app.py

import os
import queue
from huggingface_hub import snapshot_download
import hydra
import numpy as np
import wave
import io
import pyrootutils
import gc

# Download if not exists
os.makedirs("checkpoints", exist_ok=True)
snapshot_download(repo_id="fishaudio/fish-speech-1.4", local_dir="./checkpoints/fish-speech-1.4")

print("All checkpoints downloaded")

import html
import os
import threading
from argparse import ArgumentParser
from pathlib import Path
from functools import partial

import gradio as gr
import librosa
import torch
import torchaudio
import devicetorch

torchaudio.set_audio_backend("soundfile")

from loguru import logger
from transformers import AutoTokenizer

from tools.llama.generate import launch_thread_safe_queue
from tools.vqgan.inference import load_model as load_vqgan_model
from fish_speech.text.chn_text_norm.text import Text as ChnNormedText
from tools.api import decode_vq_tokens, encode_reference
from tools.auto_rerank import batch_asr, calculate_wer, is_chinese, load_model
from tools.llama.generate import (
    GenerateRequest,
    GenerateResponse,
    WrappedGenerateResponse,
    launch_thread_safe_queue,
)
from tools.vqgan.inference import load_model as load_decoder_model

# Make einx happy
os.environ["EINX_FILTER_TRACEBACK"] = "false"


HEADER_MD = """# Fish Speech

## The demo in this space is version 1.4, Please check [Fish Audio](https://fish.audio) for the best model.
## 该 Demo 为 Fish Speech 1.4 版本, 请在 [Fish Audio](https://fish.audio) 体验最新 DEMO.

A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).  
由 [Fish Audio](https://fish.audio) 研发的基于 VQ-GAN 和 Llama 的多语种语音合成. 

You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.4).  
你可以在 [这里](https://github.com/fishaudio/fish-speech) 找到源代码和 [这里](https://huggingface.co/fishaudio/fish-speech-1.4) 找到模型.  

Related code and weights are released under CC BY-NC-SA 4.0 License.  
相关代码，权重使用 CC BY-NC-SA 4.0 许可证发布.

We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.  
我们不对模型的任何滥用负责，请在使用之前考虑您当地的法律法规.

The model running in this WebUI is Fish Speech V1.4 Medium.
在此 WebUI 中运行的模型是 Fish Speech V1.4 Medium.
"""

TEXTBOX_PLACEHOLDER = """Put your text here. 在此处输入文本."""

try:
    import spaces

    GPU_DECORATOR = spaces.GPU
except ImportError:

    def GPU_DECORATOR(func):
        def wrapper(*args, **kwargs):
            return func(*args, **kwargs)

        return wrapper


def build_html_error_message(error):
    return f"""
    <div style="color: red; 
    font-weight: bold;">
        {html.escape(error)}
    </div>
    """


@GPU_DECORATOR
@torch.inference_mode()
def inference(
    text,
    enable_reference_audio,
    reference_audio,
    reference_text,
    max_new_tokens,
    chunk_length,
    top_p,
    repetition_penalty,
    temperature,
    streaming=False
):
    if args.max_gradio_length > 0 and len(text) > args.max_gradio_length:
        return (
            None,
            None,
            "Text is too long, please keep it under {} characters.".format(
                args.max_gradio_length
            ),
        )

    # Parse reference audio aka prompt
    prompt_tokens = encode_reference(
        decoder_model=decoder_model,
        reference_audio=reference_audio,
        enable_reference_audio=enable_reference_audio,
    )

    # LLAMA Inference
    request = dict(
        device=decoder_model.device,
        max_new_tokens=max_new_tokens,
        text=text,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        temperature=temperature,
        compile=args.compile,
        iterative_prompt=chunk_length > 0,
        chunk_length=chunk_length,
        max_length=2048,
        prompt_tokens=prompt_tokens if enable_reference_audio else None,
        prompt_text=reference_text if enable_reference_audio else None,
    )

    response_queue = queue.Queue()
    llama_queue.put(
        GenerateRequest(
            request=request,
            response_queue=response_queue,
        )
    )

    segments = []

    while True:
        result: WrappedGenerateResponse = response_queue.get()
        if result.status == "error":
            print(f"result={result}")
            return None, None, build_html_error_message(result.response)

        result: GenerateResponse = result.response
        if result.action == "next":
            break

        with torch.autocast(
            device_type=(
                "cpu"
                if decoder_model.device.type == "mps"
                else decoder_model.device.type
            ),
            dtype=args.precision,
        ):
            fake_audios = decode_vq_tokens(
                decoder_model=decoder_model,
                codes=result.codes,
            )

        fake_audios = fake_audios.float().cpu().numpy()
        segments.append(fake_audios)

    if len(segments) == 0:
        return (
            None,
            None,
            build_html_error_message(
                "No audio generated, please check the input text."
            ),
        )

