torchaudio
|
|
|
10 |
import io
|
11 |
import numpy as np
|
12 |
import random
|
13 |
+
import uuid
|
14 |
import spaces
|
15 |
|
16 |
|
17 |
+
DEMO_PATH = os.getenv("DEMO_PATH", "./demo")
|
18 |
+
TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
|
19 |
+
MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
|
20 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
21 |
+
whisper_model, align_model, voicecraft_model = None, None, None
|
22 |
+
|
23 |
+
|
24 |
+
def get_random_string():
|
25 |
+
return "".join(str(uuid.uuid4()).split("-"))
|
26 |
|
27 |
@spaces.GPU(duration=30)
|
28 |
def seed_everything(seed):
|
|
|
36 |
torch.backends.cudnn.deterministic = True
|
37 |
|
38 |
@spaces.GPU(duration=120)
|
39 |
+
class WhisperxAlignModel:
|
40 |
+
def __init__(self):
|
41 |
+
from whisperx import load_align_model
|
42 |
+
self.model, self.metadata = load_align_model(language_code="en", device=device)
|
43 |
+
|
44 |
+
def align(self, segments, audio_path):
|
45 |
+
from whisperx import align, load_audio
|
46 |
+
audio = load_audio(audio_path)
|
47 |
+
return align(segments, self.model, self.metadata, audio, device, return_char_alignments=False)["segments"]
|
48 |
+
|
49 |
+
@spaces.GPU(duration=120)
|
50 |
+
class WhisperModel:
|
51 |
+
def __init__(self, model_name):
|
52 |
+
from whisper import load_model
|
53 |
+
self.model = load_model(model_name, device)
|
54 |
|
|
|
|
|
55 |
from whisper.tokenizer import get_tokenizer
|
56 |
+
tokenizer = get_tokenizer(multilingual=False)
|
57 |
+
self.supress_tokens = [-1] + [
|
58 |
+
i
|
59 |
+
for i in range(tokenizer.eot)
|
60 |
+
if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
|
61 |
+
]
|
62 |
|
63 |
+
def transcribe(self, audio_path):
|
64 |
+
return self.model.transcribe(audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True)["segments"]
|
65 |
|
66 |
+
@spaces.GPU(duration=120)
|
67 |
+
class WhisperxModel:
|
68 |
+
def __init__(self, model_name, align_model: WhisperxAlignModel):
|
69 |
+
from whisperx import load_model
|
70 |
+
self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
|
71 |
+
self.align_model = align_model
|
72 |
+
|
73 |
+
def transcribe(self, audio_path):
|
74 |
+
segments = self.model.transcribe(audio_path, batch_size=8)["segments"]
|
75 |
+
return self.align_model.align(segments, audio_path)
|
76 |
+
|
77 |
+
@spaces.GPU(duration=120)
|
78 |
+
def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
|
79 |
+
global transcribe_model, align_model, voicecraft_model
|
80 |
+
|
81 |
+
if voicecraft_model_name == "giga330M_TTSEnhanced":
|
82 |
+
voicecraft_model_name = "gigaHalfLibri330M_TTSEnhanced_max16s"
|
83 |
+
|
84 |
+
if alignment_model_name is not None:
|
85 |
+
align_model = WhisperxAlignModel()
|
86 |
+
|
87 |
+
if whisper_model_name is not None:
|
88 |
+
if whisper_backend_name == "whisper":
|
89 |
+
transcribe_model = WhisperModel(whisper_model_name)
|
90 |
+
else:
|
91 |
+
if align_model is None:
|
92 |
+
raise gr.Error("Align model required for whisperx backend")
|
93 |
+
transcribe_model = WhisperxModel(whisper_model_name, align_model)
|
94 |
+
|
95 |
+
voicecraft_name = f"{voicecraft_model_name}.pth"
|
96 |
+
ckpt_fn = f"{MODELS_PATH}/{voicecraft_name}"
|
97 |
+
encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
|
98 |
if not os.path.exists(ckpt_fn):
|
99 |
os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
|
100 |
+
os.system(f"mv {voicecraft_name}\?download\=true {MODELS_PATH}/{voicecraft_name}")
|
101 |
if not os.path.exists(encodec_fn):
|
102 |
os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
|
103 |
+
os.system(f"mv encodec_4cb2048_giga.th {MODELS_PATH}/encodec_4cb2048_giga.th")
|
104 |
|
105 |
ckpt = torch.load(ckpt_fn, map_location="cpu")
|
106 |
model = voicecraft.VoiceCraft(ckpt["config"])
|
|
|
116 |
|
117 |
return gr.Accordion()
|
118 |
|
119 |
+
|
120 |
+
def get_transcribe_state(segments):
|
121 |
+
words_info = [word_info for segment in segments for word_info in segment["words"]]
|
122 |
+
return {
|
123 |
+
"segments": segments,
|
124 |
+
"transcript": " ".join([segment["text"] for segment in segments]),
|
125 |
+
"words_info": words_info,
|
126 |
+
"transcript_with_start_time": " ".join([f"{word['start']} {word['word']}" for word in words_info]),
|
127 |
+
"transcript_with_end_time": " ".join([f"{word['word']} {word['end']}" for word in words_info]),
|
128 |
+
"word_bounds": [f"{word['start']} {word['word']} {word['end']}" for word in words_info]
|
129 |
+
}
|
130 |
+
|
131 |
@spaces.GPU(duration=60)
|
132 |
def transcribe(seed, audio_path):
|
133 |
+
if transcribe_model is None:
|
134 |
+
raise gr.Error("Transcription model not loaded")
|
135 |
seed_everything(seed)
|
136 |
+
|
137 |
+
segments = transcribe_model.transcribe(audio_path)
|
138 |
+
state = get_transcribe_state(segments)
|
139 |
+
|
140 |
+
return [
|
141 |
+
state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
|
142 |
+
gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
|
143 |
+
gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
|
144 |
+
gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
|
145 |
+
state
|
146 |
]
|
|
|
|
|
|
|
|
|
|
|
|
|
147 |
|
148 |
+
|
149 |
+
def align_segments(transcript, audio_path):
|
150 |
+
from aeneas.executetask import ExecuteTask
|
151 |
+
from aeneas.task import Task
|
152 |
+
import json
|
153 |
+
config_string = 'task_language=eng|os_task_file_format=json|is_text_type=plain'
|
154 |
+
|
155 |
+
