Add TF whisper (#19378)

* simplify loop

* add featur extractor

* add model

* start conversion

* add dropout

* initial commit of test files

* copnversion for all models

* update processor for correct padding

* update feature extraction

* update integration test logits match

* fmnt: off for the logits

* on the fly mel bank

* small nit

* update test

* update tokenizer

* nit feature extraction

* update

* update tokenizer test

* adds logit processor and update tokenizer to get supress tokens

* style

* clean convert

* revert to original modeling tf utils

* Update

* update

* nit

* clean convert file

* update tests and nits

* quality

* slow generation test

* ffn_dim to allow customization

* update readme

* add to toctreee

* start fixing integration tests

* update tests and code

* fix feature extractor

* fix config tests common

* update code to fix tests

* fix feature exctractor

* nit feature extraction

* update test for new feature extractor

* style

* add absrtact

* large logits wioth custom decoder input ids

* wraap around is otrch available

* fix feature extractor

* correct logits for whisper small.en

* nit

* fix encoder_attentino_mask

* some fixes

* remove unnecessary inputs

* nits

* add normalizer file

* update etst tokenization

* fix attention mask not defined

* fix generate

* remove uncoder attention mask useless

* update test modeling whisper

* update condfig to add second non supress tokens

* nits on feature exrtactor

* nit for test tokenizers

* update etsts

* update tests

* update tokenization test

* fixup

* invalidated hf token. Clean convert openai to whisper

* fix logit tests

* fixup

* Add model to README

* Fix doc tests

* clean merge

* revert toc_tree changes

* remove useless LogitProcessor

* Update whisper .mdx

* update config file doc

* update configuration docstring

* update test tokenization

* update test tokenization

* update tokenization whisper
Added copied from where needed

* update feature extraction

* nit test name

* style

* quality

* remove get suppress tokens and update non_speech tokens global variables

* Update src/transformers/models/whisper/feature_extraction_whisper.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* clean modeling whisper and test
Removed the attention mask arguments that are deprecated

* fix large test

* Add multilingual audio test, and translate test

* style

* fix larg multilingual test

* nits

* add copied from for attention layer

* remove attention masks in doc

* add english normalizer

* Update docs/source/en/model_doc/whisper.mdx
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* update tokenization test

* remove copied from in whisper attention : no bias in k_proj only

* wrap around dependencies in english normalizer

* style

* correct import generation logits

* for now, wrap feature extractor with torch

* remove torch depencies for feature extraction and style

* Update src/transformers/models/whisper/convert_openai_whisper_to_tfms.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/whisper/configuration_whisper.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update docs/source/en/model_doc/whisper.mdx
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* fixup

* nit

* update logitds

* style

* nit

* nits and fix final tests

* add `is_more_itertools_available` to utils

* quality

* add begin supress tokens, supress tokens to generate args and config

* clean supressTokensLogitProcessor in generation logits

* Nit naming

* add supressTokensAtBegin

* udpate tests, supress tokens to None or correct values

* nit and style

* update RAG to fit test and generate_logit

* add copy pasted statment on english normalizer

* add arguments to config_common_kwargs

* Update src/transformers/generation_utils.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/generation_logits_process.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* revert changes based on reviews

* update doc and nits

* Update src/transformers/models/whisper/configuration_whisper.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* more nits

* last nits

* update test configuration common

* add BART name in decoder attention mask documentation

* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* style

* nit

* nit

* add english.json file to git

* nits on documentation

* nit

* nits

* last styling

* add main toctree file

* remove sentence piece dependency

* clean init file

* fix tokenizer that has no dependencies on sentencepiece

* update whisper init file, nit

* remove english.json file

* add get decoder prompt id

* All weights loading

* Remove hanging pdb

* Fixup and tidy up

* Use same copied from as PT model

* Remove whitespace changes

* Remove torch references

* Tie embeddings

* Remove logits processor input to generate

* Update logit values

* revert changes and add forced logit processor

* nit

* clean normalizer

* remove protected

* Add logit processors and update generation code & tests

* Some tidy up

* Update docstring

* update

* update based on review

* Update src/transformers/models/whisper/configuration_whisper.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/whisper/configuration_whisper.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update to reflect changes on the PT model branch

