* Allow dict input for audio classification pipeline
* make style
* Empty commit to trigger CI
* Empty commit to trigger CI
* check for torchaudio
* add pip instructions
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>
* Update src/transformers/pipelines/audio_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* asr -> audio class
* asr -> audio class
---------
Co-authored-by: Sylvain <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* let's go!
* initial implementation of token-level timestamps
* only return a single timestamp per token
* remove token probabilities
* fix return type
* fix doc comment
* strip special tokens
* rename
* revert to not stripping special tokens
* only support models that have alignment_heads
* add integration test
* consistently name it token-level timestamps
* small DTW tweak
* initial support for ASR pipeline
* fix pipeline doc comments
* resolve token timestamps in pipeline with chunking
* change warning when no final timestamp is found
* return word-level timestamps
* fixup
* fix bug that skipped final word in each chunk
* fix failing unit tests
* merge punctuations into the words
* also return word tokens
* also return token indices
* add (failing) unit test for combine_tokens_into_words
* make combine_tokens_into_words private
* restore OpenAI's punctuation rules
* add pipeline tests
* make requested changes
* PR review changes
* fix failing pipeline test
* small stuff from PR
* only return words and their timestamps, not segments
* move alignment_heads into generation config
* forgot to set alignment_heads in pipeline tests
* tiny comment fix
* grr
* Chunkable classification pipeline
The TokenClassificationPipeline is now able to process sequences longer than 512. No matter the framework, the model, the tokenizer. We just have to pass process_all=True and a stride number (optional). The behavior remains the same if you don't pass these optional parameters. For overlapping parts when using stride above 0, we consider only the max scores for each overlapped token in all chunks where the token is.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* update with latest black format
* update black format
* Update token_classification.py
* Update token_classification.py
* format correction
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update comments
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Update token_classification.py
Correct spaces, remove process_all and keep only stride. If stride is provided, the pipeline is applied to the whole text.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update chunk aggregation
Update the chunk aggregation strategy based on entities aggregation.
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
Remove unnecessary pop from outputs dict
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add chunking tests
* correct formating
* correct formatting
* correct model id for test chunking
* update scores with nested simplify
* Update test_pipelines_token_classification.py
* Update test_pipelines_token_classification.py
* update model to a tiny one
* Update test_pipelines_token_classification.py
* Adding smaller test for chunking.
* Fixup
* Update token_classification.py
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/pipelines/token_classification.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [WIP] whisper refacto to support language output.
* Handling merges.
* A bit more cleanup and comments.
* Many improvements.
Lots of details everywhere.
* Cleanup old code and tests.
* Handle lone timestamp tokens (just recover when something bad happens).
* Adding return_language example.
* No ffmpeg.
* Hmm.
* Some corrections.
* Both fast and slow.
* New black.
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/whisper/tokenization_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove print.
* Undoing tests modifications.
* Smaller test modifications.
* Rename.
* Remove maxDiff.
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Mark pipeline tests to skip them easily
* Mark the mixin as pipeline test
* Update src/transformers/testing_utils.py
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
* add pipeline
* update init
* add zero shot to init
* update inits and correct checkpoints
* update base to support input features
* add tests
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Update src/transformers/pipelines/zero_shot_audio_classification.py
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* update pieline code
* use tiny checkpoint
* nits and expected value with tiny model
* style
* last nit on tests values
* fix styling
* fix collate fn that was casting t float
* update
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* fix: Change is_last chunk calc and add conditional break
* format fix
* account for 0 and full stride_rights, add comment
* add new test
* make style
* update slow whisper asr test timestamps
* use nested_simplify on output and round timestamp to hundreths place
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* update whisper logit processor
* add generate for whisper
* remove part of the whisper specific code from pipeline
* update logit processes
* major update
* enforce first timestamp
* update generate
* add more tests
* update new decoding strategy
* Apply suggestions from code review
* update docstring
* fixup
* default config will not have multilingual ar
* update expected tokenizer size, see pull on the hub for whisper-tiny
* Fixing the pipeline with image processor.
* Update the slow test.
* Using only the first image processor.
* Include exclusion mecanism for Image processor.
* Do not handle Gitconfig, deemed as a bug.
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Remove `conversational` changes. They are not supposed to be here.
