* Fix loading pipelines with wav2vec models with lm when in local paths
* Adding tests
* Fix test
* Adding tests
* Flake8 fixes
* Removing conflict files :(
* Adding task type to test
* Remove unnecessary test and imports
* Rework AutoFeatureExtractor.from_pretrained internal
* Custom feature extractor
* Add more tests
* Add support for custom feature extractor code
* Clean up
* Add register API to AutoFeatureExtractor
* Compute loss independent from decoder (as 14139)
* fix expected seq_len + style
* Apply the same change to TFVisionEncoderDecoderModel
* fix style
* Add case with labels in equivalence test
* uncomment
* Add case with labels in equivalence test
* add decoder_token_labels
* use hf_compute_loss
* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add copied from
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Add TensorFlow support for ONNX export
* Change documentation to mention conversion with Tensorflow
* Refactor export into export_pytorch and export_tensorflow
* Check model's type instead of framework installation to choose between TF and Pytorch
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Alberto Bégué <alberto.begue@della.ai>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
* added classes to get started with constrained beam search
* in progress, think i can directly force tokens now but not yet with the round robin
* think now i have total control, now need to code the bank selection
* technically works as desired, need to optimize and fix design choices leading to undersirable outputs
* complete PR #1 without disjunctive decoding
* removed incorrect tests
* Delete k.txt
* Delete test.py
* Delete test.sh
* revert changes to test scripts
* genutils
* full implementation with testing, no disjunctive yet
* shifted docs
* passing all tests realistically ran locally
* removing accidentally included print statements
* fixed source of error in initial PR test
* fixing the get_device() vs device trap
* fixed documentation docstrings about constrained_beam_search
* fixed tests having failing for Speech2TextModel's floating point inputs
* fix cuda long tensor
* added examples and testing for them and founx & fixed a bug in beam_search and constrained_beam_search
* deleted accidentally added test halting code with assert False
* code reformat
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Update tests/test_generation_utils.py
* fixing based on comments on PR
* took out the testing code that should but work fails without the beam search moditification ; style changes
* fixing comments issues
* docstrings for ConstraintListState
* typo in PhrsalConstraint docstring
* docstrings improvements
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* typical decoding
* changing arg name
* add test config params
* forgotten arg rename
* fix edge case where scores are same
* test for typical logits warper
* code quality fixes
* Add wrapper classes
* convert inner layers to tf
* Add TF Encoder and Decoder layers
* TFSpeech2Text models
* Loadable model
* TF model with same outputs as PT model
* test skeleton
* correct tests and run the fixup
* correct attention expansion
* TFSpeech2Text pask_key_values with TF format
* electra is added to onnx supported model
* add google/electra-base-generator for test onnx module
Co-authored-by: Lewis Tunstall <lewis.c.tunstall@gmail.com>
* Change the way tracing happens, enabling dynamic axes out of the box
* Update the tests and modeling xlnet
* Add the non recoding of leaf modules to avoid recording more values for the methods to record than what will be seen at tracing time (which would otherwise desynchronize the recorded values and the values that need to be given to the proxies during tracing, causing errors).
* Comments and making tracing work for gpt-j and xlnet
* Refactore things related to num_choices (and batch_size, sequence_length)
* Update fx to work on PyTorch 1.10
* Postpone autowrap_function feature usage for later
* Add copyrights
* Remove unnecessary file
* Fix issue with add_new_model_like
* Apply suggestions
* Wav2Vec2 models must either throw or deal with add_apater
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Add pre-add_adapter backwards compatibility
* Add pre-add_adapter backwards compatibility
* Fix issue in tests/test_modeling_wav2vec2.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
# Add support for W&B hyperparameter sweep
This PR:
* allows using wandb for running hyperparameter search.
* The runs are visualized on W&B sweeps dashboard
* This supports runnning sweeps on parallel devices, all reporting to the same central dashboard.