* Tidy up

* Remove extra whitespace

* Fix test - make input ids small enough we can append

* Include upstream changes on main

* PR comments - add batch tests, remove comments & defaults

* Fix model output imports

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/generation_tf_logits_process.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update tests/models/whisper/test_modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update docstring example

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Remove changes to adjust_logits_during_generation function

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Tidy up imports that don't require TF

* Update tests - skip and no more skip

* Update tests/generation/test_generation_tf_logits_process.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/whisper/modeling_tf_whisper.py

* Update src/transformers/models/whisper/modeling_tf_whisper.py
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Add training flags

* Add (skipped) XLA generation tests

* Add embedding correctness test

* Add constant ids for generation tests

* Make logits finding a bit tidier

* Remove unused args

* xla generation enabled

* Don't skip XLA tests anymore

* Fix tests - add position ids to expected signature and update rag generation

* Undo method reorder

* Remove added whitespace

* Remove copy-paste gradient checkopint ref

* Remove

* Trigger CI - (issue with refs when pulling)
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: NielsRogge <niels.rogge1@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Joao Gante <joao@huggingface.co>

README.md

@@ -55,13 +55,13 @@ limitations under the License.
     <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
 </h3>
 
-🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. 
+🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
 
 These models can be applied on:
 
-* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. 
-* 🖼️ Images, for tasks like image classification, object detection, and segmentation. 
-* 🗣️ Audio, for tasks like speech recognition and audio classification. 
+* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
+* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
+* 🗣️ Audio, for tasks like speech recognition and audio classification.
 
 Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
 

docs/source/en/index.mdx

@@ -330,7 +330,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 |     Wav2Vec2-Conformer      |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            WavLM            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           Whisper           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Whisper           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           X-CLIP            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XGLM             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |

docs/source/en/model_doc/whisper.mdx

@@ -23,11 +23,11 @@ The abstract from the paper is the following:
 
 Tips:
 
-- The model usually performs well without requiring any finetuning. 
+- The model usually performs well without requiring any finetuning.
 - The architecture follows a classic encoder-decoder architecture, which means that it relies on the [`~generation_utils.GenerationMixin.generate`] function for inference.
 - One can use [`WhisperProcessor`] to prepare audio for the model, and decode the predicted ID's back into text.
 
-This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
+This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ). The Tensorflow version of this model was contributed by [amyeroberts](https://huggingface.co/amyeroberts).
 The original code can be found [here](https://github.com/openai/whisper).
 
 
@@ -66,3 +66,14 @@ The original code can be found [here](https://github.com/openai/whisper).
 
 [[autodoc]] WhisperForConditionalGeneration
     - forward
+
+
+## TFWhisperModel
+
+[[autodoc]] TFWhisperModel
+    - call
+
+## TFWhisperForConditionalGeneration
+
+[[autodoc]] TFWhisperForConditionalGeneration
+    - call

src/transformers/__init__.py

@@ -2754,6 +2754,14 @@ else:
             "TFWav2Vec2PreTrainedModel",
         ]
     )
+    _import_structure["models.whisper"].extend(
+        [
+            "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFWhisperForConditionalGeneration",
+            "TFWhisperModel",
+            "TFWhisperPreTrainedModel",
+        ]
+    )
     _import_structure["models.xglm"].extend(
         [
             "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -5303,6 +5311,12 @@ if TYPE_CHECKING:
             TFWav2Vec2Model,
             TFWav2Vec2PreTrainedModel,
         )
+        from .models.whisper import (
+            TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFWhisperForConditionalGeneration,
+            TFWhisperModel,
+            TFWhisperPreTrainedModel,
+        )
         from .models.xglm import (
             TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFXGLMForCausalLM,