tmp_transcript_path = os.path.join(TMP_PATH, f"{get_random_string()}.txt")
|
156 |
+
tmp_sync_map_path = os.path.join(TMP_PATH, f"{get_random_string()}.json")
|
157 |
+
with open(tmp_transcript_path, "w") as f:
|
158 |
+
f.write(transcript)
|
159 |
+
|
160 |
+
task = Task(config_string=config_string)
|
161 |
+
task.audio_file_path_absolute = os.path.abspath(audio_path)
|
162 |
+
task.text_file_path_absolute = os.path.abspath(tmp_transcript_path)
|
163 |
+
task.sync_map_file_path_absolute = os.path.abspath(tmp_sync_map_path)
|
164 |
+
ExecuteTask(task).execute()
|
165 |
+
task.output_sync_map_file()
|
166 |
+
|
167 |
+
with open(tmp_sync_map_path, "r") as f:
|
168 |
+
return json.load(f)
|
169 |
+
|
170 |
+
@spaces.GPU(duration=90)
|
171 |
+
def align(seed, transcript, audio_path):
|
172 |
+
if align_model is None:
|
173 |
+
raise gr.Error("Align model not loaded")
|
174 |
+
seed_everything(seed)
|
175 |
+
|
176 |
+
fragments = align_segments(transcript, audio_path)
|
177 |
+
segments = [{
|
178 |
+
"start": float(fragment["begin"]),
|
179 |
+
"end": float(fragment["end"]),
|
180 |
+
"text": " ".join(fragment["lines"])
|
181 |
+
} for fragment in fragments["fragments"]]
|
182 |
+
segments = align_model.align(segments, audio_path)
|
183 |
+
state = get_transcribe_state(segments)
|
184 |
|
185 |
return [
|
186 |
+
state["transcript_with_start_time"], state["transcript_with_end_time"],
|
187 |
+
gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
|
188 |
+
gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
|
189 |
+
gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
|
190 |
+
state
|
191 |
]
|
192 |
|
193 |
|
|
|
201 |
@spaces.GPU(duration=90)
|
202 |
def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
|
203 |
stop_repetition, sample_batch_size, kvcache, silence_tokens,
|
204 |
+
audio_path, transcribe_state, transcript, smart_transcript,
|
205 |
mode, prompt_end_time, edit_start_time, edit_end_time,
|
206 |
split_text, selected_sentence, previous_audio_tensors):
|
207 |
if voicecraft_model is None:
|
208 |
raise gr.Error("VoiceCraft model not loaded")
|
209 |
+
if smart_transcript and (transcribe_state is None):
|
210 |
raise gr.Error("Can't use smart transcript: whisper transcript not found")
|
211 |
|
212 |
seed_everything(seed)
|
|
|
223 |
else:
|
224 |
sentences = [transcript.replace("\n", " ")]
|
225 |
|
|
|
226 |
info = torchaudio.info(audio_path)
|
227 |
audio_dur = info.num_frames / info.sample_rate
|
228 |
|
|
|
235 |
if mode != "Edit":
|
236 |
from inference_tts_scale import inference_one_sample
|
237 |
|
238 |
+
if smart_transcript:
|
239 |
target_transcript = ""
|
240 |
+
for word in transcribe_state["words_info"]:
|
241 |
if word["end"] < prompt_end_time:
|
242 |
+
target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
|
243 |
elif (word["start"] + word["end"]) / 2 < prompt_end_time:
|
244 |
# include part of the word it it's big, but adjust prompt_end_time
|
245 |
+
target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
|
246 |
prompt_end_time = word["end"]
|
247 |
break
|
248 |
else:
|
|
|
265 |
|
266 |
if smart_transcript:
|
267 |
target_transcript = ""
|
268 |
+
for word in transcribe_state["words_info"]:
|
269 |
if word["start"] < edit_start_time:
|
270 |
+
target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
|
271 |
else:
|
272 |
break
|
273 |
target_transcript += f" {sentence}"
|
274 |
+
for word in transcribe_state["words_info"]:
|
275 |
if word["end"] > edit_end_time:
|
276 |
+
target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
|
277 |
else:
|
278 |
target_transcript = sentence
|
279 |
|
|
|
282 |
morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
|
283 |
mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
|
284 |
mask_interval = torch.LongTensor(mask_interval)
|
285 |
+
|
286 |
_, gen_audio = inference_one_sample(voicecraft_model["model"],
|
287 |
voicecraft_model["ckpt"]["config"],
|
288 |
voicecraft_model["ckpt"]["phn2num"],
|
|
|
301 |
output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
|
302 |
sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
|
303 |
return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
|
304 |
+
|
305 |
+
|
306 |
def update_input_audio(audio_path):
|
307 |
if audio_path is None:
|
308 |
return 0, 0, 0
|
309 |
+
|
310 |
info = torchaudio.info(audio_path)
|
311 |
max_time = round(info.num_frames / info.sample_rate, 2)
|
312 |
return [
|
|
|
315 |
gr.Slider(maximum=max_time, value=max_time),
|
316 |
]
|
317 |
|
318 |
+
|
319 |
def change_mode(mode):
|
320 |
tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
|
321 |
return [
|
|
|
372 |
|
373 |
demo_text = {
|
374 |
"TTS": {
|
375 |
+
"smart": "I cannot believe that the same model can also do text to speech synthesis too!",
|
376 |
+
"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!"
|
377 |
},
|
378 |
"Edit": {
|
379 |
"smart": "saw the mirage of the lake in the distance,",
|
380 |
"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,"
|
381 |
},
|
382 |
"Long TTS": {
|
383 |
+
"smart": "You can run the model on a big text!\n"
|
384 |
"Just write it line-by-line. Or sentence-by-sentence.\n"
|
385 |
+
"If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
|
386 |
+
"regular": "But when I had approached so near to them, the common You can run the model on a big text!\n"
|
387 |
"But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
|
388 |
+
"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!"
