app.py

import os
import gradio as gr
import torch
import torchaudio
from data.tokenizer import (
    AudioTokenizer,
    TextTokenizer,
)
from models import voicecraft
import io
import numpy as np
import random
import uuid
import spaces

import re
from num2words import num2words

DEMO_PATH = os.getenv("DEMO_PATH", "./demo")
TMP_PATH = os.getenv("TMP_PATH", "./demo/temp")
MODELS_PATH = os.getenv("MODELS_PATH", "./pretrained_models")
device = "cuda" if torch.cuda.is_available() else "cpu"
whisper_model, align_model, voicecraft_model = None, None, None


def get_random_string():
    return "".join(str(uuid.uuid4()).split("-"))

@spaces.GPU(duration=30)
def seed_everything(seed):
    if seed != -1:
        os.environ['PYTHONHASHSEED'] = str(seed)
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        torch.backends.cudnn.benchmark = False
        torch.backends.cudnn.deterministic = True

@spaces.GPU(duration=120)
class WhisperxAlignModel:
    def __init__(self):
        from whisperx import load_align_model
        self.model, self.metadata = load_align_model(language_code="en", device=device)

    def align(self, segments, audio_path):
        from whisperx import align, load_audio
        audio = load_audio(audio_path)
        return align(segments, self.model, self.metadata, audio, device, return_char_alignments=False)["segments"]

@spaces.GPU(duration=120)
class WhisperModel:
    def __init__(self, model_name):
        from whisper import load_model
        self.model = load_model(model_name, device)

        from whisper.tokenizer import get_tokenizer
        tokenizer = get_tokenizer(multilingual=False)
        self.supress_tokens = [-1] + [
            i
            for i in range(tokenizer.eot)
            if all(c in "0123456789" for c in tokenizer.decode([i]).removeprefix(" "))
        ]

    def transcribe(self, audio_path):
        return self.model.transcribe(audio_path, suppress_tokens=self.supress_tokens, word_timestamps=True)["segments"]

@spaces.GPU(duration=120)
class WhisperxModel:
    def __init__(self, model_name, align_model: WhisperxAlignModel):
        from whisperx import load_model
        self.model = load_model(model_name, device, asr_options={"suppress_numerals": True, "max_new_tokens": None, "clip_timestamps": None, "hallucination_silence_threshold": None})
        self.align_model = align_model

    def transcribe(self, audio_path):
        segments = self.model.transcribe(audio_path, batch_size=8)["segments"]
        return self.align_model.align(segments, audio_path)

@spaces.GPU(duration=120)
def load_models(whisper_backend_name, whisper_model_name, alignment_model_name, voicecraft_model_name):
    global transcribe_model, align_model, voicecraft_model

    if voicecraft_model_name == "330M":
        voicecraft_model_name = "giga330M"
    elif voicecraft_model_name == "830M":
        voicecraft_model_name = "giga830M"
    elif voicecraft_model_name == "330M_TTSEnhanced":
        voicecraft_model_name = "330M_TTSEnhanced"
    elif voicecraft_model_name == "830M_TTSEnhanced":
        voicecraft_model_name = "830M_TTSEnhanced"
    if alignment_model_name is not None:
        align_model = WhisperxAlignModel()

    if whisper_model_name is not None:
        if whisper_backend_name == "whisper":
            transcribe_model = WhisperModel(whisper_model_name)
        else:
            if align_model is None:
                raise gr.Error("Align model required for whisperx backend")
            transcribe_model = WhisperxModel(whisper_model_name, align_model)

    voicecraft_name = f"{voicecraft_model_name}.pth"
    model = voicecraft.VoiceCraft.from_pretrained(f"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}")
    phn2num = model.args.phn2num
    config = model.args
    model.to(device)

    encodec_fn = f"{MODELS_PATH}/encodec_4cb2048_giga.th"
    if not os.path.exists(encodec_fn):
        os.system(f"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th -O " + encodec_fn)

    voicecraft_model = {
        "config": config,
        "phn2num": phn2num,
        "model": model,
        "text_tokenizer": TextTokenizer(backend="espeak"),
        "audio_tokenizer": AudioTokenizer(signature=encodec_fn)
    }
    return gr.Accordion()


def get_transcribe_state(segments):
    words_info = [word_info for segment in segments for word_info in segment["words"]]
    transcript = " ".join([segment["text"] for segment in segments])
    transcript = transcript[1:] if transcript[0] == " " else transcript
    return {
        "segments": segments,
        "transcript": transcript,
        "words_info": words_info,
        "transcript_with_start_time": " ".join([f"{word['start']} {word['word']}" for word in words_info]),
        "transcript_with_end_time": " ".join([f"{word['word']} {word['end']}" for word in words_info]),
        "word_bounds": [f"{word['start']} {word['word']} {word['end']}" for word in words_info]
    }