    # Return the final audio
    audio = np.concatenate(segments, axis=0)
    return None, (decoder_model.spec_transform.sample_rate, audio), None

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        gc.collect()
    elif torch.backends.mps.is_available():
        torch.mps.empty_cache()
        gc.collect()


def inference_with_auto_rerank(
    text,
    enable_reference_audio,
    reference_audio,
    reference_text,
    max_new_tokens,
    chunk_length,
    top_p,
    repetition_penalty,
    temperature,
    use_auto_rerank,
    streaming=False,
):
    max_attempts = 2 if use_auto_rerank else 1
    best_wer = float("inf")
    best_audio = None
    best_sample_rate = None

    for attempt in range(max_attempts):
        _, (sample_rate, audio), message = inference(
            text,
            enable_reference_audio,
            reference_audio,
            reference_text,
            max_new_tokens,
            chunk_length,
            top_p,
            repetition_penalty,
            temperature,
            streaming=False,
        )

        if audio is None:
            return None, None, message

        if not use_auto_rerank:
            return None, (sample_rate, audio), None

        asr_result = batch_asr(asr_model, [audio], sample_rate)[0]
        wer = calculate_wer(text, asr_result["text"])
        
        if wer <= 0.3 and not asr_result["huge_gap"]:
            return None, (sample_rate, audio), None

        if wer < best_wer:
            best_wer = wer
            best_audio = audio
            best_sample_rate = sample_rate

        if attempt == max_attempts - 1:
            break

    return None, (best_sample_rate, best_audio), None


n_audios = 4

global_audio_list = []
global_error_list = []

def inference_wrapper(
    text,
    enable_reference_audio,
    reference_audio,
    reference_text,
    max_new_tokens,
    chunk_length,
    top_p,
    repetition_penalty,
    temperature,
    batch_infer_num,
    if_load_asr_model,
):
    audios = []
    errors = []

    for _ in range(batch_infer_num):
        result = inference_with_auto_rerank(
            text,
            enable_reference_audio,
            reference_audio,
            reference_text,
            max_new_tokens,
            chunk_length,
            top_p,
            repetition_penalty,
            temperature,
            if_load_asr_model,
        )

        _, audio_data, error_message = result

        audios.append(
            gr.Audio(value=audio_data if audio_data else None, visible=True),
        )
        errors.append(
            gr.HTML(value=error_message if error_message else None, visible=True),
        )

    for _ in range(batch_infer_num, n_audios):
        audios.append(
            gr.Audio(value=None, visible=False),
        )
        errors.append(
            gr.HTML(value=None, visible=False),
        )

    return None, *audios, *errors


def wav_chunk_header(sample_rate=44100, bit_depth=16, channels=1):
    buffer = io.BytesIO()

    with wave.open(buffer, "wb") as wav_file:
        wav_file.setnchannels(channels)
        wav_file.setsampwidth(bit_depth // 8)
        wav_file.setframerate(sample_rate)

    wav_header_bytes = buffer.getvalue()
    buffer.close()
    return wav_header_bytes


def normalize_text(user_input, use_normalization):
    if use_normalization:
        return ChnNormedText(raw_text=user_input).normalize()
    else:
        return user_input


asr_model = None


def change_if_load_asr_model(if_load):
    global asr_model

    if if_load:
        gr.Warning("Loading faster whisper model...")
        if asr_model is None:
            asr_model = load_model()
        return gr.Checkbox(label="Unload faster whisper model", value=if_load)

    if if_load is False:
        gr.Warning("Unloading faster whisper model...")
        del asr_model
        asr_model = None
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            gc.collect()
        elif torch.backends.mps.is_available():
            torch.mps.empty_cache()
            gc.collect()
        return gr.Checkbox(label="Load faster whisper model", value=if_load)


def change_if_auto_label(if_load, if_auto_label, enable_ref, ref_audio, ref_text):
    if if_load and asr_model is not None:
        if (
            if_auto_label
            and enable_ref
            and ref_audio is not None
            and ref_text.strip() == ""
        ):
            data, sample_rate = librosa.load(ref_audio)
            res = batch_asr(asr_model, [data], sample_rate)[0]
            ref_text = res["text"]
    else:
        gr.Warning("Whisper model not loaded!")

