app.py

import sys
import logging
import os
import json
import torch
import argparse
import commons
import utils
import gradio as gr
import numpy as np
import librosa
import re_matching
from tools.sentence import split_by_language
from huggingface_hub import hf_hub_download

from clap_wrapper import get_clap_audio_feature, get_clap_text_feature
from models import SynthesizerTrn
from text.symbols import symbols
from text import cleaned_text_to_sequence, get_bert
from text.cleaner import clean_text

logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
logger = logging.getLogger(__name__)

def get_net_g(model_path: str, version: str, device: str, hps):
    net_g = SynthesizerTrn(
        len(symbols),
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model,
    ).to(device)
    _ = net_g.eval()
    _ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
    return net_g

def get_text(text, language_str, hps, device, style_text=None, style_weight=0.7):
    style_text = None if style_text == "" else style_text
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
    if hps.data.add_blank:
        phone = commons.intersperse(phone, 0)
        tone = commons.intersperse(tone, 0)
        language = commons.intersperse(language, 0)
        for i in range(len(word2ph)):
            word2ph[i] = word2ph[i] * 2
        word2ph[0] += 1
    bert = get_bert(norm_text, word2ph, language_str, device, style_text, style_weight)
    del word2ph
    assert bert.shape[-1] == len(phone)
    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, phone, tone, language

def infer(*args, **kwargs):
    from infer import infer as real_infer
    return real_infer(*args, **kwargs)

def load_audio(path):
    audio, sr = librosa.load(path, 48000)
    return sr, audio

def gr_util(item):
    if item == "Text prompt":
        return {"visible": True, "__type__": "update"}, {"visible": False, "__type__": "update"}
    else:
        return {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}

def create_tts_fn(hps, net_g, device):
    def tts_fn(
        text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language,
        reference_audio, emotion, prompt_mode, style_text, style_weight
    ):
        if style_text == "":
            style_text = None
        if prompt_mode == "Audio prompt":
            if reference_audio is None:
                return ("Invalid audio prompt", None)
            else:
                reference_audio = load_audio(reference_audio)[1]
        else:
            reference_audio = None

        audio = infer(
            text=text,
            reference_audio=reference_audio,
            emotion=emotion,
            sdp_ratio=sdp_ratio,
            noise_scale=noise_scale,
            noise_scale_w=noise_scale_w,
            length_scale=length_scale,
            sid=speaker,
            language=language,
            hps=hps,
            net_g=net_g,
            device=device,
            style_text=style_text,
            style_weight=style_weight,
        )
        return "Success", (hps.data.sampling_rate, audio)
    return tts_fn

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--share", default=False, help="make link public", action="store_true")
    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
    args = parser.parse_args()

    if args.debug:
        logger.setLevel(logging.DEBUG)

    with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)

    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    models = []
    for _, info in models_info.items():
        if not info['enable']:
            continue
        name, title, link, example = info['name'], info['title'], info['link'], info['example']
        config_path = hf_hub_download(repo_id=link, filename="config.json")
        model_path = hf_hub_download(repo_id=link, filename=f"{name}.pth")
        hps = utils.get_hparams_from_file(config_path)
        version = hps.version if hasattr(hps, "version") else "v2"
        net_g = get_net_g(model_path, version, device, hps)
        fn = create_tts_fn(hps, net_g, device)
        models.append((title, example, list(hps.data.spk2id.keys()), fn))

    with gr.Blocks(theme='NoCrypt/miku') as app:
        gr.Markdown("## ✅ All models loaded successfully. Ready to use.")
        with gr.Tabs():
            for (title, example, speakers, tts_fn) in models:
                with gr.TabItem(title):
                    with gr.Row():
                        with gr.Column():
                            input_text = gr.Textbox(label="Input text", lines=5, value=example)
                            speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="Speaker")
                            prompt_mode = gr.Radio(["Text prompt", "Audio prompt"], label="Prompt Mode", value="Text prompt")
                            text_prompt = gr.Textbox(label="Text prompt", value="Happy", visible=True)
                            audio_prompt = gr.Audio(label="Audio prompt", type="filepath", visible=False)
                            sdp_ratio = gr.Slider(0, 1, 0.2, 0.1, label="SDP Ratio")
                            noise_scale = gr.Slider(0.1, 2.0, 0.6, 0.1, label="Noise")
                            noise_scale_w = gr.Slider(0.1, 2.0, 0.8, 0.1, label="Noise_W")
                            length_scale = gr.Slider(0.1, 2.0, 1.0, 0.1, label="Length")
                            language = gr.Dropdown(choices=["JP", "ZH", "EN", "mix", "auto"], value="JP", label="Language")
                            style_text = gr.Textbox(label="Style Text", placeholder="辅助文本 (留空为无)")
                            style_weight = gr.Slider(0, 1, 0.7, 0.1, label="Style Weight")
                            btn = gr.Button("Generate Audio", variant="primary")

                        with gr.Column():
                            output_msg = gr.Textbox(label="Output Message")
                            output_audio = gr.Audio(label="Output Audio")

                    prompt_mode.change(lambda x: gr_util(x), inputs=[prompt_mode], outputs=[text_prompt, audio_prompt])
                    audio_prompt.upload(lambda x: load_audio(x), inputs=[audio_prompt], outputs=[audio_prompt])
                    btn.click(tts_fn, inputs=[input_text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language, audio_prompt, text_prompt, prompt_mode, style_text, style_weight], outputs=[output_msg, output_audio])

    app.queue().launch(share=args.share)