@spaces.GPU(duration=60)
def transcribe(seed, audio_path):
    if transcribe_model is None:
        raise gr.Error("Transcription model not loaded")
    seed_everything(seed)

    segments = transcribe_model.transcribe(audio_path)
    state = get_transcribe_state(segments)

    return [
        state["transcript"], state["transcript_with_start_time"], state["transcript_with_end_time"],
        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
        state
    ]

@spaces.GPU(duration=60)
def align_segments(transcript, audio_path):
    from aeneas.executetask import ExecuteTask
    from aeneas.task import Task
    import json
    config_string = 'task_language=eng|os_task_file_format=json|is_text_type=plain'

    tmp_transcript_path = os.path.join(TMP_PATH, f"{get_random_string()}.txt")
    tmp_sync_map_path = os.path.join(TMP_PATH, f"{get_random_string()}.json")
    with open(tmp_transcript_path, "w") as f:
        f.write(transcript)

    task = Task(config_string=config_string)
    task.audio_file_path_absolute = os.path.abspath(audio_path)
    task.text_file_path_absolute = os.path.abspath(tmp_transcript_path)
    task.sync_map_file_path_absolute = os.path.abspath(tmp_sync_map_path)
    ExecuteTask(task).execute()
    task.output_sync_map_file()

    with open(tmp_sync_map_path, "r") as f:
        return json.load(f)

@spaces.GPU(duration=90)
def align(seed, transcript, audio_path):
    if align_model is None:
        raise gr.Error("Align model not loaded")
    seed_everything(seed)

    fragments = align_segments(transcript, audio_path)
    segments = [{
        "start": float(fragment["begin"]),
        "end": float(fragment["end"]),
        "text": " ".join(fragment["lines"])
    } for fragment in fragments["fragments"]]
    segments = align_model.align(segments, audio_path)
    state = get_transcribe_state(segments)

    return [
        state["transcript_with_start_time"], state["transcript_with_end_time"],
        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # prompt_to_word
        gr.Dropdown(value=state["word_bounds"][0], choices=state["word_bounds"], interactive=True), # edit_from_word
        gr.Dropdown(value=state["word_bounds"][-1], choices=state["word_bounds"], interactive=True), # edit_to_word
        state
    ]


def get_output_audio(audio_tensors, codec_audio_sr):
    result = torch.cat(audio_tensors, 1)
    buffer = io.BytesIO()
    torchaudio.save(buffer, result, int(codec_audio_sr), format="wav")
    buffer.seek(0)
    return buffer.read()

def replace_numbers_with_words(sentence):
    sentence = re.sub(r'(\d+)', r' \1 ', sentence) # add spaces around numbers
    def replace_with_words(match):
        num = match.group(0)
        try:
            return num2words(num) # Convert numbers to words
        except:
            return num # In case num2words fails (unlikely with digits but just to be safe)
    return re.sub(r'\b\d+\b', replace_with_words, sentence) # Regular expression that matches numbers

@spaces.GPU(duration=90)
def run(seed, left_margin, right_margin, codec_audio_sr, codec_sr, top_k, top_p, temperature,
        stop_repetition, sample_batch_size, kvcache, silence_tokens,
        audio_path, transcribe_state, transcript, smart_transcript,
        mode, prompt_end_time, edit_start_time, edit_end_time,
        split_text, selected_sentence, previous_audio_tensors):
    if voicecraft_model is None:
        raise gr.Error("VoiceCraft model not loaded")
    if smart_transcript and (transcribe_state is None):
        raise gr.Error("Can't use smart transcript: whisper transcript not found")

