app.py

import argparse
import os
from pathlib import Path

import logging
import re_matching

logging.getLogger("numba").setLevel(logging.WARNING)
logging.getLogger("markdown_it").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

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

logger = logging.getLogger(__name__)

import librosa
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
    Wav2Vec2Model,
    Wav2Vec2PreTrainedModel,
)

import gradio as gr

import utils
from config import config

import torch
import commons
from text import cleaned_text_to_sequence, get_bert
from emo_gen import process_func, EmotionModel, Wav2Vec2Processor, Wav2Vec2Model, Wav2Vec2PreTrainedModel, RegressionHead
from text.cleaner import clean_text
import utils

from models import SynthesizerTrn
from text.symbols import symbols
import sys

net_g = None

device = (
        "cuda:0"
        if torch.cuda.is_available()
        else (
            "mps"
            if sys.platform == "darwin" and torch.backends.mps.is_available()
            else "cpu"
        )
    )

BandList = {
        "MyGo":["燈","愛音","そよ","立希","楽奈"],
        "AveMujica":["祥子","睦","海鈴","にゃむ","初華"]
}

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):
    norm_text, phone, tone, word2ph = clean_text(text, language_str)
    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
    print(text)
    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_ori = get_bert(norm_text, word2ph, language_str, device)
    del word2ph
    assert bert_ori.shape[-1] == len(phone), phone

    if language_str == "ZH":
        bert = bert_ori
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "JP":
        bert = torch.zeros(1024, len(phone))
        ja_bert = bert_ori
        en_bert = torch.zeros(1024, len(phone))
    elif language_str == "EN":
        bert = torch.zeros(1024, len(phone))
        ja_bert = torch.zeros(1024, len(phone))
        en_bert = bert_ori
    else:
        raise ValueError("language_str should be ZH, JP or EN")

    assert bert.shape[-1] == len(
        phone
    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"

    phone = torch.LongTensor(phone)
    tone = torch.LongTensor(tone)
    language = torch.LongTensor(language)
    return bert, ja_bert, en_bert, phone, tone, language

def get_emo_(reference_audio, emotion):
    emo = (
        torch.from_numpy(get_emo(reference_audio))
        if reference_audio
        else torch.Tensor([emotion])
    )
    return emo

def get_emo(path):
    wav, sr = librosa.load(path, 16000)
    device = config.bert_gen_config.device
    return process_func(
        np.expand_dims(wav, 0).astype(np.float),
        sr,
        emotional_model,
        emotional_processor,
        device,
        embeddings=True,
    ).squeeze(0)

def infer(
    text,
    sdp_ratio,
    noise_scale,
    noise_scale_w,
    length_scale,
    sid,
    reference_audio=None,
    emotion=None,
):

    language= 'JP' if is_japanese(text) else 'ZH'
    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
        text, language, hps, device
    )
    emo = get_emo_(reference_audio, emotion)
    with torch.no_grad():
        x_tst = phones.to(device).unsqueeze(0)
        tones = tones.to(device).unsqueeze(0)
        lang_ids = lang_ids.to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        ja_bert = ja_bert.to(device).unsqueeze(0)
        en_bert = en_bert.to(device).unsqueeze(0)
        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
        emo = emo.to(device).unsqueeze(0)
        del phones
        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
        audio = (
            net_g.infer(
                x_tst,
                x_tst_lengths,
                speakers,
                tones,
                lang_ids,
                bert,
                ja_bert,
                en_bert,
                emo,
                sdp_ratio=sdp_ratio,
                noise_scale=noise_scale,
                noise_scale_w=noise_scale_w,
                length_scale=length_scale,
            )[0][0, 0]
            .data.cpu()
            .float()
            .numpy()
        )
        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert, emo
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return (hps.data.sampling_rate,audio)

def is_japanese(string):
        for ch in string:
            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
                return True
        return False

def loadmodel(model):
    _ = net_g.eval()
    _ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
    return "success"

if __name__ == "__main__":
    emotional_model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    REPO_ID = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim"
    emotional_processor = Wav2Vec2Processor.from_pretrained(emotional_model_name)
    emotional_model = EmotionModel.from_pretrained(emotional_model_name).to(device)
    hps = utils.get_hparams_from_file('Data/BanGDream/configs/config.json')
    net_g = get_net_g(
        model_path='Data/BangDream/models/G_49000.pth', version="2.1", device=device, hps=hps
    )
    speaker_ids = hps.data.spk2id
    speakers = list(speaker_ids.keys())
    languages = [ "Auto", "ZH", "JP"]
    modelPaths = []
    for dirpath, dirnames, filenames in os.walk("Data/BanGDream/models/"):
        for filename in filenames:
            modelPaths.append(os.path.join(dirpath, filename))
    with gr.Blocks() as app:
        for band in BandList:
            with gr.TabItem(band):
                for name in BandList[band]:
                    with gr.TabItem(name):
                        with gr.Row():
                            with gr.Column():
                                with gr.Row():
                                    gr.Markdown(
                                        '<div align="center">'
                                        f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' 
                                        '</div>'
                                    )
                                length_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
                                    )
                                emotion = gr.Slider(
                                    minimum=0, maximum=9, value=0, step=1, label="Emotion"
                                )
                                with gr.Accordion(label="参数设定", open=False):
                                    sdp_ratio = gr.Slider(
                                    minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
                                    )
                                    noise_scale = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
                                    )
                                    noise_scale_w = gr.Slider(
                                        minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
                                    )
                                    speaker = gr.Dropdown(
                                        choices=speakers, value=name, label="说话人"
                                    ) 
                                with gr.Accordion(label="切换模型", open=False):
                                    modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
                                    btnMod = gr.Button("载入模型")
                                    statusa = gr.TextArea()
                                    btnMod.click(loadmodel, inputs=[modelstrs], outputs = [statusa])
                            with gr.Column():
                                text = gr.TextArea(
                                    label="输入纯日语或者中文",
                                    placeholder="输入纯日语或者中文",
                                    value="为什么要演奏春日影!",
                                )
                                reference_audio = gr.Audio(label="情感参考音频（WAV 格式）：用于生成语音的情感参考。（WAV 格式）", type="filepath")                                
                                btn = gr.Button("点击生成", variant="primary")
                                audio_output = gr.Audio(label="Output Audio")
                                '''
                                btntran = gr.Button("快速中翻日")
                                translateResult = gr.TextArea("从这复制翻译后的文本")
                                btntran.click(translate, inputs=[text], outputs = [translateResult])
                                '''
                    btn.click(
                        infer,
                        inputs=[
                            text,
                            sdp_ratio,
                            noise_scale,
                            noise_scale_w,
                            length_scale,
                            speaker,
                            reference_audio,
                            emotion,
                        ],
                        outputs=[audio_output],
                    )

    print("推理页面已开启!")
    app.launch()