src/transformers/generation_tf_logits_process.py

@@ -504,3 +504,84 @@ class TFForcedEOSTokenLogitsProcessor(TFLogitsProcessor):
                     axis=-1,
                 )
         return scores
+
+
+class TFSuppressTokensAtBeginLogitsProcessor(TFLogitsProcessor):
+    r"""
+    [`TFSuppressTokensAtBeginLogitsProcessor`] suppresses a list of tokens as soon as the `generate` function starts
+    generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` at not
+    sampled at the begining of the generation.
+    """
+
+    def __init__(self, begin_suppress_tokens, begin_index):
+        self.begin_suppress_tokens = list(begin_suppress_tokens)
+        self.begin_index = begin_index
+
+    def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
+        scores = tf.cond(
+            tf.equal(cur_len, self.begin_index),
+            lambda: tf.tensor_scatter_nd_update(
+                scores,
+                indices=[[i, token] for i in range(scores.shape[0]) for token in self.begin_suppress_tokens],
+                updates=[-float("inf") for _ in range(scores.shape[0] * len(self.begin_suppress_tokens))],
+            ),
+            lambda: scores,
+        )
+        return scores
+
+
+class TFSuppressTokensLogitsProcessor(TFLogitsProcessor):
+    r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
+    are not sampled."""
+
+    def __init__(self, suppress_tokens):
+        self.suppress_tokens = list(suppress_tokens)
+
+    def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
+        scores = tf.tensor_scatter_nd_update(
+            scores,
+            indices=[[i, token] for i in range(scores.shape[0]) for token in self.suppress_tokens],
+            updates=[-float("inf") for _ in range(scores.shape[0] * len(self.suppress_tokens))],
+        )
+        return scores
+
+
+class TFForceTokensLogitsProcessor(TFLogitsProcessor):
+    r"""This processor can be used to force a list of tokens. The processor will set their log probs to `0` and all
+    other tokens to `-inf` so that they are sampled at their corresponding index."""
+
+    def __init__(self, force_token_map):
+        force_token_map = dict(force_token_map)
+        # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
+        # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
+        # Indexes without forced tokens will have an negative value.
+        force_token_array = np.ones((max(force_token_map.keys()) + 1), dtype=np.int32) * -1
+        for index, token in force_token_map.items():
+            force_token_array[index] = token
+        self.force_token_array = tf.convert_to_tensor(force_token_array, dtype=tf.int32)
+
+    def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
+        def _force_token(generation_idx):
+            batch_size = scores.shape[0]
+            current_token = self.force_token_array[generation_idx]
+
+            new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float("inf")
+            indices = tf.stack((tf.range(batch_size), tf.tile([current_token], [batch_size])), axis=1)
+            updates = tf.zeros((batch_size,), dtype=scores.dtype)
+            new_scores = tf.tensor_scatter_nd_update(new_scores, indices, updates)
+            return new_scores
+
+        scores = tf.cond(
+            tf.greater_equal(cur_len, tf.shape(self.force_token_array)[0]),
+            # If the current length is geq than the length of force_token_array, the processor does nothing.
+            lambda: tf.identity(scores),
+            # Otherwise, it may force a certain token.
+            lambda: tf.cond(
+                tf.greater_equal(self.force_token_array[cur_len], 0),
+                # Only valid (positive) tokens are forced
+                lambda: _force_token(cur_len),
+                # Otherwise, the processor does nothing.
+                lambda: scores,
+            ),
+        )
+        return scores