    seed_everything(seed)
    transcript = replace_numbers_with_words(transcript).replace("  ", " ").replace("  ", " ") # replace numbers with words, so that the phonemizer can do a better job

    if mode == "Long TTS":
        if split_text == "Newline":
            sentences = transcript.split('\n')
        else:
            from nltk.tokenize import sent_tokenize
            sentences = sent_tokenize(transcript.replace("\n", " "))
    elif mode == "Rerun":
        colon_position = selected_sentence.find(':')
        selected_sentence_idx = int(selected_sentence[:colon_position])
        sentences = [selected_sentence[colon_position + 1:]]
    else:
        sentences = [transcript.replace("\n", " ")]

    info = torchaudio.info(audio_path)
    audio_dur = info.num_frames / info.sample_rate

    audio_tensors = []
    inference_transcript = ""
    for sentence in sentences:
        decode_config = {"top_k": top_k, "top_p": top_p, "temperature": temperature, "stop_repetition": stop_repetition,
                         "kvcache": kvcache, "codec_audio_sr": codec_audio_sr, "codec_sr": codec_sr,
                         "silence_tokens": silence_tokens, "sample_batch_size": sample_batch_size}
        if mode != "Edit":
            from inference_tts_scale import inference_one_sample

            if smart_transcript:
                target_transcript = ""
                for word in transcribe_state["words_info"]:
                    if word["end"] < prompt_end_time:
                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                    elif (word["start"] + word["end"]) / 2 < prompt_end_time:
                        # include part of the word it it's big, but adjust prompt_end_time
                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                        prompt_end_time = word["end"]
                        break
                    else:
                        break
                target_transcript += f" {sentence}"
            else:
                target_transcript = sentence

            inference_transcript += target_transcript + "\n"

            prompt_end_frame = int(min(audio_dur, prompt_end_time) * info.sample_rate)
            _, gen_audio = inference_one_sample(voicecraft_model["model"],
                                                voicecraft_model["config"],
                                                voicecraft_model["phn2num"],
                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
                                                audio_path, target_transcript, device, decode_config,
                                                prompt_end_frame)
        else:
            from inference_speech_editing_scale import inference_one_sample

            if smart_transcript:
                target_transcript = ""
                for word in transcribe_state["words_info"]:
                    if word["start"] < edit_start_time:
                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
                    else:
                        break
                target_transcript += f" {sentence}"
                for word in transcribe_state["words_info"]:
                    if word["end"] > edit_end_time:
                        target_transcript += word["word"] + (" " if word["word"][-1] != " " else "")
            else:
                target_transcript = sentence

            inference_transcript += target_transcript + "\n"

            morphed_span = (max(edit_start_time - left_margin, 1 / codec_sr), min(edit_end_time + right_margin, audio_dur))
            mask_interval = [[round(morphed_span[0]*codec_sr), round(morphed_span[1]*codec_sr)]]
            mask_interval = torch.LongTensor(mask_interval)

            _, gen_audio = inference_one_sample(voicecraft_model["model"],
                                                voicecraft_model["config"],
                                                voicecraft_model["phn2num"],
                                                voicecraft_model["text_tokenizer"], voicecraft_model["audio_tokenizer"],
                                                audio_path, target_transcript, mask_interval, device, decode_config)
        gen_audio = gen_audio[0].cpu()
        audio_tensors.append(gen_audio)

    if mode != "Rerun":
        output_audio = get_output_audio(audio_tensors, codec_audio_sr)
        sentences = [f"{idx}: {text}" for idx, text in enumerate(sentences)]
        component = gr.Dropdown(choices=sentences, value=sentences[0])
        return output_audio, inference_transcript, component, audio_tensors
    else:
        previous_audio_tensors[selected_sentence_idx] = audio_tensors[0]
        output_audio = get_output_audio(previous_audio_tensors, codec_audio_sr)
        sentence_audio = get_output_audio(audio_tensors, codec_audio_sr)
        return output_audio, inference_transcript, sentence_audio, previous_audio_tensors


def update_input_audio(audio_path):
    if audio_path is None:
        return 0, 0, 0

    info = torchaudio.info(audio_path)
    max_time = round(info.num_frames / info.sample_rate, 2)
    return [
        gr.Slider(maximum=max_time, value=max_time),
        gr.Slider(maximum=max_time, value=0),
        gr.Slider(maximum=max_time, value=max_time),
    ]