|
389 |
}
|
390 |
}
|
391 |
|
392 |
all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
|
393 |
|
394 |
demo_words = [
|
395 |
+
'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',
|
396 |
+
'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',
|
397 |
+
'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',
|
398 |
+
'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'
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
399 |
]
|
400 |
|
401 |
+
demo_words_info = [
|
402 |
+
{'word': 'But', 'start': 0.029, 'end': 0.149, 'score': 0.834}, {'word': 'when', 'start': 0.189, 'end': 0.33, 'score': 0.879},
|
403 |
+
{'word': 'I', 'start': 0.43, 'end': 0.49, 'score': 0.984}, {'word': 'had', 'start': 0.53, 'end': 0.65, 'score': 0.998},
|
404 |
+
{'word': 'approached', 'start': 0.711, 'end': 1.152, 'score': 0.822}, {'word': 'so', 'start': 1.352, 'end': 1.593, 'score': 0.822},
|
405 |
+
{'word': 'near', 'start': 1.693, 'end': 1.933, 'score': 0.752}, {'word': 'to', 'start': 1.994, 'end': 2.074, 'score': 0.924},
|
406 |
+
{'word': 'them,', 'start': 2.134, 'end': 2.354, 'score': 0.914}, {'word': 'the', 'start': 2.535, 'end': 2.655, 'score': 0.818},
|
407 |
+
{'word': 'common', 'start': 2.695, 'end': 3.016, 'score': 0.971}, {'word': 'object,', 'start': 3.196, 'end': 3.577, 'score': 0.823},
|
408 |
+
{'word': 'which', 'start': 3.717, 'end': 3.898, 'score': 0.701}, {'word': 'the', 'start': 3.958, 'end': 4.058, 'score': 0.798},
|
409 |
+
{'word': 'sense', 'start': 4.098, 'end': 4.359, 'score': 0.797}, {'word': 'deceives,', 'start': 4.419, 'end': 4.92, 'score': 0.802},
|
410 |
+
{'word': 'lost', 'start': 5.101, 'end': 5.481, 'score': 0.71}, {'word': 'not', 'start': 5.682, 'end': 5.963, 'score': 0.781},
|
411 |
+
{'word': 'by', 'start': 6.043, 'end': 6.183, 'score': 0.834}, {'word': 'distance', 'start': 6.223, 'end': 6.644, 'score': 0.899},
|
412 |
+
{'word': 'any', 'start': 6.905, 'end': 7.065, 'score': 0.893}, {'word': 'of', 'start': 7.125, 'end': 7.185, 'score': 0.772},
|
413 |
+
{'word': 'its', 'start': 7.245, 'end': 7.346, 'score': 0.778}, {'word': 'marks.', 'start': 7.406, 'end': 7.727, 'score': 0.955}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
414 |
]
|
415 |
|
416 |
|
417 |
def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
|
418 |
if transcript not in all_demo_texts:
|
419 |
return transcript, edit_from_word, edit_to_word
|
420 |
+
|
421 |
replace_half = edit_word_mode == "Replace half"
|
422 |
change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
|
423 |
change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
|
|
|
430 |
]
|
431 |
|
432 |
|
433 |
+
def get_app():
|
434 |
+
with gr.Blocks() as app:
|
435 |
+
with gr.Row():
|
436 |
+
with gr.Column(scale=2):
|
437 |
+
load_models_btn = gr.Button(value="Load models")
|
438 |
+
with gr.Column(scale=5):
|
439 |
+
with gr.Accordion("Select models", open=False) as models_selector:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
440 |
with gr.Row():
|
441 |
+
voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M",
|
442 |
+
choices=["giga330M", "giga830M", "giga330M_TTSEnhanced"])
|
443 |
+
whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisper", "whisperX"])
|
444 |
+
whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
|
445 |
+
choices=[None, "base.en", "small.en", "medium.en", "large"])
|
446 |
+
align_model_choice = gr.Radio(label="Forced alignment model", value="whisperX", choices=[None, "whisperX"])
|
447 |
+
|
448 |
+
with gr.Row():
|
449 |
+
with gr.Column(scale=2):
|
450 |
+
input_audio = gr.Audio(value=f"{DEMO_PATH}/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
|
451 |
+
with gr.Group():
|
452 |
+
original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript,
|
453 |
+
info="Use whisper model to get the transcript. Fix and align it if necessary.")
|
454 |
+
with gr.Accordion("Word start time", open=False):
|
455 |
+
transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
|
456 |
+
with gr.Accordion("Word end time", open=False):
|
457 |
+
transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
|
458 |
+
|
459 |
+
transcribe_btn = gr.Button(value="Transcribe")
|
460 |
+
align_btn = gr.Button(value="Align")
|
461 |
+
|
462 |
+
with gr.Column(scale=3):
|
463 |
+
with gr.Group():
|
464 |
+
transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
|
465 |
with gr.Row():
|
466 |
+
smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
|
467 |
+
with gr.Accordion(label="?", open=False):
|
468 |
+
info = gr.Markdown(value=smart_transcript_info)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
469 |
|
470 |
+
with gr.Row():
|
471 |
+
mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
|
472 |
+
split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
|
473 |
+
info="Split text into parts and run TTS for each part.", visible=False)
|
474 |
+
edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
|
475 |
+
info="What to do with first and last word", visible=False)
|
476 |
+
|
477 |
+
with gr.Group() as tts_mode_controls:
|
478 |
+
prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
|
479 |
+
prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.001, value=3.016)
|
480 |
+
|
481 |
+
with gr.Group(visible=False) as edit_mode_controls:
|
482 |
+
with gr.Row():
|
483 |
+
edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
|
484 |
+
edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
|
485 |
+
with gr.Row():
|
486 |
+
edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.001, value=0.46)
|
487 |
+
edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.001, value=3.808)
|
488 |
+
|
489 |
+
run_btn = gr.Button(value="Run")
|
490 |
+
|
491 |
+
with gr.Column(scale=2):
|
492 |
+
output_audio = gr.Audio(label="Output Audio")
|
493 |
+
with gr.Accordion("Inference transcript", open=False):
|
494 |
+
inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
|
495 |
+
info="Inference was performed on this transcript.")
|
496 |
+
with gr.Group(visible=False) as long_tts_sentence_editor:
|
497 |
+
sentence_selector = gr.Dropdown(label="Sentence", value=None,
|
498 |
+
info="Select sentence you want to regenerate")
|
499 |
+
sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
|
500 |
+
rerun_btn = gr.Button(value="Rerun")
|
501 |
+
|
502 |
+
with gr.Row():
|
503 |
+
with gr.Accordion("Generation Parameters - change these if you are unhappy with the generation", open=False):
|
504 |
+
stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3, 4], value=3,
|
505 |
+
info="if there are long silence in the generated audio, reduce the stop_repetition to 2 or 1. -1 = disabled")
|
506 |
+
sample_batch_size = gr.Number(label="speech rate", value=4, precision=0,
|
507 |
+
info="The higher the number, the faster the output will be. "
|
508 |
+
"Under the hood, the model will generate this many samples and choose the shortest one. "
|
509 |
+
"For giga330M_TTSEnhanced, 1 or 2 should be fine since the model is trained to do TTS.")