### Usage
**To run new a hyperparameter search:**
```
trainer.hyperparameter_search(
backend="wandb",
project="transformers_sweep", # name of the project
n_trials=5,
metric="eval/loss", # metric to be optimized, default 'eval/loss'. A warning is raised if the passed metric is not found
)
```
This outputs a sweep id. Eg. `my_project/sweep_id`
**To run sweeps on parallel devices:**
Just pass sweep id which you want to run parallel
```
trainer.hyperparameter_search(
backend="wandb",
sweep_id = "my_project/sweep_id"
)
```
* Adding support for `microphone` streaming within pipeline.
- Uses `ffmpeg` to get microphone data.
- Makes sure alignment is made to `size_of_sample`.
- Works by sending `{"raw": ..data.., "stride": (n, left, right),
"partial": bool}`
directly to the pipeline enabling to stream partial results and still
get inference.
- Let's `partial` information flow through the pipeline to enable caller
to get it back and choose to display text or not.
- The striding reconstitution is bound to have errors since CTC does not
keep previous state. Currently most of the errors are we don't know if
there's a space or not between two chunks.
Since we have some left striding info, we could use that during decoding
to choose what to do with those spaces and even extra letters maybe (if
the stride is long enough, it's bound to cover at least a few symbols)
Fixing tests.
Protecting with `require_torch`.
`raw_ctc` support for nicer demo.
Post rebase fixes.
Revamp to split raw_mic_data from it's live chunking.
- Requires a refactor to make everything a bit cleaner.
Automatic resampling.
Small fix.
Small fix.
* Post rebase fix (need to let super handle more logic, reorder args.)
* Update docstrings
* Docstring format.
* Remove print.
* Prevent flow of `input_values`.
* Fixing `stride` too.
* Fixing the PR by removing `raw_ctc`.
* Better docstrings.
* Fixing init.
* Update src/transformers/pipelines/audio_utils.py
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Update tests/test_pipelines_automatic_speech_recognition.py
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Quality.
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
* Add torchvision's resize
* Rename torch_resize to default_to_square
* Apply suggestions from code review
* Add support for default_to_square and tuple of length 1
* add new test
* update test
* remove `tokenizer_file` from `additional_files_names` in `tokenization_utils_base.py`
* add `tokenizer_file` for the fast only tokenizer
* change global variables layoutxml
* remove `"tokenizer_file"` from DPR tokenizer's Global variables
* remove `tokenizer_file` from herbert slow tokenizer init
* `"tokenizer_file"` from LED tokenizer's Global variables
* remove `tokenizer_file` from mbart slow tokenizer init
* remove `tokenizer_file` from slow tokenizer template
* adapt to versioning
* adapt the `test_tokenizer_mismatch_warning` test
* clean test
* clarify `VOCAB_FILES_NAMES` in tokenization_utils_fast.py
* Revert "remove `tokenizer_file` from mbart slow tokenizer init"
This reverts commit 0dbb723fa9.
* Revert "`"tokenizer_file"` from LED tokenizer's Global variables"
This reverts commit 5a3f879bdd.
* Revert "remove `tokenizer_file` from herbert slow tokenizer init"
This reverts commit f5e10007b7.
* Revert "remove `"tokenizer_file"` from DPR tokenizer's Global variables"
This reverts commit da0895330b.
* set `tokenizer_file` in super `__init__` of mbart
* replace assert with exception for `padding_side` arg in `PreTrainedTokenizerBase` `__init__`
* add test
* fix kwargs
* reformat test
* format
* format
* fix typo to render the documentation
* Update modeling_wav2vec2.py
With very tiny sound files (less than 0.1 seconds) the num_masked_span can be too long. The issue is described in issue #15366 and discussed with @patrickvonplaten.
* correct errors with mask time indices
* remove bogus file
* make fix-copies
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* Fix the inconsistency of loss calculation between PT/TF XLNetLMHeadModel
* overwrite test_loss_computation
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