def change_mode(mode):
    # tts_mode_controls, edit_mode_controls, edit_word_mode, split_text, long_tts_sentence_editor
    return [
        gr.Group(visible=mode != "Edit"),
        gr.Group(visible=mode == "Edit"),
        gr.Radio(visible=mode == "Edit"),
        gr.Radio(visible=mode == "Long TTS"),
        gr.Group(visible=mode == "Long TTS"),
    ]


def load_sentence(selected_sentence, codec_audio_sr, audio_tensors):
    if selected_sentence is None:
        return None
    colon_position = selected_sentence.find(':')
    selected_sentence_idx = int(selected_sentence[:colon_position])
    return get_output_audio([audio_tensors[selected_sentence_idx]], codec_audio_sr)


def update_bound_word(is_first_word, selected_word, edit_word_mode):
    if selected_word is None:
        return None

    word_start_time = float(selected_word.split(' ')[0])
    word_end_time = float(selected_word.split(' ')[-1])
    if edit_word_mode == "Replace half":
        bound_time = (word_start_time + word_end_time) / 2
    elif is_first_word:
        bound_time = word_start_time
    else:
        bound_time = word_end_time

    return bound_time


def update_bound_words(from_selected_word, to_selected_word, edit_word_mode):
    return [
        update_bound_word(True, from_selected_word, edit_word_mode),
        update_bound_word(False, to_selected_word, edit_word_mode),
    ]


smart_transcript_info = """
If enabled, the target transcript will be constructed for you:</br>
 - In TTS and Long TTS mode just write the text you want to synthesize.</br>
 - In Edit mode just write the text to replace selected editing segment.</br>
If disabled, you should write the target transcript yourself:</br>
 - In TTS mode write prompt transcript followed by generation transcript.</br>
 - In Long TTS select split by newline (<b>SENTENCE SPLIT WON'T WORK</b>) and start each line with a prompt transcript.</br>
 - In Edit mode write full prompt</br>
"""

demo_original_transcript = "Gwynplaine had, besides, for his work and for his feats of strength, round his neck and over his shoulders, an esclavine of leather."

demo_text = {
    "TTS": {
        "smart": "I cannot believe that the same model can also do text to speech synthesis too!",
        "regular": "Gwynplaine had, besides, for his work and for his feats of strength, I cannot believe that the same model can also do text to speech synthesis too!"
    },
    "Edit": {
        "smart": "take over the stage for half an hour,",
        "regular": "Gwynplaine had, besides, for his work and for his feats of strength, take over the stage for half an hour, an esclavine of leather."
    },
    "Long TTS": {
        "smart": "You can run the model on a big text!\n"
                 "Just write it line-by-line. Or sentence-by-sentence.\n"
                 "If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!",
        "regular": "Gwynplaine had, besides, for his work and for his feats of strength, You can run the model on a big text!\n"
                   "Gwynplaine had, besides, for his work and for his feats of strength, Just write it line-by-line. Or sentence-by-sentence.\n"
                   "Gwynplaine had, besides, for his work and for his feats of strength, If some sentences sound odd, just rerun the model on them, no need to generate the whole text again!"
    }
}

all_demo_texts = {vv for k, v in demo_text.items() for kk, vv in v.items()}

demo_words = ['0.069 Gwynplain 0.611', '0.671 had, 0.912', '0.952 besides, 1.414', '1.494 for 1.634', '1.695 his 1.835', '1.915 work 2.136', '2.196 and 2.297', '2.337 for 2.517', '2.557 his 2.678', '2.758 feats 3.019', '3.079 of 3.139', '3.2 strength, 3.561', '4.022 round 4.263', '4.303 his 4.444', '4.524 neck 4.705', '4.745 and 4.825', '4.905 over 5.086', '5.146 his 5.266', '5.307 shoulders, 5.768', '6.23 an 6.33', '6.531 esclavine 7.133', '7.213 of 7.293', '7.353 leather. 7.614']