    return gr.Textbox(value=ref_text)


def build_app():
    with gr.Blocks(theme=gr.themes.Base()) as app:
        gr.Markdown(HEADER_MD)

        # Use light theme by default
        app.load(
            None,
            None,
            js="() => {const params = new URLSearchParams(window.location.search);if (!params.has('__theme')) {params.set('__theme', '%s');window.location.search = params.toString();}}"
            % args.theme,
        )

        # Inference
        with gr.Row():
            with gr.Column(scale=3):
                text = gr.Textbox(
                    label="Input Text", placeholder=TEXTBOX_PLACEHOLDER, lines=10
                )
                refined_text = gr.Textbox(
                    label="Realtime Transform Text",
                    placeholder=
                        "Normalization Result Preview (Currently Only Chinese)",
                    lines=5,
                    interactive=False,
                )

                with gr.Row():
                    if_refine_text = gr.Checkbox(
                        label="Text Normalization (ZH)",
                        value=False,
                        scale=1,
                    )

                    if_load_asr_model = gr.Checkbox(
                        label="Load / Unload ASR model for auto-reranking",
                        value=False,
                        scale=3,
                    )

                with gr.Row():
                    with gr.Tab(label="Advanced Config"):
                        chunk_length = gr.Slider(
                            label="Iterative Prompt Length, 0 means off",
                            minimum=0,
                            maximum=500,
                            value=200,
                            step=8,
                        )

                        max_new_tokens = gr.Slider(
                            label="Maximum tokens per batch, 0 means no limit",
                            minimum=0,
                            maximum=2048,
                            value=0,  # 0 means no limit
                            step=8,
                        )

                        top_p = gr.Slider(
                            label="Top-P",
                            minimum=0.6,
                            maximum=0.9,
                            value=0.7,
                            step=0.01,
                        )

                        repetition_penalty = gr.Slider(
                            label="Repetition Penalty",
                            minimum=1,
                            maximum=1.5,
                            value=1.2,
                            step=0.01,
                        )

                        temperature = gr.Slider(
                            label="Temperature",
                            minimum=0.6,
                            maximum=0.9,
                            value=0.7,
                            step=0.01,
                        )

                with gr.Tab(label="Reference Audio"):
                    gr.Markdown(
                        "5 to 10 seconds of reference audio, useful for specifying speaker."
                    )

                    enable_reference_audio = gr.Checkbox(
                        label="Enable Reference Audio",
                    )
                
                    # Add dropdown for selecting example audio files
                    example_audio_files = [f for f in os.listdir("examples") if f.endswith(".wav")]
                    example_audio_dropdown = gr.Dropdown(
                        label="Select Example Audio",
                        choices=[""] + example_audio_files,
                        value=""
                    )
                
                    reference_audio = gr.Audio(
                        label="Reference Audio",
                        type="filepath",
                    )
                    with gr.Row():
                        if_auto_label = gr.Checkbox(
                            label="Auto Labeling",
                            min_width=100,
                            scale=0,
                            value=False,
                        )
                        reference_text = gr.Textbox(
                            label="Reference Text",
                            lines=1,
                            placeholder="在一无所知中，梦里的一天结束了，一个新的「轮回」便会开始。",
                            value="",
                        )
                    with gr.Tab(label="Batch Inference"):
                        batch_infer_num = gr.Slider(
                            label="Batch infer nums",
                            minimum=1,
                            maximum=n_audios,
                            step=1,
                            value=1,
                        )

            with gr.Column(scale=3):
                for _ in range(n_audios):
                    with gr.Row():
                        error = gr.HTML(
                            label="Error Message",
                            visible=True if _ == 0 else False,
                        )
                        global_error_list.append(error)
                    with gr.Row():
                        audio = gr.Audio(
                            label="Generated Audio",
                            type="numpy",
                            interactive=False,
                            visible=True if _ == 0 else False,
                        )
                        global_audio_list.append(audio)