|
510 |
+
seed = gr.Number(label="seed", value=-1, precision=0, info="random seeds always works :)")
|
511 |
+
kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
|
512 |
+
info="set to 0 to use less VRAM, but with slower inference")
|
513 |
+
left_margin = gr.Number(label="left_margin", value=0.08, info="margin to the left of the editing segment")
|
514 |
+
right_margin = gr.Number(label="right_margin", value=0.08, info="margin to the right of the editing segment")
|
515 |
+
top_p = gr.Number(label="top_p", value=0.9, info="0.9 is a good value, 0.8 is also good")
|
516 |
+
temperature = gr.Number(label="temperature", value=1, info="haven't try other values, do not recommend to change")
|
517 |
+
top_k = gr.Number(label="top_k", value=0, info="0 means we don't use topk sampling, because we use topp sampling")
|
518 |
+
codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000, info='encodec specific, Do not change')
|
519 |
+
codec_sr = gr.Number(label="codec_sr", value=50, info='encodec specific, Do not change')
|
520 |
+
silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]", info="encodec specific, do not change")
|
521 |
+
|
522 |
+
|
523 |
+
audio_tensors = gr.State()
|
524 |
+
transcribe_state = gr.State(value={"words_info": demo_words_info})
|
525 |
+
|
526 |
+
|
527 |
+
mode.change(fn=update_demo,
|
528 |
+
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
529 |
+
outputs=[transcript, edit_from_word, edit_to_word])
|
530 |
+
edit_word_mode.change(fn=update_demo,
|
531 |
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
532 |
outputs=[transcript, edit_from_word, edit_to_word])
|
533 |
+
smart_transcript.change(fn=update_demo,
|
534 |
+
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
535 |
+
outputs=[transcript, edit_from_word, edit_to_word])
|
536 |
+
|
537 |
+
load_models_btn.click(fn=load_models,
|
538 |
+
inputs=[whisper_backend_choice, whisper_model_choice, align_model_choice, voicecraft_model_choice],
|
539 |
+
outputs=[models_selector])
|
540 |
+
|
541 |
+
input_audio.upload(fn=update_input_audio,
|
542 |
+
inputs=[input_audio],
|
543 |
+
outputs=[prompt_end_time, edit_start_time, edit_end_time])
|
544 |
+
transcribe_btn.click(fn=transcribe,
|
545 |
+
inputs=[seed, input_audio],
|
546 |
+
outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
|
547 |
+
prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
|
548 |
+
align_btn.click(fn=align,
|
549 |
+
inputs=[seed, original_transcript, input_audio],
|
550 |
+
outputs=[transcript_with_start_time, transcript_with_end_time,
|
551 |
+
prompt_to_word, edit_from_word, edit_to_word, transcribe_state])
|
552 |
+
|
553 |
+
mode.change(fn=change_mode,
|
554 |
+
inputs=[mode],
|
555 |
+
outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
|
556 |
+
|
557 |
+
run_btn.click(fn=run,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
558 |
inputs=[
|
559 |
seed, left_margin, right_margin,
|
560 |
codec_audio_sr, codec_sr,
|
561 |
top_k, top_p, temperature,
|
562 |
stop_repetition, sample_batch_size,
|
563 |
kvcache, silence_tokens,
|
564 |
+
input_audio, transcribe_state, transcript, smart_transcript,
|
565 |
+
mode, prompt_end_time, edit_start_time, edit_end_time,
|
566 |
split_text, sentence_selector, audio_tensors
|
567 |
],
|
568 |
+
outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
|
569 |
+
|
570 |
+
sentence_selector.change(fn=load_sentence,
|
571 |
+
inputs=[sentence_selector, codec_audio_sr, audio_tensors],
|
572 |
+
outputs=[sentence_audio])
|
573 |
+
rerun_btn.click(fn=run,
|
574 |
+
inputs=[
|
575 |
+
seed, left_margin, right_margin,
|
576 |
+
codec_audio_sr, codec_sr,
|
577 |
+
top_k, top_p, temperature,
|
578 |
+
stop_repetition, sample_batch_size,
|
579 |
+
kvcache, silence_tokens,
|
580 |
+
input_audio, transcribe_state, transcript, smart_transcript,
|
581 |
+
gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
|
582 |
+
split_text, sentence_selector, audio_tensors
|
583 |
+
],
|
584 |
+
outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
|
585 |
+
|
586 |
+
prompt_to_word.change(fn=update_bound_word,
|
587 |
+
inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
|
588 |
+
outputs=[prompt_end_time])
|
589 |
+
edit_from_word.change(fn=update_bound_word,
|
590 |
+
inputs=[gr.State(True), edit_from_word, edit_word_mode],
|
591 |
+
outputs=[edit_start_time])
|
592 |
+
edit_to_word.change(fn=update_bound_word,
|
593 |
+
inputs=[gr.State(False), edit_to_word, edit_word_mode],
|
594 |
+
outputs=[edit_end_time])
|
595 |
+
edit_word_mode.change(fn=update_bound_words,
|
596 |
+
inputs=[edit_from_word, edit_to_word, edit_word_mode],
|
597 |
+
outputs=[edit_start_time, edit_end_time])
|
598 |
+
return app
|
599 |
|
600 |
|
601 |
if __name__ == "__main__":
|
602 |
+
import argparse
|
603 |
+
|
604 |
+
parser = argparse.ArgumentParser(description="VoiceCraft gradio app.")
|
605 |
+
|
606 |
+
parser.add_argument("--demo-path", default="./demo", help="Path to demo directory")
|
607 |
+
parser.add_argument("--tmp-path", default="./demo/temp", help="Path to tmp directory")
|
608 |
+
parser.add_argument("--models-path", default="./pretrained_models", help="Path to voicecraft models directory")
|
609 |
+
parser.add_argument("--port", default=7860, type=int, help="App port")
|
610 |
+
parser.add_argument("--share", action="store_true", help="Launch with public url")
|
611 |
+
|
612 |
+
os.environ["USER"] = os.getenv("USER", "user")
|
613 |
+
args = parser.parse_args()
|
614 |
+
DEMO_PATH = args.demo_path
|
615 |
+
TMP_PATH = args.tmp_path
|
616 |
+
MODELS_PATH = args.models_path
|
617 |
+
|
618 |
+
app = get_app()
|
619 |
+
app.queue().launch(share=args.share, server_port=args.port)
|
app_old.py
ADDED
@@ -0,0 +1,528 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
import os
|
2 |
+
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
|
3 |
+
# os.environ["CUDA_VISIBLE_DEVICES"] = "1" # these are only used if developping locally
|
4 |
+
import gradio as gr
|
5 |
+
import torch
|
6 |
+
import torchaudio
|
7 |
+
from data.tokenizer import (
|
8 |
+
AudioTokenizer,
|
9 |
+
TextTokenizer,
|
10 |
+
)
|
11 |
+
from models import voicecraft
|
12 |
+