src/transformers/generation_tf_utils.py

@@ -26,11 +26,14 @@ from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
 from .generation_tf_logits_process import (
     TFForcedBOSTokenLogitsProcessor,
     TFForcedEOSTokenLogitsProcessor,
+    TFForceTokensLogitsProcessor,
     TFLogitsProcessorList,
     TFMinLengthLogitsProcessor,
     TFNoBadWordsLogitsProcessor,
     TFNoRepeatNGramLogitsProcessor,
     TFRepetitionPenaltyLogitsProcessor,
+    TFSuppressTokensAtBeginLogitsProcessor,
+    TFSuppressTokensLogitsProcessor,
     TFTemperatureLogitsWarper,
     TFTopKLogitsWarper,
     TFTopPLogitsWarper,
@@ -401,6 +404,9 @@ class TFGenerationMixin:
         return_dict_in_generate=None,
         forced_bos_token_id=None,
         forced_eos_token_id=None,
+        suppress_tokens: Optional[List[int]] = None,
+        begin_suppress_tokens: Optional[List[int]] = None,
+        forced_decoder_ids: Optional[List[int]] = None,
         **model_kwargs,
     ) -> Union[TFGreedySearchOutput, TFSampleOutput, TFBeamSearchOutput, TFBeamSampleOutput, tf.Tensor]:
         r"""
@@ -494,6 +500,14 @@ class TFGenerationMixin:
                 the target language token.
             forced_eos_token_id (`int`, *optional*):
                 The id of the token to force as the last generated token when `max_length` is reached.
+            suppress_tokens  (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
+                A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
+                their log probs to `-inf` so that they are not sampled.
+            begin_suppress_tokens  (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
+                A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
+                logit processor will set their log probs to `-inf` so that they are not sampled.
+            forced_decoder_ids (`List[int]`, *optional*, defaults to `model.config.forced_decoder_ids`):
+                A list of tokens that will be forced as beginning tokens, before sampling.
             model_specific_kwargs:
                 Additional model specific kwargs will be forwarded to the `forward` function of the model.
 
@@ -609,6 +623,9 @@ class TFGenerationMixin:
                 return_dict_in_generate=return_dict_in_generate,
                 forced_bos_token_id=forced_bos_token_id,
                 forced_eos_token_id=forced_eos_token_id,
+                suppress_tokens=suppress_tokens,
+                begin_suppress_tokens=begin_suppress_tokens,
+                forced_decoder_ids=forced_decoder_ids,
                 **model_kwargs,
             )
 
@@ -648,6 +665,12 @@ class TFGenerationMixin:
         forced_eos_token_id = (
             forced_eos_token_id if forced_eos_token_id is not None else self.config.forced_eos_token_id
         )
+        suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
+        begin_suppress_tokens = (
+            begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
+        )
+        if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
+            forced_decoder_ids = self.config.forced_decoder_ids
 
         output_scores = output_scores if output_scores is not None else self.config.output_scores
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1368,6 +1391,9 @@ class TFGenerationMixin:
         return_dict_in_generate=None,
         forced_bos_token_id=None,
         forced_eos_token_id=None,
+        suppress_tokens=None,
+        begin_suppress_tokens=None,
+        forced_decoder_ids=None,
         **model_kwargs,
     ) -> Union[TFGreedySearchOutput, TFSampleOutput, TFBeamSearchOutput, TFBeamSampleOutput, tf.Tensor]:
         r"""
@@ -1461,6 +1487,15 @@ class TFGenerationMixin:
                 the target language token.
             forced_eos_token_id (`int`, *optional*):
                 The id of the token to force as the last generated token when `max_length` is reached.
+            suppress_tokens  (`List[int]`, *optional*, defaults to `model.config.suppress_tokens`):
+                A list of tokens that will be supressed at generation. The `SupressTokens` logit processor will set
+                their log probs to `-inf` so that they are not sampled.
+            begin_suppress_tokens  (`List[int]`, *optional*, defaults to `model.config.begin_suppress_tokens`):
+                A list of tokens that will be supressed at the begining of the generation. The `SupressBeginTokens`
+                logit processor will set their log probs to `-inf` so that they are not sampled.
+            forced_decoder_ids (`List[int]`, *optional*, defaults to `model.config.forced_decoder_ids`):
+                A list of tokens that will be forced as beginning tokens.
+
             model_kwargs:
                 Additional model specific kwargs will be forwarded to the `call` function of the model.
 
