Bert-vits2最终版Bert-vits2-2.3云端训练和推理(Colab免费GPU算力平台)

对于深度学习初学者来说，JupyterNoteBook的脚本运行形式显然更加友好，依托Python语言的跨平台特性，JupyterNoteBook既可以在本地线下环境运行，也可以在线上服务器上运行。GoogleColab作为免费GPU算力平台的执牛耳者，更是让JupyterNoteBook的脚本运行形式如虎添翼。

本次我们利用Bert-vits2的最终版Bert-vits2-v2.3和JupyterNoteBook的脚本来复刻生化危机6的人气角色艾达王(ada wong)。

本地调试JupyterNoteBook

众所周知，GoogleColab虽然可以免费提供GPU让用户用于模型训练和推理，但是每一个JupyterNoteBook文件脚本最多只能运行12小时，随后就会被限制，所以为了避免浪费宝贵的GPU使用时间，我们可以在线下调试自己的JupyterNoteBook脚本，调试成功后，就可以把脚本直接上传到GoogleColab平台。

首先通过pip命令进行本地安装：

python3 -m pip install jupyter

随后运行启动命令：

jupyter notebook

此时，访问本地的notebook地址：

随后选择文件-》新建-》Notebook 即可。

输入笔记内容：

#@title 查看显卡

!nvidia-smi

点击运行单元格：

程序返回：

#@title 查看显卡

!nvidia-smi

Wed Dec 27 12:36:10 2023

+---------------------------------------------------------------------------------------+

| NVIDIA-SMI 546.17                 Driver Version: 546.17       CUDA Version: 12.3     |

|-----------------------------------------+----------------------+----------------------+

| GPU  Name                     TCC/WDDM  | Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |

|                                         |                      |               MIG M. |

|=========================================+======================+======================|

|   0  NVIDIA GeForce RTX 4060 ...  WDDM  | 00000000:01:00.0 Off |                  N/A |

| N/A   50C    P0              20W / 115W |      0MiB /  8188MiB |      0%      Default |

|                                         |                      |                  N/A |

+-----------------------------------------+----------------------+----------------------+  

+---------------------------------------------------------------------------------------+

| Processes:                                                                            |

|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |

|        ID   ID                                                             Usage      |

|=======================================================================================|

|  No running processes found                                                           |

+---------------------------------------------------------------------------------------+

至此，就可以在本地调试NoteBook了。

安装ffmpeg

新增单元格：

#@title 安装ffmpeg

import os, uuid, re, IPython

import ipywidgets as widgets

import time  

from glob import glob

from google.colab import output, drive  

from IPython.display import clear_output

import os, sys, urllib.request

HOME = os.path.expanduser("~")

pathDoneCMD = f'{HOME}/doneCMD.sh'

if not os.path.exists(f"{HOME}/.ipython/ttmg.py"):

    hCode = "https://raw.githubusercontent.com/yunooooo/gcct/master/res/ttmg.py"

    urllib.request.urlretrieve(hCode, f"{HOME}/.ipython/ttmg.py")  

from ttmg import (

    loadingAn,

    textAn,

)  

loadingAn(name="lds")

textAn("Cloning Repositories...", ty='twg')

!git clone https://github.com/XniceCraft/ffmpeg-colab.git

!chmod 755 ./ffmpeg-colab/install

textAn("Installing FFmpeg...", ty='twg')

!./ffmpeg-colab/install

clear_output()

print('Installation finished!')

!rm -fr /content/ffmpeg-colab

!ffmpeg -version

由于语音转写需要ffmpeg的参与，所以需要安装ffmpeg的最新版本。

程序返回：

Installation finished!

c Copyright (c) 2000-2023 the FFmpeg developers

built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

configuration: --prefix=/home/ffmpeg-builder/release --pkg-config-flags=--static --extra-libs=-lm --disable-doc --disable-debug --disable-shared --disable-ffprobe --enable-static --enable-gpl --enable-version3 --enable-runtime-cpudetect --enable-avfilter --enable-filters --enable-nvenc --enable-nvdec --enable-cuvid --toolchain=hardened --disable-stripping --enable-opengl --pkgconfigdir=/home/ffmpeg-builder/release/lib/pkgconfig --extra-cflags='-I/home/ffmpeg-builder/release/include -static-libstdc++ -static-libgcc ' --extra-ldflags='-L/home/ffmpeg-builder/release/lib -fstack-protector -static-libstdc++ -static-libgcc ' --extra-cxxflags=' -static-libstdc++ -static-libgcc ' --extra-libs='-ldl -lrt -lpthread' --enable-ffnvcodec --enable-gmp --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libfdk-aac --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libkvazaar --enable-libmp3lame --enable-libopus --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libshine --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtheora --enable-libvidstab --ld=g++ --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxvid --enable-libzimg --enable-openssl --enable-zlib --enable-nonfree --extra-libs=-lpthread --enable-pthreads --extra-libs=-lgomp