import io
|
13 |
+
import numpy as np
|
14 |
+
import random
|
15 |
+
import spaces
|
16 |
+
|
17 |
+
|
18 |
+
whisper_model, voicecraft_model = None, None
|
19 |
+
|
20 |
+
@spaces.GPU(duration=30)
|
21 |
+
def seed_everything(seed):
|
22 |
+
if seed != -1:
|
23 |
+
os.environ['PYTHONHASHSEED'] = str(seed)
|
24 |
+
random.seed(seed)
|
25 |
+
np.random.seed(seed)
|
26 |
+
torch.manual_seed(seed)
|
27 |
+
torch.cuda.manual_seed(seed)
|
28 |
+
torch.backends.cudnn.benchmark = False
|
29 |
+
torch.backends.cudnn.deterministic = True
|
30 |
+
|
31 |
+
@spaces.GPU(duration=120)
|
32 |
+
def load_models(whisper_model_choice, voicecraft_model_choice):
|
33 |
+
global whisper_model, voicecraft_model
|
34 |
+
|
35 |
+
if whisper_model_choice is not None:
|
36 |
+
import whisper
|
37 |
+
from whisper.tokenizer import get_tokenizer
|
38 |
+
whisper_model = {
|
39 |
+
"model": whisper.load_model(whisper_model_choice),
|
40 |
+
"tokenizer": get_tokenizer(multilingual=False)
|
41 |
+
}
|
42 |
+
|
43 |
+
|
44 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
45 |
+
|
46 |
+
voicecraft_name = f"{voicecraft_model_choice}.pth"
|
47 |
+
ckpt_fn = f"./pretrained_models/{voicecraft_name}"
|
48 |
+
encodec_fn = "./pretrained_models/encodec_4cb2048_giga.th"
|
49 |
+
if not os.path.exists(ckpt_fn):
|
50 |
+
os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\?download\=true")
|
51 |
+
os.system(f"mv {voicecraft_name}\?download\=true ./pretrained_models/{voicecraft_name}")
|
52 |
+
if not os.path.exists(encodec_fn):
|
53 |
+
os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th")
|
54 |
+
os.system(f"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th")
|
55 |
+
|
56 |
+
ckpt = torch.load(ckpt_fn, map_location="cpu")
|
57 |
+
model = voicecraft.VoiceCraft(ckpt["config"])
|
58 |
+
model.load_state_dict(ckpt["model"])
|
59 |
+
model.to(device)
|
60 |
+
model.eval()
|
61 |
+
voicecraft_model = {
|
62 |
+
"ckpt": ckpt,
|
63 |
+
"model": model,
|
64 |
+
"text_tokenizer": TextTokenizer(backend="espeak"),
|
65 |
+
"audio_tokenizer": AudioTokenizer(signature=encodec_fn)
|
66 |
+
}
|
67 |
+
|
68 |
+
return gr.Accordion()
|
69 |
+
|
70 |
+
@spaces.GPU(duration=60)
|
71 |
+
def transcribe(seed, audio_path):
|
72 |
+
if whisper_model is None:
|
73 |
+
raise gr.Error("Whisper model not loaded")
|
74 |
+
seed_everything(seed)
|
75 |
+
|
76 |
+
number_tokens = [
|
77 |
+
i
|
78 |
+
for i in range(whisper_model["tokenizer"].eot)
|
79 |
+
if all(c in "0123456789" for c in whisper_model["tokenizer"].decode([i]).removeprefix(" "))
|
80 |
+
]
|
81 |
+
result = whisper_model["model"].transcribe(audio_path, suppress_tokens=[-1] + number_tokens, word_timestamps=True)
|
82 |
+
words = [word_info for segment in result["segments"] for word_info in segment["words"]]
|
83 |
+
|
84 |
+
transcript = result["text"]
|
85 |
+
transcript_with_start_time = " ".join([f"{word['start']} {word['word']}" for word in words])
|
86 |
+
transcript_with_end_time = " ".join([f"{word['word']} {word['end']}" for word in words])
|
87 |
+
|
88 |
+
choices = [f"{word['start']} {word['word']} {word['end']}" for word in words]
|
89 |
+
|
90 |
+
return [
|
91 |
+
transcript, transcript_with_start_time, transcript_with_end_time,
|
92 |
+
gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # prompt_to_word
|
93 |
+
gr.Dropdown(value=choices[0], choices=choices, interactive=True), # edit_from_word
|
94 |
+
gr.Dropdown(value=choices[-1], choices=choices, interactive=True), # edit_to_word
|
95 |
+
words
|
96 |
+
]
|
97 |
+
|
98 |
+
|
99 |
+
def get_output_audio(audio_tensors, codec_audio_sr):
|
100 |
+
result = torch.cat(audio_tensors, 1)
|
101 |
+
buffer = io.BytesIO()
|
102 |
+
torchaudio.save(buffer, result, int(codec_audio_sr), format="wav")
|
103 |
+
buffer.seek(0)
|
104 |
+
return buffer.read()
|
105 |
+
|
106 |
+
@spaces.GPU(duration=90)
|
107 |
+
def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
|
108 |
+
stop_repetition, sample_batch_size, kvcache, silence_tokens,
|
109 |
+
audio_path, word_info, transcript, smart_transcript,
|
110 |
+
mode, prompt_end_time, edit_start_time, edit_end_time,
|
111 |
+
split_text, selected_sentence, previous_audio_tensors):
|
112 |
+
if voicecraft_model is None:
|
113 |
+
raise gr.Error("VoiceCraft model not loaded")
|
114 |
+
if smart_transcript and (word_info is None):
|
115 |
+
raise gr.Error("Can't use smart transcript: whisper transcript not found")
|
116 |
+
|
117 |
+
seed_everything(seed)
|
118 |
+
if mode == "Long TTS":
|
119 |
+
if split_text == "Newline":
|
120 |
+
sentences = transcript.split('\n')
|
121 |
+
else:
|
122 |
+
from nltk.tokenize import sent_tokenize
|
123 |
+
sentences = sent_tokenize(transcript.replace("\n", " "))
|
124 |
+
elif mode == "Rerun":
|
125 |
+
colon_position = selected_sentence.find(':')
|
126 |
+
selected_sentence_idx = int(selected_sentence[:colon_position])
|
127 |
+
sentences = [selected_sentence[colon_position + 1:]]
|
128 |
+
else:
|
129 |
+
sentences = [transcript.replace("\n", " ")]
|
130 |
+
|
131 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
132 |
+
info = torchaudio.info(audio_path)
|
133 |
+
audio_dur = info.num_frames / info.sample_rate
|
134 |
+
|
135 |
+
audio_tensors = []
|
136 |
+
inference_transcript = ""
|
137 |
+
for sentence in sentences:
|
138 |
+
decode_config = {"top_k": top_k, "top_p": top_p, "temperature": temperature, "stop_repetition": stop_repetition,
|
139 |
+
"kvcache": kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr,
|
140 |
+
"silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
|
141 |
+
if mode != "Edit":
|
142 |
+
from inference_tts_scale import inference_one_sample
|
143 |
+
|
144 |
+
if smart_transcript:
|
145 |
+
target_transcript = ""
|
146 |
+
for word in word_info:
|
147 |
+
if word["end"] < prompt_end_time:
|
148 |
+
target_transcript += word["word"]
|
149 |
+
elif (word["start"] + word["end"]) / 2 < prompt_end_time:
|
150 |
+
# include part of the word it it's big, but adjust prompt_end_time
|
151 |
+
target_transcript += word["word"]
|
152 |
+
prompt_end_time = word["end"]
|
153 |
+
break
|
154 |
+
else:
|
155 |
+
break
|
156 |
+
target_transcript += f" {sentence}"