@@ -1695,12 +1730,16 @@ class TFGenerationMixin:
         logits_processor = self._get_logits_processor(
             repetition_penalty=repetition_penalty,
             no_repeat_ngram_size=no_repeat_ngram_size,
+            input_ids_seq_length=input_ids_seq_length,
             bad_words_ids=bad_words_ids,
             min_length=min_length,
             max_length=max_length,
             eos_token_id=eos_token_id,
             forced_bos_token_id=forced_bos_token_id,
             forced_eos_token_id=forced_eos_token_id,
+            suppress_tokens=suppress_tokens,
+            begin_suppress_tokens=begin_suppress_tokens,
+            forced_decoder_ids=forced_decoder_ids,
         )
 
         # 9. go into different generation modes
@@ -1994,7 +2033,7 @@ class TFGenerationMixin:
         def _initialize_past(past, num_padding_values, batch_axis):
             """initialize past with zeros -- the structure depends on `batch_axis`"""
             if batch_axis == 0:
-                padding_values = tf.scatter_nd(indices=[[2, 1]], updates=[num_padding_values], shape=(4, 2))
+                padding_values = tf.constant([[0, 0], [0, 0], [0, num_padding_values], [0, 0]], dtype=tf.int32)
                 new_past = ()
                 for past_layer in past:
                     new_past_layer = list(past_layer)
@@ -2099,12 +2138,16 @@ class TFGenerationMixin:
         self,
         repetition_penalty: float,
         no_repeat_ngram_size: int,
+        input_ids_seq_length: int,
         bad_words_ids: List[List[int]],
         min_length: int,
         max_length: int,
         eos_token_id: int,
         forced_bos_token_id: int,
         forced_eos_token_id: int,
+        suppress_tokens: Optional[List[int]] = None,
+        begin_suppress_tokens: Optional[List[int]] = None,
+        forced_decoder_ids: Optional[List[int]] = None,
     ) -> TFLogitsProcessorList:
         """
         This class returns a [`TFLogitsProcessorList`] list object that contains all relevant [`TFLogitsProcessor`]
@@ -2118,6 +2161,12 @@ class TFGenerationMixin:
         )
         bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
         eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        suppress_tokens = suppress_tokens if suppress_tokens is not None else self.config.suppress_tokens
+        begin_suppress_tokens = (
+            begin_suppress_tokens if begin_suppress_tokens is not None else self.config.begin_suppress_tokens
+        )
+        if forced_decoder_ids is None and hasattr(self.config, "forced_decoder_ids"):
+            forced_decoder_ids = self.config.forced_decoder_ids
 
         # instantiate processors list
         if repetition_penalty is not None and repetition_penalty != 1.0:
@@ -2132,7 +2181,16 @@ class TFGenerationMixin:
             processors.append(TFForcedBOSTokenLogitsProcessor(forced_bos_token_id))
         if forced_eos_token_id is not None:
             processors.append(TFForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id))
-
+        if suppress_tokens is not None:
+            processors.append(TFSuppressTokensLogitsProcessor(suppress_tokens))
+        if begin_suppress_tokens is not None:
+            begin_index = input_ids_seq_length
+            begin_index = begin_index if (input_ids_seq_length > 1 or forced_bos_token_id is None) else begin_index + 1
+            if forced_decoder_ids is not None:
+                begin_index += forced_decoder_ids[-1][0]  # generation starts after the last token that is forced
+            processors.append(TFSuppressTokensAtBeginLogitsProcessor(begin_suppress_tokens, begin_index))
+        if forced_decoder_ids is not None:
+            processors.append(TFForceTokensLogitsProcessor(forced_decoder_ids))
         return processors
 