libavutil      58.  2.100 / 58.  2.100

libavcodec     60.  3.100 / 60.  3.100

libavformat    60.  3.100 / 60.  3.100

libavdevice    60.  1.100 / 60.  1.100

libavfilter     9.  3.100 /  9.  3.100

libswscale      7.  1.100 /  7.  1.100

libswresample   4. 10.100 /  4. 10.100

libpostproc    57.  1.100 / 57.  1.100

这里安装的是最新版ffmpeg version 6.0

克隆代码库

接着克隆代码库：

#@title 克隆代码仓库

!git clone https://github.com/v3ucn/Bert-vits2-V2.3.git

程序返回：

Cloning into 'Bert-vits2-V2.3'...

remote: Enumerating objects: 234, done.

remote: Counting objects: 100% (234/234), done.

remote: Compressing objects: 100% (142/142), done.

remote: Total 234 (delta 80), reused 232 (delta 78), pack-reused 0

Receiving objects: 100% (234/234), 4.16 MiB | 14.14 MiB/s, done.

Resolving deltas: 100% (80/80), done.

安装项目依赖

随后进入项目的目录，安装依赖：

#@title 安装所需要的依赖

%cd /content/Bert-vits2-V2.3

!pip install -r requirements.txt

下载必要的模型

新增单元格，下载模型：

#@title 下载必要的模型

!wget -P slm/wavlm-base-plus/ https://huggingface.co/microsoft/wavlm-base-plus/resolve/main/pytorch_model.bin

!wget -P emotional/clap-htsat-fused/ https://huggingface.co/laion/clap-htsat-fused/resolve/main/pytorch_model.bin

!wget -P emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/ https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim/resolve/main/pytorch_model.bin

!wget -P bert/chinese-roberta-wwm-ext-large/ https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/resolve/main/pytorch_model.bin

!wget -P bert/bert-base-japanese-v3/ https://huggingface.co/cl-tohoku/bert-base-japanese-v3/resolve/main/pytorch_model.bin

!wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.bin

!wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.generator.bin

!wget -P bert/deberta-v2-large-japanese/ https://huggingface.co/ku-nlp/deberta-v2-large-japanese/resolve/main/pytorch_model.bin

下载底模文件

接着下载预训练模型的底模：

#@title 下载底模文件  

!wget -P Data/ada/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/DUR_0.pth

!wget -P Data/ada/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/D_0.pth

!wget -P Data/ada/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/G_0.pth

!wget -P Data/ada/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/WD_0.pth

注意2.3版本的底模是4个。

切分数据集

接着把艾达王的音频素材上传到Data/ada/raw/ada.wav

随后新建单元格：

#@title 切分数据集  

!python3 audio_slicer.py

素材就会被切分。

转写和标注

此时我们需要把切片素材转写：

#@title 转写和标注

!pip install git+https://github.com/openai/whisper.git

!python3 short_audio_transcribe.py

注意这里单独安装whisper，很多人直接用 pip install whisper，其实这不是正确的安装方式，需要单独指定安装源：pip install git+https://github.com/openai/whisper.git，切记，否则会报错。

执行完毕后会在角色目录生成转写文件esd.list:

./Data\ada\wavs\ada_0.wav|ada|EN|I do. The kind you like.

./Data\ada\wavs\ada_1.wav|ada|EN|Now where's the amber?

./Data\ada\wavs\ada_10.wav|ada|EN|Leave the girl. She's lost no matter what.

./Data\ada\wavs\ada_11.wav|ada|EN|You walk away now, and who knows?

./Data\ada\wavs\ada_12.wav|ada|EN|Maybe you'll live to meet me again.

./Data\ada\wavs\ada_13.wav|ada|EN|And I might get you that greeting you were looking for.

./Data\ada\wavs\ada_14.wav|ada|EN|How about we continue this discussion another time?

