bert-vits2搭建与训练，实现训练个性化音色

bert-vits2训练流程

对应的B站搭建流程视频教程：https://www.bilibili.com/video/BV1b34y1g7Ah/?spm_id_from=333.999.0.0&vd_source=2a400e9a86101d2bf4ac9d3ed71e65c9

1. python 版本 python 3.8 + cuda11.7.

2. 下载bert-vits2 git clone https://github.com/fishaudio/Bert-VITS2.git

3. pytorch linux直接pip安装， windows去官网下载（nvcc -v查看cuda版本）

4. 模型下载 中文模型，日文模型，一般都是缺少大文件，去huggingface下载对应缺少的文件放在项目的bert下。

   中：https://huggingface.co/hfl/chinese-roberta-wwm-ext-large
   日：https://huggingface.co/cl-tohoku/bert-base-japanese-v3/tree/main
   # 下载好放在bert里
   

5. 测试

   python ./text/chinese_bert.py 
   python ./text/japanese_bert.py
   

6. 下载数据集 在raw文件夹下创建对应名称的文件夹

    https://www.bilibili.com/read/cv26659988/?from=articleDetail  
    # 放在./raw/{name}下
   

注： 如果想要训练自己的音色，而项目代码并没给如何得到.wav.lab格式的文件形式，下面是我自己写的自动标注代码，只用把音频放在新建的data/name下，name要与下面文件中的对应运行这个文件就行：

import os
from pathlib import Path
import librosa
from scipy.io import wavfile
import numpy as np
import whisper

a="ll" # 请在这里修改说话人的名字，目前只支持中文语音,将音频放在data/ll下

def split_long_audio(model, filepaths, save_dir="data_dir", out_sr=44100):
    files=os.listdir(filepaths)
    filepaths=[os.path.join(filepaths,i)  for i in files]

    i=0
    for file_idx, filepath in enumerate(filepaths):

        save_path = Path(save_dir)
        save_path.mkdir(exist_ok=True, parents=True)

        print(f"Transcribing file {file_idx}: '{filepath}' to segments...")
        result = model.transcribe(filepath, word_timestamps=True, task="transcribe", beam_size=5, best_of=5)
        segments = result['segments']

        wav, sr = librosa.load(filepath, sr=None, offset=0, duration=None, mono=True)
        wav, _ = librosa.effects.trim(wav, top_db=20)
        peak = np.abs(wav).max()
        if peak > 1.0:
            wav = 0.98 * wav / peak
        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=out_sr)
        wav2 /= max(wav2.max(), -wav2.min())

        for i, seg in enumerate(segments):
            start_time = seg['start']
            end_time = seg['end']
            wav_seg = wav2[int(start_time * out_sr):int(end_time * out_sr)]
            wav_seg_name = f"{a}_{i}.wav" # 修改名字
            i+=1
            out_fpath = save_path / wav_seg_name
            wavfile.write(out_fpath, rate=out_sr, data=(wav_seg * np.iinfo(np.int16).max).astype(np.int16))

def transcribe_one(audio_path): # 使用whisper语音识别
    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio_path)
    audio = whisper.pad_or_trim(audio)
    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    # detect the spoken language
    _, probs = model.detect_language(mel)
    print(f"Detected language: {max(probs, key=probs.get)}")
    lang = max(probs, key=probs.get)
    # decode the audio
    options = whisper.DecodingOptions(beam_size=5)
    result = whisper.decode(model, mel, options)

    # print the recognized text
    print(result.text)
    return result.text 

if __name__ == '__main__':
    whisper_size = "medium"
    model = whisper.load_model(whisper_size)
    audio_path = f"./raw/{a}"
    if os.path.exists(audio_path):
	    for filename in os.listdir(audio_path): # 删除原来的音频和文本
	        file_path = os.path.join(audio_path, filename)
	        os.remove(file_path)
    split_long_audio(model, f"data/{a}", f"./raw/{a}")
    files=os.listdir(audio_path)
    file_list_sorted = sorted(files, key=lambda x: int(os.path.splitext(x)[0].split('_')[1]))
    filepaths=[os.path.join(audio_path,i)  for i in file_list_sorted]
    for file_idx, filepath in enumerate(filepaths):  # 循环使用whisper遍历每一个音频,写入.alb
        text = transcribe_one(filepath)
        with open(f"./raw/{a}/{a}_{file_idx}.lab",'w') as f:
            f.write(text)

7. 工具类，自己命名一下，放在根目录下，修改里面的ch_name，运行，他会在filelists下生成txt文件

ch_name
import os

out_file = f"filelists/genshin_out.txt"
def process():
    with open(out_file,'w' , encoding="Utf-8") as wf:
        ch_name = 'nxt'
        ch_language = 'ZH'
        path = f"./raw/{ch_name}"
        files = os.listdir(path)
        for f in files:
            if f.endswith(".lab"):
                with open(os.path.join(path,f),'r', encoding="utf-8") as perFile:
                    line = perFile.readline() 
                    result = f"./dataset/{ch_name}/{f.split('.')[0]}.wav|{ch_name}|{ch_language}|{line}"
                    wf.write(f"{result}\n")

if __name__ == "__main__":
    process()

7. 修改preprocess_text.py，运行该文件。filelists 文件夹下会生成.clean等文件

    default="filelists/genshin_out.txt"
   

8. 重采样，会在dataset下生成重采样后的音频，如果修改了源音频要进行二次训练，需要将原dataset下的文件删除。

python resample.py

9.运行bert_gen.py,生成pt文件

# 修改num_processes
python bert_gen.py

10.底模下载:放在logs/genshin_mix

https://openi.pcl.ac.cn/Stardust_minus/Bert-VITS2/modelmanage/model_filelist_tmpl?name=Bert-VITS2%E5%BA%95%E6%A8%A1
# D_0 G_0 DUR_0

11. 开始训练

    python train_ms.py -m genshin_mix -c configs/config.json