* 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>
* 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
* 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>
* 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
* make SpeechT5 model by copying Wav2Vec2
* add paper to docs
* whoops added docs in wrong file
* remove SpeechT5Tokenizer + put CTC back in the name
* remove deprecated class
* remove unused docstring
* delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead
* remove classes we don't need right now
* initial stab at speech encoder prenet
* add more speech encoder prenet stuff
* improve SpeechEncoderPrenet
* add encoder (not finished yet)
* add relative position bias to self-attention
* add encoder CTC layers
* fix formatting
* add decoder from BART, doesn't work yet
* make it work with generate loop
* wrap the encoder into a speech encoder class
* wrap the decoder in a text decoder class
* changed my mind
* changed my mind again ;-)
* load decoder weights, make it work
* add weights for text decoder postnet
* add SpeechT5ForCTC model that uses only the encoder
* clean up EncoderLayer and DecoderLayer
* implement _init_weights in SpeechT5PreTrainedModel
* cleanup config + Encoder and Decoder
* add head + cross attention masks
* improve doc comments
* fixup
* more cleanup
* more fixup
* TextDecoderPrenet works now, thanks Kendall
* add CTC loss
* add placeholders for other pre/postnets
* add type annotation
* fix freeze_feature_encoder
* set padding tokens to 0 in decoder attention mask
* encoder attention mask downsampling
* remove features_pen calculation
* disable the padding tokens thing again
* fixup
* more fixup
* code review fixes
* rename encoder/decoder wrapper classes
* allow checkpoints to be loaded into SpeechT5Model
* put encoder into wrapper for CTC model
* clean up conversion script
* add encoder for TTS model
* add speech decoder prenet
* add speech decoder post-net
* attempt to reconstruct the generation loop
* add speech generation loop
* clean up generate_speech
* small tweaks
* fix forward pass
* enable always dropout on speech decoder prenet
* sort declaration
* rename models
* fixup
* fix copies
* more fixup
* make consistency checker happy
* add Seq2SeqSpectrogramOutput class
* doc comments
* quick note about loss and labels
* add HiFi-GAN implementation (from Speech2Speech PR)
* rename file
* add vocoder to TTS model
* improve vocoder
* working on tokenizer
* more better tokenizer
* add CTC tokenizer
* fix decode and batch_code in CTC tokenizer
* fix processor
* two processors and feature extractors
* use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2
* cleanup
* more cleanup
* even more fixup
* notebooks
* fix log-mel spectrograms
* support reduction factor
* fixup
* shift spectrograms to right to create decoder inputs
* return correct labels
* add labels for stop token prediction
* fix doc comments
* fixup
* remove SpeechT5ForPreTraining
* more fixup
* update copyright headers
* add usage examples
* add SpeechT5ProcessorForCTC
* fixup
* push unofficial checkpoints to hub
* initial version of tokenizer unit tests
* add slow test
* fix failing tests
* tests for CTC tokenizer
* finish CTC tokenizer tests
* processor tests
* initial test for feature extractors
* tests for spectrogram feature extractor
* fixup
* more fixup
* add decorators
* require speech for tests
* modeling tests
* more tests for ASR model
* fix imports
* add fake tests for the other models
* fixup
* remove jupyter notebooks
* add missing SpeechT5Model tests
* add missing tests for SpeechT5ForCTC
* add missing tests for SpeechT5ForTextToSpeech
* sort tests by name
* fix Hi-Fi GAN tests
* fixup
* add speech-to-speech model
* refactor duplicate speech generation code
* add processor for SpeechToSpeech model
* add usage example
* add tests for speech-to-speech model
* fixup
* enable gradient checkpointing for SpeechT5FeatureEncoder
* code review
* push_to_hub now takes repo_id
* improve doc comments for HiFi-GAN config
* add missing test
* add integration tests
* make number of layers in speech decoder prenet configurable
* rename variable
* rename variables
* add auto classes for TTS and S2S
* REMOVE CTC!!!
* S2S processor does not support save/load_pretrained
* fixup
* these models are now in an auto mapping
* fix doc links
* rename HiFiGAN to HifiGan, remove separate config file
* REMOVE auto classes
* there can be only one
* fixup
* replace assert
* reformat
* feature extractor can process input and target at same time
* update checkpoint names
* fix commit hash
