File size: 3,059 Bytes
8718761 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import warnings
import importlib.metadata
import whisper.tokenizer
from .utils import get_func_parameters
_COMPATIBLE_WHISPER_VERSIONS = (
'20230314',
'20230918',
'20231105',
'20231106',
'20231117',
)
_required_whisper_ver = _COMPATIBLE_WHISPER_VERSIONS[-1]
_TOKENIZER_PARAMS = get_func_parameters(whisper.tokenizer.get_tokenizer)
def warn_compatibility_issues(
whisper_module,
ignore: bool = False,
additional_msg: str = ''
):
compatibility_warning = ''
if not ignore:
if whisper_module.__version__ not in _COMPATIBLE_WHISPER_VERSIONS:
compatibility_warning += (f'Whisper {whisper_module.__version__} is installed.'
f'Versions confirm to be compatible: {", ".join(_COMPATIBLE_WHISPER_VERSIONS)}\n')
_is_whisper_repo_version = bool(importlib.metadata.distribution('openai-whisper').read_text('direct_url.json'))
if _is_whisper_repo_version:
compatibility_warning += ('The detected version appears to be installed from the repository '
'which can have compatibility issues '
'due to multiple commits sharing the same version number. '
f'It is recommended to install version {_required_whisper_ver} from PyPI.\n')
if compatibility_warning:
compatibility_warning = (
'The installed version of Whisper might be incompatible.\n'
+ compatibility_warning +
'To prevent errors and performance issues, reinstall correct version with: '
f'"pip install --upgrade --no-deps --force-reinstall openai-whisper=={_required_whisper_ver}".'
)
if additional_msg:
compatibility_warning += f' {additional_msg}'
warnings.warn(compatibility_warning)
def get_tokenizer(model=None, is_faster_model: bool = False, **kwargs):
"""
Backward compatible wrapper of :func:`whisper.tokenizer.get_tokenizer` and
:class:`faster_whisper.tokenizer.Tokenizer`.
"""
if is_faster_model:
import faster_whisper.tokenizer
tokenizer = faster_whisper.tokenizer.Tokenizer
params = get_func_parameters(tokenizer)
if model is not None and 'tokenizer' not in kwargs:
kwargs['tokenizer'] = model.hf_tokenizer
else:
tokenizer = whisper.tokenizer.get_tokenizer
params = _TOKENIZER_PARAMS
if model is not None and 'multilingual' not in kwargs:
kwargs['multilingual'] = \
(model.is_multilingual if hasattr(model, 'is_multilingual') else model.model.is_multilingual)
if 'num_languages' in params:
if hasattr(model, 'num_languages'):
kwargs['num_languages'] = \
(model.num_languages if hasattr(model, 'num_languages') else model.model.num_languages)
elif 'num_languages' in kwargs:
del kwargs['num_languages']
return tokenizer(**kwargs)
|