|
157 |
+
else:
|
158 |
+
target_transcript = sentence
|
159 |
+
|
160 |
+
inference_transcript += target_transcript + "\n"
|
161 |
+
|
162 |
+
prompt_end_frame = int(min(audio_dur, prompt_end_time) * info.sample_rate)
|
163 |
+
_, gen_audio = inference_one_sample(voicecraft_model["model"],
|
164 |
+
voicecraft_model["ckpt"]["config"],
|
165 |
+
voicecraft_model["ckpt"]["phn2num"],
|
166 |
+
voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
|
167 |
+
audio_path, target_transcript, device, decode_config,
|
168 |
+
prompt_end_frame)
|
169 |
+
else:
|
170 |
+
from inference_speech_editing_scale import inference_one_sample
|
171 |
+
|
172 |
+
if smart_transcript:
|
173 |
+
target_transcript = ""
|
174 |
+
for word in word_info:
|
175 |
+
if word["start"] < edit_start_time:
|
176 |
+
target_transcript += word["word"]
|
177 |
+
else:
|
178 |
+
break
|
179 |
+
target_transcript += f" {sentence}"
|
180 |
+
for word in word_info:
|
181 |
+
if word["end"] > edit_end_time:
|
182 |
+
target_transcript += word["word"]
|
183 |
+
else:
|
184 |
+
target_transcript = sentence
|
185 |
+
|
186 |
+
inference_transcript += target_transcript + "\n"
|
187 |
+
|
188 |
+
morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
|
189 |
+
mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
|
190 |
+
mask_interval = torch.LongTensor(mask_interval)
|
191 |
+
|
192 |
+
_, gen_audio = inference_one_sample(voicecraft_model["model"],
|
193 |
+
voicecraft_model["ckpt"]["config"],
|
194 |
+
voicecraft_model["ckpt"]["phn2num"],
|
195 |
+
voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
|
196 |
+
audio_path, target_transcript, mask_interval, device, decode_config)
|
197 |
+
gen_audio = gen_audio[0].cpu()
|
198 |
+
audio_tensors.append(gen_audio)
|
199 |
+
|
200 |
+
if mode != "Rerun":
|
201 |
+
output_audio = get_output_audio(audio_tensors, codec_audio_sr)
|
202 |
+
sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
|
203 |
+
component = gr.Dropdown(choices=sentences, value=sentences[0])
|
204 |
+
return output_audio, inference_transcript, component, audio_tensors
|
205 |
+
else:
|
206 |
+
previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
|
207 |
+
output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
|
208 |
+
sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
|
209 |
+
return output_audio, inference_transcript, sentence_audio, previous_audio_tensors
|
210 |
+
|
211 |
+
|
212 |
+
def update_input_audio(audio_path):
|
213 |
+
if audio_path is None:
|
214 |
+
return 0, 0, 0
|
215 |
+
|
216 |
+
info = torchaudio.info(audio_path)
|
217 |
+
max_time = round(info.num_frames / info.sample_rate, 2)
|
218 |
+
return [
|
219 |
+
gr.Slider(maximum=max_time, value=max_time),
|
220 |
+
gr.Slider(maximum=max_time, value=0),
|
221 |
+
gr.Slider(maximum=max_time, value=max_time),
|
222 |
+
]
|
223 |
+
|
224 |
+
|
225 |
+
def change_mode(mode):
|
226 |
+
tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
|
227 |
+
return [
|
228 |
+
gr.Group(visible=mode != "Edit"),
|
229 |
+
gr.Group(visible=mode == "Edit"),
|
230 |
+
gr.Radio(visible=mode == "Edit"),
|
231 |
+
gr.Radio(visible=mode == "Long TTS"),
|
232 |
+
gr.Group(visible=mode == "Long TTS"),
|
233 |
+
]
|
234 |
+
|
235 |
+
|
236 |
+
def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
|
237 |
+
if selected_sentence is None:
|
238 |
+
return None
|
239 |
+
colon_position = selected_sentence.find(':')
|
240 |
+
selected_sentence_idx = int(selected_sentence[:colon_position])
|
241 |
+
return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)
|
242 |
+
|
243 |
+
|
244 |
+
def update_bound_word(is_first_word, selected_word, edit_word_mode):
|
245 |
+
if selected_word is None:
|
246 |
+
return None
|
247 |
+
|
248 |
+
word_start_time = float(selected_word.split(' ')[0])
|
249 |
+
word_end_time = float(selected_word.split(' ')[-1])
|
250 |
+
if edit_word_mode == "Replace half":
|
251 |
+
bound_time = (word_start_time + word_end_time) / 2
|
252 |
+
elif is_first_word:
|
253 |
+
bound_time = word_start_time
|
254 |
+
else:
|
255 |
+
bound_time = word_end_time
|
256 |
+
|
257 |
+
return bound_time
|
258 |
+
|
259 |
+
|
260 |
+
def update_bound_words(from_selected_word, to_selected_word, edit_word_mode):
|
261 |
+
return [
|
262 |
+
update_bound_word(True, from_selected_word, edit_word_mode),
|
263 |
+
update_bound_word(False, to_selected_word, edit_word_mode),
|
264 |
+
]
|
265 |
+
|
266 |
+
|
267 |
+
smart_transcript_info = """
|
268 |
+
If enabled, the target transcript will be constructed for you:</br>
|
269 |
+
- In TTS and Long TTS mode just write the text you want to synthesize.</br>
|
270 |
+
- In Edit mode just write the text to replace selected editing segment.</br>
|
271 |
+
If disabled, you should write the target transcript yourself:</br>
|
272 |
+
- In TTS mode write prompt transcript followed by generation transcript.</br>
|
273 |
+
- In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
|
274 |
+
- In Edit mode write full prompt</br>
|
275 |
+
"""
|
276 |
+
|
277 |
+
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."
|
278 |
+
|
279 |
+
demo_text = {
|
280 |
+
"TTS": {
|
281 |
+
"smart": "I cannot believe that the same model can also do text to speech synthesis as well!",
|
282 |
+
"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!"
|
283 |
+
},
|
284 |
+
"Edit": {
|
285 |
+
"smart": "saw the mirage of the lake in the distance,",
|
286 |
+
"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,"
|
287 |
+
},
|
288 |
+
"Long TTS": {
|
289 |
+
"smart": "You can run generation on a big text!\n"
|
290 |
+
"Just write it line-by-line. Or sentence-by-sentence.\n"
|
291 |
+
"If some sentences sound odd, just rerun generation on them, no need to generate the whole text again!",
|
292 |
+
"regular": "But when I had approached so near to them, the common You can run generation on a big text!\n"
|
293 |
+
"But when I had approached so near to them, the common Just write it line-by-line. Or sentence-by-sentence.\n"
|
294 |
+
"But when I had approached so near to them, the common If some sentences sound odd, just rerun generation on them, no need to generate the whole text again!"
