Yue_gradio.py

#好用的

import os
os.system('pip install -U tensorflow')
os.system('pip install -q unidecode tensorboardX')
os.system('pip install librosa==0.8.0')
os.system('pip install pysoundfile==0.9.0.post1')
os.system('pip install unidecode==1.3.4')
os.system('pip install pyopenjtalk --no-build-isolation')
os.system('pip install inflect==5.6.2')
os.system('pip install janome==0.4.2')
os.system('pip install tqdm -q')
os.system('pip install gdown')
os.system('pip install -q librosa unidecode')

os.system('pip install ipython')
os.system('pip install --upgrade jupyter ipywidgets')
os.system('jupyter nbextension enable --py widgetsnbextension')
os.system('pip uninstall tqdm')
os.system('pip install tqdm')

import time
import pyopenjtalk
import soundfile as sf
import gradio as gr
import torch
import IPython.display as ipd
import numpy as np
import torch
import json 
from hparams import create_hparams
from model import Tacotron2
from layers import TacotronSTFT
from audio_processing import griffin_lim
from text import text_to_sequence
from env import AttrDict
from meldataset import MAX_WAV_VALUE
from models import Generator

#@，tlitle 配置并运行

#国际 HiFi-GAN 模型(有点机器音): 1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW
#@markdown 你训练好的tacotron2模型的路径填在`Tacotron2_Model`这里
Tacotron2_Model = '/content/Yui_TrapGenesis'#@param {type:"string"}
TACOTRON2_ID = Tacotron2_Model
HIFIGAN_ID = "1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW"
#@markdown 选择预处理文本的cleaner
text_cleaner = 'japanese_phrase_cleaners'#@param {type:"string"}
import pyopenjtalk
import soundfile as sf
import gradio as gr

# 全局变量声明
model = None
hparams = None
hifigan = None
thisdict = None
pronounciation_dictionary = False
show_graphs = False  # 添加show_graphs变量，并赋予默认值

# 初始化函数
def initialize():
    global model, hparams, hifigan, thisdict, pronounciation_dictionary

    # 检查是否已初始化
    try:
        initialized
    except NameError:
        print("Setting up, please wait.\n")

    from tqdm.notebook import tqdm
    with tqdm(total=5, leave=False) as pbar:
        import os
        from os.path import exists, join, basename, splitext
        git_repo_url = 'https://github.com/CjangCjengh/tacotron2-japanese.git'
        project_name = splitext(basename(git_repo_url))[0]
        if not exists(project_name):
            # clone and install
            os.system('git clone -q --recursive {git_repo_url}')
            os.system('git clone -q --recursive https://github.com/SortAnon/hifi-gan')

        pbar.update(1) # downloaded TT2 and HiFi-GAN
        import sys
        sys.path.append('hifi-gan')
        sys.path.append(project_name)
        import time
        import matplotlib
        import matplotlib.pylab as plt
        import gdown
        d = 'https://drive.google.com/uc?id='

       # %matplotlib inline
        import IPython.display as ipd
        import numpy as np
        import torch
        import json
        from hparams import create_hparams
        from model import Tacotron2
        from layers import TacotronSTFT
        from audio_processing import griffin_lim
        from text import text_to_sequence
        from env import AttrDict
        from meldataset import MAX_WAV_VALUE
        from models import Generator

        pbar.update(1) # initialized Dependancies

        graph_width = 900
        graph_height = 360
        def plot_data(data, figsize=(int(graph_width/100), int(graph_height/100))):
           # %matplotlib inline
            fig, axes = plt.subplots(1, len(data), figsize=figsize)
            for i in range(len(data)):
                axes[i].imshow(data[i], aspect='auto', origin='upper',
                            interpolation='none', cmap='inferno')
            fig.canvas.draw()
            plt.show()

        # Setup Pronounciation Dictionary
        os.system('wget https://github.com/wind4000/tacotron2/releases/download/v0.2/merged.dict.txt')
        thisdict = {}
        for line in reversed((open('merged.dict.txt', "r").read()).splitlines()):
            thisdict[(line.split(" ",1))[0]] = (line.split(" ",1))[1].strip()

        pbar.update(1) # Downloaded and Set up Pronounciation Dictionary

        def ARPA(text, punctuation=r"!?,.;", EOS_Token=True):
            out = ''
            for word_ in text.split(" "):
                word=word_; end_chars = ''
                while any(elem in word for elem in punctuation) and len(word) > 1:
                    if word[-1] in punctuation: end_chars = word[-1] + end_chars; word = word[:-1]
                    else: break
                try:
                    word_arpa = thisdict[word.upper()]
                    word = "{" + str(word_arpa) + "}"
                except KeyError: pass
                out = (out + " " + word + end_chars).strip()
            if EOS_Token and out[-1] != ";": out += ";"
            return out

        def get_hifigan(MODEL_ID):
            # Download HiFi-GAN
            hifigan_pretrained_model = 'hifimodel'
            gdown.download(d+MODEL_ID, hifigan_pretrained_model, quiet=False)
            if not exists(hifigan_pretrained_model):
                raise Exception("HiFI-GAN model failed to download!")

            # Load HiFi-GAN
            conf = os.path.join("hifi-gan", "config_v1.json")
            with open(conf) as f:
                json_config = json.loads(f.read())
            h = AttrDict(json_config)
            torch.manual_seed(h.seed)
            hifigan = Generator(h).to(torch.device("cuda"))
            state_dict_g = torch.load(hifigan_pretrained_model, map_location=torch.device("cuda"))
            hifigan.load_state_dict(state_dict_g["generator"])
            hifigan.eval()
            hifigan.remove_weight_norm()
            return hifigan, h

        hifigan, h = get_hifigan(HIFIGAN_ID)
        pbar.update(1) # Downloaded and Set up HiFi-GAN

        def has_MMI(STATE_DICT):
            return any(True for x in STATE_DICT.keys() if "mi." in x)

        def get_Tactron2(MODEL_ID):
            # Download Tacotron2
            tacotron2_pretrained_model = TACOTRON2_ID
            if not exists(tacotron2_pretrained_model):
                raise Exception("Tacotron2 model failed to download!")
            # Load Tacotron2 and Config
            hparams = create_hparams()
            hparams.sampling_rate = 22050
            hparams.max_decoder_steps = 2000 # Max Duration
            hparams.gate_threshold = 0.80 # Model must be 25% sure the clip is over before ending generation
            model = Tacotron2(hparams)
            state_dict = torch.load(tacotron2_pretrained_model)['state_dict']
            if has_MMI(state_dict):
                raise Exception("ERROR: This notebook does not currently support MMI models.")
            model.load_state_dict(state_dict)
            _ = model.cuda().eval().half()
            return model, hparams

        model, hparams = get_Tactron2(TACOTRON2_ID)
        previous_tt2_id = TACOTRON2_ID

        pbar.update(1) # Downloaded and Set up Tacotron2

        # 初始化
initialize()

import soundfile as sf

def end_to_end_infer(text, pronounciation_dictionary, show_graphs):
    audio = None  # 定义一个变量用于存储音频数据
    for i in [x for x in text.split("\n") if len(x)]:
        if not pronounciation_dictionary:
            if i[-1] != ";":
                i = i + ";"
        else:
            i = ARPA(i)
        with torch.no_grad():
            sequence = np.array(text_to_sequence(i, [text_cleaner]))[None, :]
            sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
            mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
            if show_graphs:
                plot_data((mel_outputs_postnet.float().data.cpu().numpy()[0],
                           alignments.float().data.cpu().numpy()[0].T))
            y_g_hat = hifigan(mel_outputs_postnet.float())
            audio = y_g_hat.squeeze()
            audio = audio * MAX_WAV_VALUE
            output_filename = f"output_{time.strftime('%Y%m%d%H%M%S')}.wav"
            sf.write(output_filename, audio.cpu().numpy().astype('int16'), hparams.sampling_rate)
            print(f"音频已保存为 {output_filename}")
            print("")
            ipd.display(ipd.Audio(audio.cpu().numpy().astype("int16"), rate=hparams.sampling_rate))
    return audio  # 返回音频数据

# 文本到语音转换函数
def text_to_speech(text, max_decoder_steps=2000, gate_threshold=0.5):
    global model, hparams, hifigan, thisdict, pronounciation_dictionary, show_graphs

    hparams.max_decoder_steps = max_decoder_steps
    hparams.gate_threshold = gate_threshold
    output_filename = f"output_{time.strftime('%Y%m%d%H%M%S')}.wav"
    audio = end_to_end_infer(text, pronounciation_dictionary, show_graphs)
    if audio is not None:
        sf.write(output_filename, audio.cpu().numpy().astype('int16'), hparams.sampling_rate)
        return output_filename
    else:
        return None

# Gradio界面
inputs = [
    gr.inputs.Textbox(lines=3, label="输入文本"),
    gr.inputs.Slider(minimum=100, maximum=5000, default=2000, step=100, label="最大解码步数"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, step=0.05, label="门控阈值")
]
outputs = gr.outputs.File(label="下载生成的音频")

gr.Interface(fn=text_to_speech, inputs=inputs, outputs=outputs).launch(debug=True,share=True)