                with gr.Row():
                    stream_audio = gr.Audio(
                        label="Streaming Audio",
                        streaming=True,
                        autoplay=True,
                        interactive=False,
                        show_download_button=True,
                    )
                with gr.Row():
                    with gr.Column(scale=3):
                        generate = gr.Button(
                            value="\U0001F3A7 " + "Generate", variant="primary"
                        )
                        generate_stream = gr.Button(
                            value="\U0001F3A7 " + "Streaming Generate",
                            variant="primary",
                        )

        text.input(
            fn=normalize_text, inputs=[text, if_refine_text], outputs=[refined_text]
        )

        if_load_asr_model.change(
            fn=change_if_load_asr_model,
            inputs=[if_load_asr_model],
            outputs=[if_load_asr_model],
        )

        if_auto_label.change(
            fn=lambda: gr.Textbox(value=""),
            inputs=[],
            outputs=[reference_text],
        ).then(
            fn=change_if_auto_label,
            inputs=[
                if_load_asr_model,
                if_auto_label,
                enable_reference_audio,
                reference_audio,
                reference_text,
            ],
            outputs=[reference_text],
        )
        
        def select_example_audio(audio_file):
            if audio_file:
                audio_path = os.path.join("examples", audio_file)
                lab_file = os.path.splitext(audio_file)[0] + ".lab"
                lab_path = os.path.join("examples", lab_file)
                
                if os.path.exists(lab_path):
                    with open(lab_path, "r", encoding="utf-8") as f:
                        lab_content = f.read().strip()
                else:
                    lab_content = ""
                
                return audio_path, lab_content, True
            return None, "", False

        # Connect the dropdown to update reference audio and text
        example_audio_dropdown.change(
            fn=select_example_audio,
            inputs=[example_audio_dropdown],
            outputs=[reference_audio, reference_text, enable_reference_audio]
        )
        # # Submit
        generate.click(
            inference_wrapper,
            [
                refined_text,
                enable_reference_audio,
                reference_audio,
                reference_text,
                max_new_tokens,
                chunk_length,
                top_p,
                repetition_penalty,
                temperature,
                batch_infer_num,
                if_load_asr_model,
            ],
            [stream_audio, *global_audio_list, *global_error_list],
            concurrency_limit=1,
        )
    return app


def parse_args():
    parser = ArgumentParser()
    parser.add_argument(
        "--llama-checkpoint-path",
        type=Path,
        default="checkpoints/fish-speech-1.4",
    )
    parser.add_argument(
        "--decoder-checkpoint-path",
        type=Path,
        default="checkpoints/fish-speech-1.4/firefly-gan-vq-fsq-8x1024-21hz-generator.pth",
    )
    parser.add_argument("--decoder-config-name", type=str, default="firefly_gan_vq")
    #parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--device", type=str, default=devicetorch.get(torch))
    parser.add_argument("--half", action="store_true")
    #parser.add_argument("--compile", action="store_true",default=True)
    parser.add_argument("--compile", action="store_true",default=False)
    parser.add_argument("--max-gradio-length", type=int, default=0)
    parser.add_argument("--theme", type=str, default="light")

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    args.precision = torch.half if args.half else torch.bfloat16

    logger.info("Loading Llama model...")
    llama_queue = launch_thread_safe_queue(
        checkpoint_path=args.llama_checkpoint_path,
        device=args.device,
        precision=args.precision,
        compile=args.compile,
    )
    logger.info("Llama model loaded, loading VQ-GAN model...")

    decoder_model = load_decoder_model(
        config_name=args.decoder_config_name,
        checkpoint_path=args.decoder_checkpoint_path,
        device=args.device,
    )

    logger.info("Decoder model loaded, warming up...")

    # Dry run to check if the model is loaded correctly and avoid the first-time latency
    list(
        inference(
            text="Hello, world!",
            enable_reference_audio=False,
            reference_audio=None,
            reference_text="",
            max_new_tokens=0,
            chunk_length=200,
            top_p=0.7,
            repetition_penalty=1.2,
            temperature=0.7,
        )
    )

    logger.info("Warming up done, launching the web UI...")

    app = build_app()
    app.launch(show_api=True)