whisper-vits-japanese
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AlexandaJerry
Vits Japanese with Whisper as data processor (you can train your VITS even you only have audios)
usage: whisper [-h] [--model {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large}] [--device DEVICE] [--output_dir OUTPUT_DIR] [--verbose VERBOSE] [--task {transcribe,translate}] [--language {af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,hi,hr,ht,hu,hy,id,is,it,iw,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh,Afrikaans,Albanian,Amharic,Arabic,Armenian,Assamese,Azerbaijani,Bashkir,Basque,Belarusian,Bengali,Bosnian,Breton,Bulgarian,Burmese,Castilian,Catalan,Chinese,Croatian,Czech,Danish,Dutch,English,Estonian,Faroese,Finnish,Flemish,French,Galician,Georgian,German,Greek,Gujarati,Haitian,Haitian Creole,Hausa,Hawaiian,Hebrew,Hindi,Hungarian,Icelandic,Indonesian,Italian,Japanese,Javanese,Kannada,Kazakh,Khmer,Korean,Lao,Latin,Latvian,Letzeburgesch,Lingala,Lithuanian,Luxembourgish,Macedonian,Malagasy,Malay,Malayalam,Maltese,Maori,Marathi,Moldavian,Moldovan,Mongolian,Myanmar,Nepali,Norwegian,Nynorsk,Occitan,Panjabi,Pashto,Persian,Polish,Portuguese,Punjabi,Pushto,Romanian,Russian,Sanskrit,Serbian,Shona,Sindhi,Sinhala,Sinhalese,Slovak,Slovenian,Somali,Spanish,Sundanese,Swahili,Swedish,Tagalog,Tajik,Tamil,Tatar,Telugu,Thai,Tibetan,Turkish,Turkmen,Ukrainian,Urdu,Uzbek,Valencian,Vietnamese,Welsh,Yiddish,Yoruba}] [--temperature TEMPERATURE] [--best_of BEST_OF] [--beam_size BEAM_SIZE] [--patience PATIENCE] [--length_penalty LENGTH_PENALTY] [--suppress_tokens SUPPRESS_TOKENS] [--initial_prompt INITIAL_PROMPT]...
# Error: Fail to install pyopenjtalk in Colab 在安装`pyopenjtalk==0.1.3`时出现了错误 Error occured when install `pyopenjtalk==0.1.3` from requirements.txt ``` Collecting pyopenjtalk==0.1.3 (from -r requirements.txt (line 9)) Downloading pyopenjtalk-0.1.3.tar.gz (1.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5...
colab上torch版本1.12.1依旧报错 IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/mnt/d/work/whisper-vits-japanese/env/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/mnt/d/work/whisper-vits-japanese/env/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch...
这是部分日志图，难道是因为第一次训练还没有完全结束的原因吗？ [INFO] {'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 800, 'learning_rate': 0.0002, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 24, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 8192, 'init_lr_ratio': 1, 'warmup_epochs': 0,...
您好，我按照教學一步步執行單人訓練的步驟，在最後要進行訓練時遇到了以下錯誤： ``` IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/mnt/d/work/whisper-vits-japanese/env/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop data = fetcher.fetch(index) File "/mnt/d/work/whisper-vits-japanese/env/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 54, in...
经过了debug，发现调用remove_path函数的apply不起作用 但是作者的调用是明显有效的 于是在apply函数下又生成了一个文件，这才发现原来是斜杠方向在linux里和Windows里有本质不同 但是在程序的其他地方混合使用都是没问题的，但是apply函数并不是对路径进行处理，而是对“/”这个符号进行上纲上线的处理 这就导致id无法被切分 把函数修改成以下形式就行，\\的意思是避免被解读成转义字符 def remove_path(path): path = path.split('\\')[-1] return path
我在您的代码注释中野当然看到了 ``` #如果输出提示nophoneme这种情况，检查下/content/whisper-vits-japanese/filelists的两个txt文件里有没有出现英文转写 ``` 不过，我想知道更加具体一些的内容，请问英文转写是指什么呢？这里我不是很能够理解您的意思，还请见谅 这个路径下的四个文件都打开看过，文件名和您的视频中的是一样的 其中两个是包含了训练集内容的txt文件，大致看下来似乎都是日语，另外两个都是包含有箭头的注音标记 请问该如何判断是不是含有英文转写呢？以及如何处理这种情况 另外还有个问题我想问一下，在whisper的运行过程中，我看到有些句子里出现了日语掺杂英文单词的情况，请问这种情况是否会影响训练呢？ 希望能够得到帮助，谢谢！
若你使用了auto_ms.py来生成txt，则**必须**在Alignment and Text Conversion这一步应修改为 ``` !python preprocess.py --text_index 2 --text_cleaners japanese_cleaners --filelists /content/whisper-vits-japanese/filelists/train_filelist.txt /content/whisper-vits-japanese/filelists/val_filelist.txt ``` 由于多人训练的txt格式与单人训练不同，这里必须要将 `text_index` 参数修改为对应格式的下标，即 `2`。否则会clean错文本，变成将speakId给clean成数字的发音了。 另外，不清楚是我的环境的问题，在使用默认的numpy版本时在 `train_ms.py` 这一步时会报错： > ValueError: numpy.ndarray size changed, may indicate binary...
I found these problems when applying auto.py. These may be reason of [issue #2 ](https://github.com/AlexandaJerry/whisper-vits-japanese/issues/2). 1. 1st wrong call of `replace` would fail finding wav files in some situation 2....
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Vits Japanese with Whisper as data processor (you can train your VITS even you only have audios)