./Data\ada\wavs\ada_15.wav|ada|EN|Sorry, nothing yet.

./Data\ada\wavs\ada_16.wav|ada|EN|But my little helper is creating

./Data\ada\wavs\ada_17.wav|ada|EN|Quite the commotion.

./Data\ada\wavs\ada_18.wav|ada|EN|Everything will work out just fine.

./Data\ada\wavs\ada_19.wav|ada|EN|He's a good boy. Predictable.

./Data\ada\wavs\ada_2.wav|ada|EN|The deal was, we get you out of here when you deliver the amber. No amber, no protection, Louise.

./Data\ada\wavs\ada_20.wav|ada|EN|Nothing personal, Leon.

./Data\ada\wavs\ada_21.wav|ada|EN|Louise and I had an arrangement.

./Data\ada\wavs\ada_22.wav|ada|EN|Don't worry, I'll take good care of it.

./Data\ada\wavs\ada_23.wav|ada|EN|Just one question.

./Data\ada\wavs\ada_24.wav|ada|EN|What are you planning to do with this?

./Data\ada\wavs\ada_25.wav|ada|EN|So, we're talking millions of casualties?

./Data\ada\wavs\ada_26.wav|ada|EN|We're changing course. Now.

./Data\ada\wavs\ada_3.wav|ada|EN|You can stop right there, Leon.

./Data\ada\wavs\ada_4.wav|ada|EN|wouldn't make me use this.

./Data\ada\wavs\ada_5.wav|ada|EN|Would you? You don't seem surprised.

./Data\ada\wavs\ada_6.wav|ada|EN|Interesting.

./Data\ada\wavs\ada_7.wav|ada|EN|Not a bad move

./Data\ada\wavs\ada_8.wav|ada|EN|Very smooth. Ah, Leon.

./Data\ada\wavs\ada_9.wav|ada|EN|You know I don't work and tell.

这里一共27条切片语音，对应27个转写文本，注意语言是英语。

音频重新采样

对素材音频进行重新采样的操作：

#@title 重新采样

!python3 resample.py --sr 44100 --in_dir ./Data/ada/raw/ --out_dir ./Data/ada/wavs/

预处理标签文件

接着处理转写文件，生成训练集和验证集：

#@title 预处理标签文件

!python3 preprocess_text.py --transcription-path ./Data/ada/esd.list --t

程序返回：

pytorch_model.bin: 100% 1.32G/1.32G [00:10<00:00, 122MB/s]

spm.model: 100% 2.46M/2.46M [00:00<00:00, 115MB/s]

The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.

0it [00:00, ?it/s]

[nltk_data] Downloading package averaged_perceptron_tagger to

[nltk_data]     /root/nltk_data...

[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.

[nltk_data] Downloading package cmudict to /root/nltk_data...

[nltk_data]   Unzipping corpora/cmudict.zip.

100% 27/27 [00:00<00:00, 4457.63it/s]

总重复音频数：0，总未找到的音频数:0

训练集和验证集生成完成！

生成 BERT 特征文件

最后生成bert特征文件：

#@title 生成 BERT 特征文件

!python3 bert_gen.py --config-path ./Data/ada/configs/config.json

对应27个素材：

100% 27/27 [00:33<00:00,  1.25s/it]

bert生成完毕!, 共有27个bert.pt生成!

模型训练

万事俱备，开始训练：

#@title 开始训练

!python3 train_ms.py

模型会在models目录生成，项目默认设置了训练间隔是50步，可以根据自己的需求修改config.json配置文件。

模型推理

一般情况下，训练了50步或者100步左右，可以推理一下查看效果，然后继续训练：

#@title 开始推理

!python3 webui.py

| numexpr.utils | INFO | NumExpr defaulting to 2 threads.

/usr/local/lib/python3.10/dist-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.

  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")

| utils | INFO | Loaded checkpoint 'Data/ada/models/G_150.pth' (iteration 25)

推理页面已开启!

Running on local URL:  http://127.0.0.1:7860

Running on public URL: https://814833a6f477ba151c.gradio.live

点击第二个公网地址进行推理即可。

结语

至此，我们已经完成了基于JupyterNoteBook的数据切分、转写、预处理、训练以及推理流程。最后奉上线上GoogleColab，以飨众乡亲：

https://colab.research.google.com/drive/1-H1DGG5dTy8u_8vFbq1HACXPX9AAM76s?usp=sharing