Dear Benoit Favre,

Your project is really important! Is it possible to customize for new language? If yes, could you tell short hints for it?

Thank you in advance!

Hi thanks for your effort on developing recasepunc! I know that you can't provide help for models not trained by you, but maybe you have an idea what's going wrong here:

I'm loading the model vosk-recasepunc-de-0.21 from https://alphacephei.com/vosk/models. When I do so, torch tells me that it can't find WordpieceTokenizer. Do you know why? Is the model incompatible?

Punc predict path: C:\Users\admin\meety\vosk-recasepunc-de-0.21\checkpoint Traceback (most recent call last): File "main2.py", line 120, in t = transcriber() File "main2.py", line 32, in init self.casePuncPredictor = CasePuncPredictor(punc_predict_path, lang="de") File "C:\Users\admin\meety\recasepunc.py", line 273, in init loaded = torch.load(checkpoint_path, map_location=device if torch.cuda.is_available() else 'cpu') File "C:\Users\admin\Anaconda3\envs\meety\lib\site-packages\torch\serialization.py", line 607, in load return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) File "C:\Users\admin\Anaconda3\envs\meety\lib\site-packages\torch\serialization.py", line 882, in _load result = unpickler.load() File "C:\Users\admin\Anaconda3\envs\meety\lib\site-packages\torch\serialization.py", line 875, in find_class return super().find_class(mod_name, name) AttributeError: Can't get attribute 'WordpieceTokenizer' on <module 'main' from 'main2.py'>

Hello, I'm trying to use example.py on a french model (fr.22000 or fr-txt.large.19000) But I have this error: raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for Model: Unexpected key(s) in state_dict: "bert.position_ids". I also tried with the following command, same error in output. python recasepunc.py predict fr.22000 < toto.txt > output.txt Do you have any advice? Thanks

Hi, on start punctuation app use about 9Gb RAM, but in one moment(in load model ). Then we need about 1.5GB. Can we reduce 9GB on start? maybe on start we check our model and it feature can be turn off?

When I use Russian model, it gives me this error:

WARNING: reverting to cpu as cuda is not available
Some weights of the model checkpoint at DeepPavlov/rubert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight']

 File "C:\pypy\rus\recasepunc.py", line 741, in <module>
    main(config, config.action, config.action_args)
  File "C:\pypy\rus\recasepunc.py", line 715, in main
    generate_predictions(config, *args)
  File "C:\pypy\rus\recasepunc.py", line 349, in generate_predictions
    for line in sys.stdin:
  File "C:\Users\Xenia\AppData\Local\Programs\Python\Python39\lib\codecs.py", line 322, in decode
    (result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xef in position 0: invalid continuation byte

 File "C:\Users\Xenia\AppData\Local\Programs\Python\Python39\lib\site-packages\flask\app.py", line 1796, in dispatch_request
    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)      
  File "C:\pypy\app.py", line 32, in process_audio
    cased = subprocess.check_output('python rus/recasepunc.py predict rus/checkpoint', shell=True, text=True, input=text)
  File "C:\Users\Xenia\AppData\Local\Programs\Python\Python39\lib\subprocess.py", 
line 420, in check_output
    return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
  File "C:\Users\Xenia\AppData\Local\Programs\Python\Python39\lib\subprocess.py", 
line 524, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command 'python rus/recasepunc.py predict rus/checkpoint' returned non-zero exit status 1.

Sorry for a long message, I'm not sure which of these messages are the most important. Should I use another version of transformers? I use transformers==4.16.2 and it works fine with English model.

Is it possible that export model to something that can be used in C++ using libtorch?

1. export existing model(checkpoint provided in this repo)
2. export model after I train with my own data which option above possible, or both?

I tried to use the french models (both fr.22000 and fr-txt.large.19000) on a very simple text:

j'aime les fleurs les olives et la raclette

When running python3 recasepunc.py predict fr.22000 < input.txt > output.txt (or with the other model), I get the following RuntimeError:

Traceback (most recent call last):
  File "/home/mael/charly/recasepunc/recasepunc.py", line 733, in <module>
    main(config, config.action, config.action_args)
  File "/home/mael/charly/recasepunc/recasepunc.py", line 707, in main
    generate_predictions(config, *args)
  File "/home/mael/charly/recasepunc/recasepunc.py", line 336, in generate_predictions
    model.load_state_dict(loaded['model_state_dict'])
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Model:
	Unexpected key(s) in state_dict: "bert.position_ids".

I tried the same with the english model, and it worked perfectly. Looks like something is broken with the french ones?

look at parameters below. They really became bool, i find this bug while debugging it. ''' if name == 'main': parser = argparse.ArgumentParser() parser.add_argument("action", help="train|eval|predict|tensorize|preprocess", type=str) ... parser.add_argument("--updates", help="number of training updates to perform", default=default_config.updates, type=bool) parser.add_argument("--period", help="validation period in updates", default=default_config.period, type=bool) parser.add_argument("--lr", help="learning rate", default=default_config.lr, type=bool) parser.add_argument("--dab-rate", help="drop at boundaries rate", default=default_config.dab_rate, type=bool) config = Config(**parser.parse_args().dict)

main(config, config.action, config.action_args)

'''

Hi, thank you for this repo! I'm trying to reproduce results for different language, so I'm using multilingual-bert fine-tuned to my language dataset. Everything goes well during preprocessing and training, the resuls are comparable with those for English and French (97-99% for case and punctuation).

But when I try to use trained model, it gives very poor results even for sentences from training dataset. It works, sometimes it puts capital letters or dots, but it's rare and mostly model can't handle. Also when I try to evaluate model with command from the README (also tried it for already used validation sets, for instance with command python recasepunc.py eval bertugan_casepunc.24000 valid.case+punc.x valid.case+punc.y) it gives error:

File "recasepunc.py", line 220, in batchify x = x[:(len(x) // max_length) * max_length].reshape(-1, max_length) TypeError: unhashable type: 'slice'

Sorry for pointing to two different problems in one Issue, but I though maybe it can be one common mistake for both cases.