* More limited setup -> setupclass conversion
* make fixup
* Trigger tests
* Fixup UDOP
* Missed a spot
* tearDown -> tearDownClass where appropriate
* Couple more class fixes
* Fixups for UDOP and VisionTextDualEncoder
* Ignore errors when removing the tmpdir, in case it already got cleaned up somewhere
* CLIP fixes
* More correct classmethods
* Wav2Vec2Bert fixes
* More methods become static
* More class methods
* More class methods
* Revert changes for integration tests / modeling files
* Use a different tempdir for tests that actually write to it
* Remove addClassCleanup and just use teardownclass
* Remove changes in modeling files
* Cleanup get_processor_dict() for got_ocr2
* Fix regression on Wav2Vec2BERT test that was masked by this before
* Rework tests that modify the tmpdir
* make fix-copies
* revert clvp modeling test changes
* Fix CLIP processor test
* make fix-copies
* tmp commit
* move tests to the right class
* remove ALL all_generative_model_classes = ...
* skip tf roberta
* skip InstructBlipForConditionalGenerationDecoderOnlyTest
* videollava
* reduce diff
* reduce diff
* remove on vlms
* fix a few more
* manual rebase bits
* more manual rebase
* remove all manual generative model class test entries
* fix up to ernie
* a few more removals
* handle remaining cases
* recurrent gemma
* it's better here
* make fixup
* tf idefics is broken
* tf bert + generate is broken
* don't touch tf :()
* don't touch tf :(
* make fixup
* better comments for test skips
* revert tf changes
* remove empty line removal
* one more
* missing one
* use torch.testing.assertclose instead to get more details about error in cis
* fix
* style
* test_all
* revert for I bert
* fixes and updates
* more image processing fixes
* more image processors
* fix mamba and co
* style
* less strick
* ok I won't be strict
* skip and be done
* up
* add more cases
* fix method not found in unittest
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* fix more cases
* add more models
* add all
* no unittest.case
* remove for oneformer
* fix style
---------
Signed-off-by: Lin, Fanli <fanli.lin@intel.com>
* idefics2 enable_input_require_grads not aligned with disable_input_require_grads
make peft+idefics2 checkpoints disable fail
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* split test case
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* fix ci failure
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* refine test
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi <yi.a.wang@intel.com>
* Pass datasets trust_remote_code
* Pass trust_remote_code in more tests
* Add trust_remote_dataset_code arg to some tests
* Revert "Temporarily pin datasets upper version to fix CI"
This reverts commit b7672826ca.
* Pass trust_remote_code in librispeech_asr_dummy docstrings
* Revert "Pin datasets<2.20.0 for examples"
This reverts commit 833fc17a3e.
* Pass trust_remote_code to all examples
* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects
* Pass trust_remote_code to tests
* Pass trust_remote_code to docstrings
* Fix flax examples tests requirements
* Pass trust_remote_dataset_code arg to tests
* Replace trust_remote_dataset_code with trust_remote_code in one example
* Fix duplicate trust_remote_code
* Replace args.trust_remote_dataset_code with args.trust_remote_code
* Replace trust_remote_dataset_code with trust_remote_code in parser
* Replace trust_remote_dataset_code with trust_remote_code in dataclasses
* Replace trust_remote_dataset_code with trust_remote_code arg
* Rename to test_model_common_attributes
The method name is misleading - it is testing being able to get and set embeddings, not common attributes to all models
* Explicitly skip
* add tests for batching support
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/test_modeling_common.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* fixes and comments
* use cosine distance for conv models
* skip mra model testing
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* finzalize and make style
* check model type by input names
* Update tests/models/vilt/test_modeling_vilt.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* fixed batch size for all testers
* Revert "fixed batch size for all testers"
This reverts commit 525f3a0a05.
* add batch_size for all testers
* dict from model output
* do not skip layoutlm
* bring back some code from git revert
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/test_modeling_common.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* clean-up
* where did minus go in tolerance
* make whisper happy
* deal with consequences of losing minus
* deal with consequences of losing minus
* maskformer needs its own test for happiness
* fix more models
* tag flaky CV models from Amy's approval
* make codestyle
---------
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix bug in SpeechT5 speech decoder prenet's forward method
- Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues.
- Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact.
- This change resolves a critical bug affecting the model's performance in handling speaker embeddings.
* Refactor SpeechT5 text to speech integration tests
- Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite.
- Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations.
- Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing.
- Fixed existing test cases where incorrect assumptions about output shapes led to potential errors.
* Fix bug in SpeechT5 speech decoder prenet's forward method
- Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues.
- Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact.
- This change resolves a critical bug affecting the model's performance in handling speaker embeddings.