|
295 |
+
}
|
296 |
+
}
|
297 |
+
|
298 |
+
all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}
|
299 |
+
|
300 |
+
demo_words = [
|
301 |
+
"0.03 but 0.18",
|
302 |
+
"0.18 when 0.32",
|
303 |
+
"0.32 i 0.48",
|
304 |
+
"0.48 had 0.64",
|
305 |
+
"0.64 approached 1.19",
|
306 |
+
"1.22 so 1.58",
|
307 |
+
"1.58 near 1.91",
|
308 |
+
"1.91 to 2.07",
|
309 |
+
"2.07 them 2.42",
|
310 |
+
"2.53 the 2.61",
|
311 |
+
"2.61 common 3.01",
|
312 |
+
"3.05 object 3.62",
|
313 |
+
"3.68 which 3.93",
|
314 |
+
"3.93 the 4.02",
|
315 |
+
"4.02 sense 4.34",
|
316 |
+
"4.34 deceives 4.97",
|
317 |
+
"5.04 lost 5.54",
|
318 |
+
"5.54 not 6.00",
|
319 |
+
"6.00 by 6.14",
|
320 |
+
"6.14 distance 6.67",
|
321 |
+
"6.79 any 7.05",
|
322 |
+
"7.05 of 7.18",
|
323 |
+
"7.18 its 7.34",
|
324 |
+
"7.34 marks 7.87"
|
325 |
+
]
|
326 |
+
|
327 |
+
demo_word_info = [
|
328 |
+
{"word": "but", "start": 0.03, "end": 0.18},
|
329 |
+
{"word": "when", "start": 0.18, "end": 0.32},
|
330 |
+
{"word": "i", "start": 0.32, "end": 0.48},
|
331 |
+
{"word": "had", "start": 0.48, "end": 0.64},
|
332 |
+
{"word": "approached", "start": 0.64, "end": 1.19},
|
333 |
+
{"word": "so", "start": 1.22, "end": 1.58},
|
334 |
+
{"word": "near", "start": 1.58, "end": 1.91},
|
335 |
+
{"word": "to", "start": 1.91, "end": 2.07},
|
336 |
+
{"word": "them", "start": 2.07, "end": 2.42},
|
337 |
+
{"word": "the", "start": 2.53, "end": 2.61},
|
338 |
+
{"word": "common", "start": 2.61, "end": 3.01},
|
339 |
+
{"word": "object", "start": 3.05, "end": 3.62},
|
340 |
+
{"word": "which", "start": 3.68, "end": 3.93},
|
341 |
+
{"word": "the", "start": 3.93, "end": 4.02},
|
342 |
+
{"word": "sense", "start": 4.02, "end": 4.34},
|
343 |
+
{"word": "deceives", "start": 4.34, "end": 4.97},
|
344 |
+
{"word": "lost", "start": 5.04, "end": 5.54},
|
345 |
+
{"word": "not", "start": 5.54, "end": 6.0},
|
346 |
+
{"word": "by", "start": 6.0, "end": 6.14},
|
347 |
+
{"word": "distance", "start": 6.14, "end": 6.67},
|
348 |
+
{"word": "any", "start": 6.79, "end": 7.05},
|
349 |
+
{"word": "of", "start": 7.05, "end": 7.18},
|
350 |
+
{"word": "its", "start": 7.18, "end": 7.34},
|
351 |
+
{"word": "marks", "start": 7.34, "end": 7.87}
|
352 |
+
]
|
353 |
+
|
354 |
+
|
355 |
+
def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
|
356 |
+
if transcript not in all_demo_texts:
|
357 |
+
return transcript, edit_from_word, edit_to_word
|
358 |
+
|
359 |
+
replace_half = edit_word_mode == "Replace half"
|
360 |
+
change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
|
361 |
+
change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
|
362 |
+
demo_edit_from_word_value = demo_words[2] if replace_half else demo_words[3]
|
363 |
+
demo_edit_to_word_value = demo_words[12] if replace_half else demo_words[11]
|
364 |
+
return [
|
365 |
+
demo_text[mode]["smart" if smart_transcript else "regular"],
|
366 |
+
demo_edit_from_word_value if change_edit_from_word else edit_from_word,
|
367 |
+
demo_edit_to_word_value if change_edit_to_word else edit_to_word,
|
368 |
+
]
|
369 |
+
|
370 |
+
|
371 |
+
with gr.Blocks() as app:
|
372 |
+
with gr.Row():
|
373 |
+
with gr.Column(scale=2):
|
374 |
+
load_models_btn = gr.Button(value="Load models")
|
375 |
+
with gr.Column(scale=5):
|
376 |
+
with gr.Accordion("Select models", open=False) as models_selector:
|
377 |
+
with gr.Row():
|
378 |
+
voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="giga830M", choices=["giga330M", "giga830M"])
|
379 |
+
whisper_model_choice = gr.Radio(label="Whisper model", value="base.en",
|
380 |
+
choices=[None, "tiny.en", "base.en", "small.en", "medium.en", "large"])
|
381 |
+
|
382 |
+
with gr.Row():
|
383 |
+
with gr.Column(scale=2):
|
384 |
+
input_audio = gr.Audio(sources=["upload", "microphone"], value="./demo/84_121550_000074_000000.wav", label="Input Audio", type="filepath", interactive=True)
|
385 |
+
with gr.Group():
|
386 |
+
original_transcript = gr.Textbox(label="Original transcript", lines=5, value=demo_original_transcript, interactive=False,
|
387 |
+
info="Use whisper model to get the transcript. Fix it if necessary.")
|
388 |
+
with gr.Accordion("Word start time", open=False):
|
389 |
+
transcript_with_start_time = gr.Textbox(label="Start time", lines=5, interactive=False, info="Start time before each word")
|
390 |
+
with gr.Accordion("Word end time", open=False):
|
391 |
+
transcript_with_end_time = gr.Textbox(label="End time", lines=5, interactive=False, info="End time after each word")
|
392 |
+
|
393 |
+
transcribe_btn = gr.Button(value="Transcribe")
|
394 |
+
|
395 |
+
with gr.Column(scale=3):
|
396 |
+
with gr.Group():
|
397 |
+
transcript = gr.Textbox(label="Text", lines=7, value=demo_text["TTS"]["smart"])
|
398 |
+
with gr.Row():
|
399 |
+
smart_transcript = gr.Checkbox(label="Smart transcript", value=True)
|
400 |
+
with gr.Accordion(label="?", open=False):
|
401 |
+
info = gr.Markdown(value=smart_transcript_info)
|
402 |
+
|
403 |
+
with gr.Row():
|
404 |
+
mode = gr.Radio(label="Mode", choices=["TTS", "Edit", "Long TTS"], value="TTS")
|
405 |
+
split_text = gr.Radio(label="Split text", choices=["Newline", "Sentence"], value="Newline",
|
406 |
+
info="Split text into parts and run TTS for each part.", visible=False)
|
407 |
+
edit_word_mode = gr.Radio(label="Edit word mode", choices=["Replace half", "Replace all"], value="Replace half",
|
408 |
+
info="What to do with first and last word", visible=False)
|
409 |
+
|
410 |
+
with gr.Group() as tts_mode_controls:
|
411 |
+
prompt_to_word = gr.Dropdown(label="Last word in prompt", choices=demo_words, value=demo_words[10], interactive=True)
|
412 |
+
prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.93, step=0.01, value=3.01)
|
413 |
+
|
414 |
+
with gr.Group(visible=False) as edit_mode_controls:
|
415 |
+
with gr.Row():
|
416 |
+
edit_from_word = gr.Dropdown(label="First word to edit", choices=demo_words, value=demo_words[2], interactive=True)
|
417 |
+
edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[12], interactive=True)
|
418 |
+
with gr.Row():
|
419 |
+
edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.93, step=0.01, value=0.35)
|
420 |
+
edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.93, step=0.01, value=3.75)
|
421 |
+
|
422 |
+
run_btn = gr.Button(value="Run")
|
423 |
+
|
424 |
+
with gr.Column(scale=2):
|
425 |
+
output_audio = gr.Audio(label="Output Audio")
|
426 |
+
with gr.Accordion("Inference transcript", open=False):
|
427 |
+
inference_transcript = gr.Textbox(label="Inference transcript", lines=5, interactive=False,
|
428 |
+
info="Inference was performed on this transcript.")