     def greedy_search(

src/transformers/models/auto/modeling_tf_auto.py

@@ -80,6 +80,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("vit", "TFViTModel"),
         ("vit_mae", "TFViTMAEModel"),
         ("wav2vec2", "TFWav2Vec2Model"),
+        ("whisper", "TFWhisperModel"),
         ("xglm", "TFXGLMModel"),
         ("xlm", "TFXLMModel"),
         ("xlm-roberta", "TFXLMRobertaModel"),
@@ -145,6 +146,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict(
         ("t5", "TFT5ForConditionalGeneration"),
         ("tapas", "TFTapasForMaskedLM"),
         ("transfo-xl", "TFTransfoXLLMHeadModel"),
+        ("whisper", "TFWhisperForConditionalGeneration"),
         ("xlm", "TFXLMWithLMHeadModel"),
         ("xlm-roberta", "TFXLMRobertaForMaskedLM"),
         ("xlnet", "TFXLNetLMHeadModel"),
@@ -253,6 +255,7 @@ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict(
     [
         ("speech_to_text", "TFSpeech2TextForConditionalGeneration"),
+        ("whisper", "TFWhisperForConditionalGeneration"),
     ]
 )
 

src/transformers/models/rag/modeling_tf_rag.py

@@ -1262,6 +1262,7 @@ class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss
                 eos_token_id=eos_token_id,
                 forced_bos_token_id=None,
                 forced_eos_token_id=None,
+                input_ids_seq_length=tf.shape(decoder_input_ids)[-1],
             )
             model_kwargs["attention_mask"] = context_attention_mask
 

src/transformers/models/whisper/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -41,6 +41,18 @@ else:
         "WhisperPreTrainedModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_whisper"] = [
+        "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFWhisperForConditionalGeneration",
+        "TFWhisperModel",
+        "TFWhisperPreTrainedModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig
@@ -61,6 +73,19 @@ if TYPE_CHECKING:
             WhisperPreTrainedModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_whisper import (
+            TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFWhisperForConditionalGeneration,
+            TFWhisperModel,
+            TFWhisperPreTrainedModel,
+        )
+
 else:
     import sys
 

src/transformers/models/whisper/feature_extraction_whisper.py

@@ -218,7 +218,6 @@ class WhisperFeatureExtractor(SequenceFeatureExtractor):
         return_attention_mask: Optional[bool] = None,
         padding: Optional[str] = "max_length",
         max_length: Optional[int] = None,
-        sampling_rate: Optional[int] = None,
         **kwargs
     ) -> BatchFeature:
         """
@@ -262,19 +261,6 @@ class WhisperFeatureExtractor(SequenceFeatureExtractor):
                 The value that is used to fill the padding values / vectors.
         """
 
-        if sampling_rate is not None:
-            if sampling_rate != self.sampling_rate:
-                raise ValueError(
-                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
-                    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
-                    f" {self.sampling_rate} and not {sampling_rate}."
-                )
-        else:
-            logger.warning(
-                "It is strongly recommended to pass the `sampling_rate` argument to this function. "
-                "Failing to do so can result in silent errors that might be hard to debug."
-            )
-
         is_batched = bool(
             isinstance(raw_speech, (list, tuple))
             and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))

src/transformers/utils/dummy_tf_objects.py

@@ -2394,6 +2394,30 @@ class TFWav2Vec2PreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFWhisperForConditionalGeneration(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFWhisperModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFWhisperPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/generation/test_generation_tf_logits_process.py