demo_words_info = [{'word': 'Gwynplain', 'start': 0.069, 'end': 0.611, 'score': 0.833}, {'word': 'had,', 'start': 0.671, 'end': 0.912, 'score': 0.879}, {'word': 'besides,', 'start': 0.952, 'end': 1.414, 'score': 0.863}, {'word': 'for', 'start': 1.494, 'end': 1.634, 'score': 0.89}, {'word': 'his', 'start': 1.695, 'end': 1.835, 'score': 0.669}, {'word': 'work', 'start': 1.915, 'end': 2.136, 'score': 0.916}, {'word': 'and', 'start': 2.196, 'end': 2.297, 'score': 0.766}, {'word': 'for', 'start': 2.337, 'end': 2.517, 'score': 0.808}, {'word': 'his', 'start': 2.557, 'end': 2.678, 'score': 0.786}, {'word': 'feats', 'start': 2.758, 'end': 3.019, 'score': 0.97}, {'word': 'of', 'start': 3.079, 'end': 3.139, 'score': 0.752}, {'word': 'strength,', 'start': 3.2, 'end': 3.561, 'score': 0.742}, {'word': 'round', 'start': 4.022, 'end': 4.263, 'score': 0.916}, {'word': 'his', 'start': 4.303, 'end': 4.444, 'score': 0.666}, {'word': 'neck', 'start': 4.524, 'end': 4.705, 'score': 0.908}, {'word': 'and', 'start': 4.745, 'end': 4.825, 'score': 0.882}, {'word': 'over', 'start': 4.905, 'end': 5.086, 'score': 0.847}, {'word': 'his', 'start': 5.146, 'end': 5.266, 'score': 0.791}, {'word': 'shoulders,', 'start': 5.307, 'end': 5.768, 'score': 0.729}, {'word': 'an', 'start': 6.23, 'end': 6.33, 'score': 0.854}, {'word': 'esclavine', 'start': 6.531, 'end': 7.133, 'score': 0.803}, {'word': 'of', 'start': 7.213, 'end': 7.293, 'score': 0.772}, {'word': 'leather.', 'start': 7.353, 'end': 7.614, 'score': 0.896}]


def update_demo(mode, smart_transcript, edit_word_mode, transcript, edit_from_word, edit_to_word):
    if transcript not in all_demo_texts:
        return transcript, edit_from_word, edit_to_word

    replace_half = edit_word_mode == "Replace half"
    change_edit_from_word = edit_from_word == demo_words[2] or edit_from_word == demo_words[3]
    change_edit_to_word = edit_to_word == demo_words[11] or edit_to_word == demo_words[12]
    demo_edit_from_word_value = demo_words[2] if replace_half else demo_words[3]
    demo_edit_to_word_value = demo_words[12] if replace_half else demo_words[11]
    return [
        demo_text[mode]["smart" if smart_transcript else "regular"],
        demo_edit_from_word_value if change_edit_from_word else edit_from_word,
        demo_edit_to_word_value if change_edit_to_word else edit_to_word,
    ]


def get_app():
    with gr.Blocks() as app:
        with gr.Row():
            with gr.Column(scale=2):
                load_models_btn = gr.Button(value="Load models")
            with gr.Column(scale=5):
                with gr.Accordion("Select models", open=False) as models_selector:
                    with gr.Row():
                        voicecraft_model_choice = gr.Radio(label="VoiceCraft model", value="830M_TTSEnhanced",
                                                        choices=["330M", "830M", "330M_TTSEnhanced", "830M_TTSEnhanced"])
                        whisper_backend_choice = gr.Radio(label="Whisper backend", value="whisperX", choices=["whisperX", "whisper"])
                        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=["whisperX", None])

        with gr.Row():
            with gr.Column(scale=2):
                input_audio = gr.Audio(value=f"{DEMO_PATH}/5895_34622_000026_000002.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 whisperx 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 all",
                                                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[11], interactive=True)
                        prompt_end_time = gr.Slider(label="Prompt end time", minimum=0, maximum=7.614, step=0.001, value=3.600)

                    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[12], interactive=True)
                            edit_to_word = gr.Dropdown(label="Last word to edit", choices=demo_words, value=demo_words[18], interactive=True)
                        with gr.Row():
                            edit_start_time = gr.Slider(label="Edit from time", minimum=0, maximum=7.614, step=0.001, value=4.022)
                            edit_end_time = gr.Slider(label="Edit to time", minimum=0, maximum=7.614, step=0.001, value=5.768)

                    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=3, 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)