|
429 |
+
with gr.Group(visible=False) as long_tts_sentence_editor:
|
430 |
+
sentence_selector = gr.Dropdown(label="Sentence", value=None,
|
431 |
+
info="Select sentence you want to regenerate")
|
432 |
+
sentence_audio = gr.Audio(label="Sentence Audio", scale=2)
|
433 |
+
rerun_btn = gr.Button(value="Rerun")
|
434 |
+
|
435 |
+
with gr.Row():
|
436 |
+
with gr.Accordion("VoiceCraft config", open=False):
|
437 |
+
seed = gr.Number(label="seed", value=-1, precision=0)
|
438 |
+
left_margin = gr.Number(label="left_margin", value=0.08)
|
439 |
+
right_margin = gr.Number(label="right_margin", value=0.08)
|
440 |
+
codec_audio_sr = gr.Number(label="codec_audio_sr", value=16000)
|
441 |
+
codec_sr = gr.Number(label="codec_sr", value=50)
|
442 |
+
top_k = gr.Number(label="top_k", value=0)
|
443 |
+
top_p = gr.Number(label="top_p", value=0.8)
|
444 |
+
temperature = gr.Number(label="temperature", value=1)
|
445 |
+
stop_repetition = gr.Radio(label="stop_repetition", choices=[-1, 1, 2, 3], value=3,
|
446 |
+
info="if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1, -1 = disabled")
|
447 |
+
sample_batch_size = gr.Number(label="sample_batch_size", value=4, precision=0,
|
448 |
+
info="generate this many samples and choose the shortest one")
|
449 |
+
kvcache = gr.Radio(label="kvcache", choices=[0, 1], value=1,
|
450 |
+
info="set to 0 to use less VRAM, but with slower inference")
|
451 |
+
silence_tokens = gr.Textbox(label="silence tokens", value="[1388,1898,131]")
|
452 |
+
|
453 |
+
|
454 |
+
audio_tensors = gr.State()
|
455 |
+
word_info = gr.State(value=demo_word_info)
|
456 |
+
|
457 |
+
|
458 |
+
mode.change(fn=update_demo,
|
459 |
+
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
460 |
+
outputs=[transcript, edit_from_word, edit_to_word])
|
461 |
+
edit_word_mode.change(fn=update_demo,
|
462 |
+
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
463 |
+
outputs=[transcript, edit_from_word, edit_to_word])
|
464 |
+
smart_transcript.change(fn=update_demo,
|
465 |
+
inputs=[mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word],
|
466 |
+
outputs=[transcript, edit_from_word, edit_to_word])
|
467 |
+
|
468 |
+
load_models_btn.click(fn=load_models,
|
469 |
+
inputs=[whisper_model_choice, voicecraft_model_choice],
|
470 |
+
outputs=[models_selector])
|
471 |
+
|
472 |
+
input_audio.change(fn=update_input_audio,
|
473 |
+
inputs=[input_audio],
|
474 |
+
outputs=[prompt_end_time, edit_start_time, edit_end_time])
|
475 |
+
transcribe_btn.click(fn=transcribe,
|
476 |
+
inputs=[seed, input_audio],
|
477 |
+
outputs=[original_transcript, transcript_with_start_time, transcript_with_end_time,
|
478 |
+
prompt_to_word, edit_from_word, edit_to_word, word_info])
|
479 |
+
|
480 |
+
mode.change(fn=change_mode,
|
481 |
+
inputs=[mode],
|
482 |
+
outputs=[tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor])
|
483 |
+
|
484 |
+
run_btn.click(fn=run,
|
485 |
+
inputs=[
|
486 |
+
seed, left_margin, right_margin,
|
487 |
+
codec_audio_sr, codec_sr,
|
488 |
+
top_k, top_p, temperature,
|
489 |
+
stop_repetition, sample_batch_size,
|
490 |
+
kvcache, silence_tokens,
|
491 |
+
input_audio, word_info, transcript, smart_transcript,
|
492 |
+
mode, prompt_end_time, edit_start_time, edit_end_time,
|
493 |
+
split_text, sentence_selector, audio_tensors
|
494 |
+
],
|
495 |
+
outputs=[output_audio, inference_transcript, sentence_selector, audio_tensors])
|
496 |
+
|
497 |
+
sentence_selector.change(fn=load_sentence,
|
498 |
+
inputs=[sentence_selector, codec_audio_sr, audio_tensors],
|
499 |
+
outputs=[sentence_audio])
|
500 |
+
rerun_btn.click(fn=run,
|
501 |
+
inputs=[
|
502 |
+
seed, left_margin, right_margin,
|
503 |
+
codec_audio_sr, codec_sr,
|
504 |
+
top_k, top_p, temperature,
|
505 |
+
stop_repetition, sample_batch_size,
|
506 |
+
kvcache, silence_tokens,
|
507 |
+
input_audio, word_info, transcript, smart_transcript,
|
508 |
+
gr.State(value="Rerun"), prompt_end_time, edit_start_time, edit_end_time,
|
509 |
+
split_text, sentence_selector, audio_tensors
|
510 |
+
],
|
511 |
+
outputs=[output_audio, inference_transcript, sentence_audio, audio_tensors])
|
512 |
+
|
513 |
+
prompt_to_word.change(fn=update_bound_word,
|
514 |
+
inputs=[gr.State(False), prompt_to_word, gr.State("Replace all")],
|
515 |
+
outputs=[prompt_end_time])
|
516 |
+
edit_from_word.change(fn=update_bound_word,
|
517 |
+
inputs=[gr.State(True), edit_from_word, edit_word_mode],
|
518 |
+
outputs=[edit_start_time])
|
519 |
+
edit_to_word.change(fn=update_bound_word,
|
520 |
+
inputs=[gr.State(False), edit_to_word, edit_word_mode],
|
521 |
+
outputs=[edit_end_time])
|
522 |
+
edit_word_mode.change(fn=update_bound_words,
|
523 |
+
inputs=[edit_from_word, edit_to_word, edit_word_mode],
|
524 |
+
outputs=[edit_start_time, edit_end_time])
|
525 |
+
|
526 |
+
|
527 |
+
if __name__ == "__main__":
|
528 |
+
app.launch()
|