@@ -29,11 +29,14 @@ if is_tf_available():
     from transformers.generation_tf_logits_process import (
         TFForcedBOSTokenLogitsProcessor,
         TFForcedEOSTokenLogitsProcessor,
+        TFForceTokensLogitsProcessor,
         TFLogitsProcessorList,
         TFMinLengthLogitsProcessor,
         TFNoBadWordsLogitsProcessor,
         TFNoRepeatNGramLogitsProcessor,
         TFRepetitionPenaltyLogitsProcessor,
+        TFSuppressTokensAtBeginLogitsProcessor,
+        TFSuppressTokensLogitsProcessor,
         TFTemperatureLogitsWarper,
         TFTopKLogitsWarper,
         TFTopPLogitsWarper,
@@ -331,6 +334,86 @@ class TFLogitsProcessorTest(unittest.TestCase):
         scores = logits_processor(input_ids, scores, cur_len)
         self.assertFalse(tf.math.reduce_any(tf.math.is_inf((scores))))
 
+    @parameterized.expand([(False,), (True,)])
+    def test_suppress_tokens_at_begin_logits_processor(self, use_xla):
+        vocab_size = 20
+        batch_size = 4
+
+        begin_suppress_tokens = [1, 2, 3]
+        begin_index = 5
+
+        logits_processor = TFSuppressTokensAtBeginLogitsProcessor(
+            begin_suppress_tokens=begin_suppress_tokens, begin_index=begin_index
+        )
+        if use_xla:
+            logits_processor = tf.function(logits_processor, jit_compile=True)
+
+        # Check that no scores are suppressed if begin_index is not reached
+        cur_len = 4
+        input_ids = tf.convert_to_tensor([[11, 17, 15, 8], [14, 0, 19, 5], [13, 11, 18, 19], [11, 12, 16, 15]])
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+        scores = logits_processor(input_ids, scores, cur_len)
+        self.assertFalse(tf.math.reduce_any(tf.math.is_inf((scores))))
+
+        # Check that scores are suppressed if begin_index is reached
+        cur_len = 5
+        input_ids = tf.convert_to_tensor([[5, 5, 5, 0, 17], [18, 1, 9, 14, 17], [18, 6, 8, 15, 19], [8, 12, 17, 1, 2]])
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+        scores = logits_processor(input_ids, scores, cur_len)
+        self.assertTrue(tf.math.reduce_all(tf.math.is_inf(tf.gather(scores, begin_suppress_tokens, axis=1))))
+
+    @parameterized.expand([(False,), (True,)])
+    def test_suppress_tokens_logits_processor(self, use_xla):
+        vocab_size = 20
+        batch_size = 4
+
+        suppress_tokens = [1, 3, 5]
+        keep_tokens = [i for i in range(vocab_size) if i not in suppress_tokens]
+
+        logits_processor = TFSuppressTokensLogitsProcessor(suppress_tokens=suppress_tokens)
+        if use_xla:
+            logits_processor = tf.function(logits_processor, jit_compile=True)
+
+        # Check that suppress_tokens are suppressed and others are not
+        cur_len = 5
+        input_ids = tf.convert_to_tensor([[0, 10, 19, 6, 3], [17, 4, 8, 17, 2], [7, 1, 11, 6, 15], [5, 8, 13, 16, 0]])
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+        scores = logits_processor(input_ids, scores, cur_len)
+        self.assertTrue(tf.math.reduce_all(tf.math.is_inf(tf.gather(scores, suppress_tokens, axis=1))))
+        self.assertFalse(tf.math.reduce_any(tf.math.is_inf(tf.gather(scores, keep_tokens, axis=1))))
+
+    @parameterized.expand([(False,), (True,)])
+    def test_force_tokens_logits_processor(self, use_xla):
+        vocab_size = 20
+        batch_size = 4
+
+        force_token_map = {1: 2, 3: 2}
+
+        logits_processor = TFForceTokensLogitsProcessor(force_token_map=force_token_map)
+        if use_xla:
+            logits_processor = tf.function(logits_processor, jit_compile=True)
+
+        # check that if the cur_len is contained in the force_token_map, the logits are the same
+        # for all tokens except the one the force_token_map points to
+        cur_len = 1
+        input_ids = tf.convert_to_tensor([[11], [7], [5], [15]])
+        ids_tensor((batch_size, cur_len), vocab_size=20)
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+        scores = logits_processor(input_ids, scores, cur_len)
+        tf.debugging.assert_near(tf.gather(scores, [force_token_map[cur_len]], axis=1), 0.0)
+
+        non_forced_inds = [i for i in range(vocab_size) if i != force_token_map[cur_len]]
+        self.assertTrue(
+            tf.math.reduce_all(tf.math.is_inf(tf.gather(scores, [non_forced_inds], axis=1))),
+        )
+
+        # check that if the cur_len is not contained in the force_token_map, the logits are not modified
+        cur_len = 2
+        input_ids = tf.convert_to_tensor([[2, 19], [19, 15], [4, 9], [7, 6]])
+        scores = self._get_uniform_logits(batch_size, vocab_size)
+        scores = logits_processor(input_ids, scores, cur_len)
+        self.assertFalse(tf.math.reduce_any(tf.math.is_inf((scores))))
+
     @parameterized.expand([(False,), (True,)])
     def test_processor_list(self, use_xla):
         # TODO (Joao): reintroduce TFNoRepeatNGramLogitsProcessor when it gets compatible with XLA

tests/test_modeling_tf_common.py

@@ -736,6 +736,23 @@ class TFModelTesterMixin:
                         dtype="float32",
                     ),
                 }
+            elif model_class.__name__ in ["TFWhisperModel", "TFWhisperForConditionalGeneration"]:
+                inputs = {
+                    "decoder_input_ids": tf.keras.Input(
+                        batch_shape=(2, max_input),
+                        name="decoder_input_ids",
+                        dtype="int32",
+                    ),
+                    "input_features": tf.keras.Input(
+                        batch_shape=(
+                            2,
+                            self.model_tester.num_mel_bins,
+                            self.model_tester.seq_length,
+                        ),
+                        name="input_features",
+                        dtype="float32",
+                    ),
+                }
             elif self.is_encoder_decoder:
                 inputs = {
                     "decoder_input_ids": tf.keras.Input(
@@ -1223,8 +1240,17 @@ class TFModelTesterMixin:
 
             # fetch the output for an input exclusively made of new members of the vocabulary
             inputs_dict = copy.deepcopy(original_inputs_dict)
-            new_vocab_input_ids = ids_tensor(inputs_dict["input_ids"].shape, new_tokens_size)
+            ids_feat_name = None
+            if "input_ids" in inputs_dict:
+                ids_feat_name = "input_ids"
+            elif "decoder_input_ids" in inputs_dict:
+                ids_feat_name = "decoder_input_ids"
+            else:
+                assert False, "No input ids feature found in the inputs dict"
+
+            new_vocab_input_ids = ids_tensor(inputs_dict[ids_feat_name].shape, new_tokens_size)
             new_vocab_input_ids += old_total_size
+            inputs_dict[ids_feat_name] = new_vocab_input_ids
             if "input_ids" in inputs_dict:
                 inputs_dict["input_ids"] = new_vocab_input_ids
             if "decoder_input_ids" in inputs_dict:

utils/check_repo.py

@@ -105,6 +105,8 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
     "TFRobertaForMultipleChoice",  # TODO: fix
     "TrOCRDecoderWrapper",  # Building part of bigger (tested) model.
+    "TFWhisperEncoder",  # Building part of bigger (tested) model.
+    "TFWhisperDecoder",  # Building part of bigger (tested) model.
     "SeparableConv1D",  # Building part of bigger (tested) model.
     "FlaxBartForCausalLM",  # Building part of bigger (tested) model.
     "FlaxBertForCausalLM",  # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM.

utils/documentation_tests.txt

@@ -97,4 +97,5 @@ src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
 src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py
 src/transformers/models/wavlm/modeling_wavlm.py
 src/transformers/models/whisper/modeling_whisper.py
+src/transformers/models/whisper/modeling_tf_whisper.py
 src/transformers/models/yolos/modeling_yolos.py