* Address first row of comments.
* Remove OneFormer modifications.
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* major refactor
* add correct strides for tests
* Update src/transformers/pipelines/automatic_speech_recognition.py
* fix whisper timestamp test
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* add draft logit processor
* add template functions
* update timesapmt processor parameters
* draft script
* simplify code
* cleanup
* fixup and clean
* update pipeline
* style
* clean up previous idea
* add tokenization utils
* update tokenizer and asr output
* fit whisper type
* style and update test
* clean test
* style test
* update tests
* update error test
* udpate code (not based on review yet)
* update tokenization
* update asr pipeline
* update code
* cleanup and update test
* fmt
* remove text verificatino
* cleanup
* cleanup
* add model test
* update tests
* update code add docstring
* update code and add docstring
* fix pipeline tests
* add draft logit processor
add template functions
update timesapmt processor parameters
draft script
simplify code
cleanup
fixup and clean
update pipeline
style
clean up previous idea
add tokenization utils
update tokenizer and asr output
fit whisper type
style and update test
clean test
style test
update tests
update error test
udpate code (not based on review yet)
update tokenization
update asr pipeline
update code
cleanup and update test
fmt
remove text verificatino
cleanup
cleanup
add model test
update tests
update code add docstring
update code and add docstring
fix pipeline tests
* Small update.
* Fixup.
* Tmp.
* More support.
* Making `forced_decoder_ids` non mandatory for users to set.
* update and fix first bug
* properly process sequence right after merge if last
* tofo
* allow list inputs + compute begin index better
* start adding tests
* add the 3 edge cases
* style
* format sequences
* fixup
* update
* update
* style
* test passes, edge cases should be good
* update last value
* remove Trie
* update tests and expec ted values
* handle bigger chunk_length
* clean tests a bit
* refactor chunk iter and clean pipeline
* update tests
* style
* refactor chunk iter and clean pipeline
* upade
* resolve comments
* Apply suggestions from code review
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* take stride right into account
* update test expected values
* Update code based on review
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: sgugger <sylvain.gugger@gmail.com>
* Fixing #20783
* Update src/transformers/pipelines/base.py
* Fixing some tests.
* Fixup.
* Remove ffmpeg dep + a bit more relaxed for bigbird QA precision.
* Better dataset.
* Prevent failing on TF.
* Better condition. We can't use `can_use_iterator` since we cannot use it
directly.
* add torch_dtype attribute to Pipeline
* Use torch_dtype to cast input tensor type in AutomaticSpeechRecognitionPipeline
* Fix code quality
* Add TextGenerationPipeline fp16 test
* Fix code quality
* Remove useless require in tests
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Supporting `fp16` for asr pipeline
* Adding test.
* Style.
* Oops.
* Flake8 update ?
* Fixing flake8 ?
* Revert "Flake8 update ?"
This reverts commit 0b917fcb52.
* Style (acctidentally deleted flake8 F401.)
* Move to a bigger test (no small whisper model, and s2t doesn't seem to
accept torch_dtype=fp16).
Also we need to use a GPU to actually compute on fp16.
* Using BatchFeature capability.
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.
This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.
The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.
The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.
The pipeline is changed to `ChunkPipeline` to support batching cleanly.
This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.
**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.
**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.
**Breaking change**: The types is now simplified and flattened:
`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.
It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.
* Fixing the doctests.
* Behind is_torch_available.
* Fix ImageSegmentationPipelineTests
* Use 0.9
* no zip
* links to show images
* links to show images
* rebase
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Adding support for LayoutLMvX variants for `object-detection`.
* Revert bogs `layoutlm` feature extractor which does not exist (it was a
V2 model) .
* Updated condition.
* Handling the comments.
* Factored out some code in the image-segmentation pipeline
Re-enable `small_model_pt`.
Re-enable `small_model_pt`.
Enabling the current test with the current values.
Debugging the values on the CI.
More logs ? Printing doesn't work ?
Using the CI values instead. Seems to be a Pillow sensitivity.
Added a test showcasing that models not supporting some tasks get a
clear error.
Factored out code.
Further factor out.
Fixup.
Bad rebase.
Put `panoptic` before `instance` as it should be a superset.
* Fixing tests.
* Adding subtasks tests
+ Fixes `instance` segmentation which was broken due to default and
non kwargs arguments.
* Fix bad replace.
* Re-enable `small_model_pt`.
Re-enable `small_model_pt`.
Enabling the current test with the current values.
Debugging the values on the CI.
More logs ? Printing doesn't work ?
Using the CI values instead. Seems to be a Pillow sensitivity.
* Update src/transformers/pipelines/image_segmentation.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Clean up deprecation warnings
Notes:
Changed some strings in tests to raw strings, which will change the literal content of the strings as they are fed into whatever machine handles them.
Test cases for past in the past/past_key_values switch changed/removed due to warning of impending removal
* Add PILImageResampling abstraction for PIL.Image.Resampling
This PR (https://github.com/huggingface/transformers/pull/19367) introduced a few breaking changes:
- Removed an argument `mask_threshold`.
- Broke the default behavior (instance vs panoptic in the function call)
https://github.com/huggingface/transformers/pull/19367/files#diff-60f846b86fb6a21d4caf60f5b3d593a04accb8f248de3029cccae2ff898c5bc3R119-R120
- Broke the actual masks: https://github.com/huggingface/transformers/pull/1961
This PR is the start of a handful that will aim at bringing back the old
behavior(s).
- tests should not have to specify `task` by default, unless we want to
modify the behavior and have a lower form of segmentation running)
- `test_small_model_pt` should be working.
This specific PR starts with adding more information to the masks hash
because missing the actual mask was actual easy to miss (the hashes do
change, but it was easy to miss that one code path wasn't properly
updated).
So we go from a simple `hash` to
```
{"hash": #smaller hash, "shape": (h, w), "white_pixels": n}
```
The `shape` should help make sure the interpolation of the mask works
correctly, the `white_pixels` hopefully helps detect big regressions in
their amount when the hash gets modified.
* add return_tensors parameter for feature_extraction w/ test
add return_tensor parameter for feature extraction
Revert "Merge branch 'feature-extraction-return-tensor' of https://github.com/ajsanjoaquin/transformers into feature-extraction-return-tensor"
This reverts commit d559da743b87914e111a84a98ba6dbb70d08ad88, reversing
changes made to bbef89278650c04c090beb65637a8e9572dba222.
call parameter directly
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Fixup.
Update src/transformers/pipelines/feature_extraction.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Fix the imports.
* Fixing the test by not overflowing the model capacity.
Co-authored-by: AJ San Joaquin <ajsanjoaquin@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Remove key word argument X from pipeline predict and transform methods
As __call__ of pipeline clasees require one positional argument, passing
the input as a keyword argument inside predict, transform methods, causing
__call__ to fail. Hence in this commit the keyword argument is modified
into positional argument.
* Implement basic tests for scikitcompat pipeline interface
* Seperate tests instead of running with parameterized based on framework as both frameworks will not be active at the same time
* Improve error messaging for ASR pipeline.
- Raise error early (in `_sanitize`) so users don't waste time trying to
run queries with invalid params.
- Fix the error was after using `config.inputs_to_logits_ratio` so our
check was masked by the failing property does not exist.
- Added some manual check on s2t for the error message.
No non ctc model seems to be used by the default runner (they are all
skipped).
* Removing pdb.
* Stop the early error it doesn't really work :(.
* Add initial files for depth estimation pipelines
* Add test file for depth estimation pipeline
* Update model mapping names
* Add updates for depth estimation output
* Add generic test
* Hopefully fixing the tests.
* Check if test passes
* Add make fixup and make fix-copies changes after rebase with main
* Rebase with main
* Fixing up depth pipeline.
* This is not used anymore.
* Fixing the test. `Image` is a module `Image.Image` is the type.
* Update docs/source/en/main_classes/pipelines.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* update feature extractor params
* update attention mask handling
* fix doc and pipeline test
* add warning when skipping test
* add whisper translation and transcription test
* fix build doc test
* Rework pipeline tests
* Try to fix Flax tests
* Try to put it before
* Use a new decorator instead
* Remove ignore marker since it doesn't work
* Filter pipeline tests
* Woopsie
* Use the fitlered list
* Clean up and fake modif
* Remove init
* Revert fake modif