* Refactor SpeechT5 text to speech integration tests
- Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite.
- Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations.
- Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing.
- Fixed existing test cases where incorrect assumptions about output shapes led to potential errors.
* Enhance handling of speaker embeddings in SpeechT5
- Refined the generate and generate_speech functions in the SpeechT5 class to robustly handle two scenarios for speaker embeddings: matching the batch size (one embedding per sample) and one-to-many (a single embedding for all samples in the batch).
- The update includes logic to repeat the speaker embedding when a single embedding is provided for multiple samples, and a ValueError is raised for any mismatched dimensions.
- Also added corresponding test cases to validate both scenarios, ensuring complete coverage and functionality for diverse speaker embedding situations.
* Improve Test Robustness with Randomized Speaker Embeddings
* try to stylify using ruff
* might need to remove these changes?
* use ruf format andruff check
* use isinstance instead of type comparision
* use # fmt: skip
* use # fmt: skip
* nits
* soem styling changes
* update ci job
* nits isinstance
* more files update
* nits
* more nits
* small nits
* check and format
* revert wrong changes
* actually use formatter instead of checker
* nits
* well docbuilder is overwriting this commit
* revert notebook changes
* try to nuke docbuilder
* style
* fix feature exrtaction test
* remve `indent-width = 4`
* fixup
* more nits
* update the ruff version that we use
* style
* nuke docbuilder styling
* leve the print for detected changes
* nits
* Remove file I/O
Co-authored-by: charliermarsh
<charlie.r.marsh@gmail.com>
* style
* nits
* revert notebook changes
* Add # fmt skip when possible
* Add # fmt skip when possible
* Fix
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* More ` # fmt: skip` usage
* NIts
* more fixes
* fix tapas
* Another way to skip
* Recommended way
* Fix two more fiels
* Remove asynch
Remove asynch
---------
Co-authored-by: charliermarsh <charlie.r.marsh@gmail.com>
* fix speecht5 wrong attention mask when padding
* enable batch generation and add parameter attention_mask
* fix doc
* fix format
* batch postnet inputs, return batched lengths, and consistent to old api
* fix format
* fix format
* fix the format
* fix doc-builder error
* add test, cross attention and docstring
* optimize code based on reviews
* docbuild
* refine
* not skip slow test
* add consistent dropout for batching
* loose atol
* add another test regarding to the consistency of vocoder
* fix format
* refactor
* add return_concrete_lengths as parameter for consistency w/wo batching
* fix review issues
* fix cross_attention issue
* stronger GC tests
* better tests and skip failing tests
* break down into 3 sub-tests
* break down into 3 sub-tests
* refactor a bit
* more refactor
* fix
* last nit
* credits contrib and suggestions
* credits contrib and suggestions
---------
Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add: NumberNormalizer works for integers, floats, common currencies, negative numbers and percentages
* fix: renamed number normalizer class and added normalization to SpeechT5Processor
* fix: restyled with black and ruff, should pass code quality tests
* fix: moved normalization to tokenizer and other small changes to normalizer
* add: test for normalization and changed the existing full tokenizer test
* fix: tokenization tests now pass, made changes to existing tokenization where normalization is covered; added normalize arg to func signature
* fix: changed default normalize setting to False, modified the tests a bit
* fix: added support for comma separated numbers, tokenization on the fly with kwargs and normalizer getter setter funcs
* Fix TypeError: Object of type int64 is not JSON serializable
* Convert numpy.float64 and numpy.int64 to float and int for json serialization
* Black reformatted examples/pytorch/token-classification/run_ner_no_trainer.py
* * make style
* wrong argument name
* append eos_token_id
* all tokenizers need mask and ctc_blank tokens
* remove reduction factor from feature extractor
* add proper TTS loss
* did shifting the wrong way around
* mask out padded portions
* remove logits again (don't really need it)
* fix unit tests
* fixup
* pad also returns the decoder attention mask, since that's useful to have
* clean up feature extractor logic
* pad can handle TTS task too
* remove stop_labels from loss calculation
* simplify logic
* fixup
* do -100 masking properly
* small STFT optimization (calculate mel filterbanks only once)
* replace torchaudio fbanks with audio_utils
* remove torchaudio dependency
* simplify & speed up the STFT
* don't serialize window and mel filters
* output cross attentions when generating speech
* add guided attention loss
* fix failing test
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* Update src/transformers/models/speecht5/modeling_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* change type annotation of attention_mask to LongTensor
* extract loss into class
* remove unused frame_signal_scale argument
* use config object in loss class
* fix type annotations in doc comments
* change optional to just bool
* implement missing tokenizer method
* add deprecation warning
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add deprecation warning for stop_labels
---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
