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Wav2Vec2 (#9659)

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* add raw scaffold

* implement feat extract layers

* make style

* remove +

* correctly convert weights

* make feat extractor work

* make feature extraction proj work

* run forward pass

* finish forward pass

* Succesful decoding example

* remove unused files

* more changes

* add wav2vec tokenizer

* add new structure

* fix run forward

* add other layer norm architecture

* finish 2nd structure

* add model tests

* finish tests for tok and model

* clean-up

* make style

* finish docstring for model and config

* make style

* correct docstring

* correct tests

* change checkpoints to fairseq

* fix examples

* finish wav2vec2

* make style

* apply sylvains suggestions

* apply lysandres suggestions

* change print to log.info

* re-add assert statement

* add input_values as required input name

* finish wav2vec2 tokenizer

* Update tests/test_tokenization_wav2vec2.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* apply sylvains suggestions

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Patrick von Platen
2021-02-02 15:52:10 +03:00
committed by GitHub
parent d996024af7
commit d6217fb30c
20 changed files with 2233 additions and 5 deletions
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12
src/transformers/file_utils.py
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@@ -155,6 +155,14 @@ except importlib_metadata.PackageNotFoundError:
_scatter_available = False
_soundfile_available = importlib.util.find_spec("soundfile") is not None
try:
_soundfile_version = importlib_metadata.version("soundfile")
logger.debug(f"Successfully imported soundfile version {_soundfile_version}")
except importlib_metadata.PackageNotFoundError:
_soundfile_available = False
torch_cache_home = os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
old_default_cache_path = os.path.join(torch_cache_home, "transformers")
# New default cache, shared with the Datasets library
@@ -311,6 +319,10 @@ def is_sagemaker_distributed_available():
return importlib.util.find_spec("smdistributed") is not None
def is_soundfile_availble():
return _soundfile_available
def torch_only_method(fn):
def wrapper(*args, **kwargs):
if not _torch_available: