# Speech2Text

## Speech2Text

### Overview

The Speech2Text model was proposed in [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It’s a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST: [LibriSpeech](http://www.openslr.org/12), [CoVoST 2](https://github.com/facebookresearch/covost), [MuST-C](https://ict.fbk.eu/must-c/).

This model was contributed by [valhalla](https://huggingface.co/valhalla). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text).

### Inference

Speech2Text is a speech model that accepts a float tensor of log-mel filter-bank features extracted from the speech signal. It’s a transformer-based seq2seq model, so the transcripts/translations are generated autoregressively. The `generate()` method can be used for inference.

The [Speech2TextFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor) class is responsible for extracting the log-mel filter-bank features. The [Speech2TextProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextProcessor) wraps [Speech2TextFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor) and [Speech2TextTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer) into a single instance to both extract the input features and decode the predicted token ids.

The feature extractor depends on `torchaudio` and the tokenizer depends on `sentencepiece` so be sure to install those packages before running the examples. You could either install those as extra speech dependencies with `pip install transformers"[speech, sentencepiece]"` or install the packages separately with `pip install torchaudio sentencepiece`. Also `torchaudio` requires the development version of the [libsndfile](http://www.mega-nerd.com/libsndfile/) package which can be installed via a system package manager. On Ubuntu it can be installed as follows: `apt install libsndfile1-dev`

* ASR and Speech Translation

Copied

```
>>> import torch
>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
>>> from datasets import load_dataset

>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")

>>> inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt")
>>> generated_ids = model.generate(inputs["input_features"], attention_mask=inputs["attention_mask"])

>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> transcription
['mister quilter is the apostle of the middle classes and we are glad to welcome his gospel']
```

*   Multilingual speech translation

    For multilingual speech translation models, `eos_token_id` is used as the `decoder_start_token_id` and the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate()` method. The following example shows how to transate English speech to French text using the _facebook/s2t-medium-mustc-multilingual-st_ checkpoint.

Copied

```
>>> import torch
>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
>>> from datasets import load_dataset

>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")

>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")

>>> inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt")
>>> generated_ids = model.generate(
...     inputs["input_features"],
...     attention_mask=inputs["attention_mask"],
...     forced_bos_token_id=processor.tokenizer.lang_code_to_id["fr"],
... )

>>> translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> translation
["(Vidéo) Si M. Kilder est l'apossible des classes moyennes, et nous sommes heureux d'être accueillis dans son évangile."]
```

See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for Speech2Text checkpoints.

### Speech2TextConfig

#### class transformers.Speech2TextConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/configuration_speech_to_text.py#L31)

( vocab\_size = 10000encoder\_layers = 12encoder\_ffn\_dim = 2048encoder\_attention\_heads = 4decoder\_layers = 6decoder\_ffn\_dim = 2048decoder\_attention\_heads = 4encoder\_layerdrop = 0.0decoder\_layerdrop = 0.0use\_cache = Trueis\_encoder\_decoder = Trueactivation\_function = 'relu'd\_model = 256dropout = 0.1attention\_dropout = 0.0activation\_dropout = 0.0init\_std = 0.02decoder\_start\_token\_id = 2scale\_embedding = Truepad\_token\_id = 1bos\_token\_id = 0eos\_token\_id = 2max\_source\_positions = 6000max\_target\_positions = 1024num\_conv\_layers = 2conv\_kernel\_sizes = (5, 5)conv\_channels = 1024input\_feat\_per\_channel = 80input\_channels = 1\*\*kwargs )

Parameters

* **vocab\_size** (`int`, _optional_, defaults to 50265) — Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [Speech2TextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextModel)
* **d\_model** (`int`, _optional_, defaults to 1024) — Dimensionality of the layers and the pooler layer.
* **encoder\_layers** (`int`, _optional_, defaults to 12) — Number of encoder layers.
* **decoder\_layers** (`int`, _optional_, defaults to 12) — Number of decoder layers.
* **encoder\_attention\_heads** (`int`, _optional_, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
* **decoder\_attention\_heads** (`int`, _optional_, defaults to 16) — Number of attention heads for each attention layer in the Transformer decoder.
* **decoder\_ffn\_dim** (`int`, _optional_, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
* **encoder\_ffn\_dim** (`int`, _optional_, defaults to 4096) — Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.
* **activation\_function** (`str` or `function`, _optional_, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
* **dropout** (`float`, _optional_, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
* **attention\_dropout** (`float`, _optional_, defaults to 0.0) — The dropout ratio for the attention probabilities.
* **activation\_dropout** (`float`, _optional_, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer.
* **init\_std** (`float`, _optional_, defaults to 0.02) — The standard deviation of the truncated\_normal\_initializer for initializing all weight matrices.
* **encoder\_layerdrop** (`float`, _optional_, defaults to 0.0) — The LayerDrop probability for the encoder. See the \[LayerDrop paper]\(see [https://arxiv.org/abs/1909.11556](https://arxiv.org/abs/1909.11556)) for more details.
* **decoder\_layerdrop** (`float`, _optional_, defaults to 0.0) — The LayerDrop probability for the decoder. See the \[LayerDrop paper]\(see [https://arxiv.org/abs/1909.11556](https://arxiv.org/abs/1909.11556)) for more details.
* **use\_cache** (`bool`, _optional_, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models).
* **max\_source\_positions** (`int`, _optional_, defaults to 6000) — The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
* **max\_target\_positions** (`int`, _optional_, defaults to 1024) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
* **num\_conv\_layers** (`int`, _optional_, defaults to 2) — Number of 1D convolutional layers in the conv module.
* **conv\_kernel\_sizes** (`Tuple[int]`, _optional_, defaults to `(5, 5)`) — A tuple of integers defining the kernel size of each 1D convolutional layer in the conv module. The length of `conv_kernel_sizes` has to match `num_conv_layers`.
* **conv\_channels** (`int`, _optional_, defaults to 1024) — An integer defining the number of output channels of each convolution layers except the final one in the conv module.
* **input\_feat\_per\_channel** (`int`, _optional_, defaults to 80) — An integer specifying the size of feature vector. This is also the dimensions of log-mel filter-bank features.
* **input\_channels** (`int`, _optional_, defaults to 1) — An integer specifying number of input channels of the input feature vector.

This is the configuration class to store the configuration of a [Speech2TextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextModel). It is used to instantiate an Speech2Text model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Speech2Text [facebook/s2t-small-librispeech-asr](https://huggingface.co/facebook/s2t-small-librispeech-asr) architecture.

Configuration objects inherit from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) for more information.



Example:

Copied

```
>>> from transformers import Speech2TextConfig, Speech2TextModel

>>> # Initializing a Speech2Text s2t_transformer_s style configuration
>>> configuration = Speech2TextConfig()

>>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration
>>> model = Speech2TextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

### Speech2TextTokenizer

#### class transformers.Speech2TextTokenizer

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/tokenization_speech_to_text.py#L59)

( vocab\_filespm\_filebos\_token = '\<s>'eos\_token = '\</s>'pad\_token = '\<pad>'unk\_token = '\<unk>'do\_upper\_case = Falsedo\_lower\_case = Falsetgt\_lang = Nonelang\_codes = Noneadditional\_special\_tokens = Nonesp\_model\_kwargs: typing.Union\[typing.Dict\[str, typing.Any], NoneType] = None\*\*kwargs )

Parameters

* **vocab\_file** (`str`) — File containing the vocabulary.
* **spm\_file** (`str`) — Path to the [SentencePiece](https://github.com/google/sentencepiece) model file
* **bos\_token** (`str`, _optional_, defaults to `"<s>"`) — The beginning of sentence token.
* **eos\_token** (`str`, _optional_, defaults to `"</s>"`) — The end of sentence token.
* **unk\_token** (`str`, _optional_, defaults to `"<unk>"`) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
* **pad\_token** (`str`, _optional_, defaults to `"<pad>"`) — The token used for padding, for example when batching sequences of different lengths.
* **do\_upper\_case** (`bool`, _optional_, defaults to `False`) — Whether or not to uppercase the output when decoding.
* **do\_lower\_case** (`bool`, _optional_, defaults to `False`) — Whether or not to lowercase the input when tokenizing.
* **tgt\_lang** (`str`, _optional_) — A string representing the target language.
*   **sp\_model\_kwargs** (`dict`, _optional_) — Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set:

    * `enable_sampling`: Enable subword regularization.
    * `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
      * `nbest_size = {0,1}`: No sampling is performed.
      * `nbest_size > 1`: samples from the nbest\_size results.
      * `nbest_size < 0`: assuming that nbest\_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.
    * `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

    \*\*kwargs — Additional keyword arguments passed along to [PreTrainedTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizer)

Construct an Speech2Text tokenizer.

This tokenizer inherits from [PreTrainedTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains some of the main methods. Users should refer to the superclass for more information regarding such methods.

**build\_inputs\_with\_special\_tokens**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/tokenization_speech_to_text.py#L217)

( token\_ids\_0token\_ids\_1 = None )

Build model inputs from a sequence by appending eos\_token\_id.

**get\_special\_tokens\_mask**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/tokenization_speech_to_text.py#L224)

( token\_ids\_0: typing.List\[int]token\_ids\_1: typing.Optional\[typing.List\[int]] = Nonealready\_has\_special\_tokens: bool = False ) → `List[int]`

Parameters

* **token\_ids\_0** (`List[int]`) — List of IDs.
* **token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.
* **already\_has\_special\_tokens** (`bool`, _optional_, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

Returns

`List[int]`

A list of integers in the range \[0, 1]: 1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method.

**create\_token\_type\_ids\_from\_sequences**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/tokenization_utils_base.py#L3289)

( token\_ids\_0: typing.List\[int]token\_ids\_1: typing.Optional\[typing.List\[int]] = None ) → `List[int]`

Parameters

* **token\_ids\_0** (`List[int]`) — The first tokenized sequence.
* **token\_ids\_1** (`List[int]`, _optional_) — The second tokenized sequence.

Returns

`List[int]`

The token type ids.

Create the token type IDs corresponding to the sequences passed. [What are token type IDs?](https://huggingface.co/docs/transformers/glossary#token-type-ids)

Should be overridden in a subclass if the model has a special way of building those.

**save\_vocabulary**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/tokenization_speech_to_text.py#L268)

( save\_directory: strfilename\_prefix: typing.Optional\[str] = None )

### Speech2TextFeatureExtractor

#### class transformers.Speech2TextFeatureExtractor

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py#L33)

( feature\_size = 80sampling\_rate = 16000num\_mel\_bins = 80padding\_value = 0.0do\_ceptral\_normalize = Truenormalize\_means = Truenormalize\_vars = True\*\*kwargs )

Parameters

* **feature\_size** (`int`, defaults to 80) — The feature dimension of the extracted features.
* **sampling\_rate** (`int`, defaults to 16000) — The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
* **num\_mel\_bins** (`int`, defaults to 80) — Number of Mel-frequency bins.
* **padding\_value** (`float`, defaults to 0.0) — The value that is used to fill the padding vectors.
* **do\_ceptral\_normalize** (`bool`, _optional_, defaults to `True`) — Whether or not to apply utterance-level cepstral mean and variance normalization to extracted features.
* **normalize\_means** (`bool`, _optional_, defaults to `True`) — Whether or not to zero-mean normalize the extracted features.
* **normalize\_vars** (`bool`, _optional_, defaults to `True`) — Whether or not to unit-variance normalize the extracted features.

Constructs a Speech2Text feature extractor.

This feature extractor inherits from [Speech2TextFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

This class extracts mel-filter bank features from raw speech using TorchAudio and applies utterance-level cepstral mean and variance normalization to the extracted features.

**\_\_call\_\_**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py#L126)

( raw\_speech: typing.Union\[numpy.ndarray, typing.List\[float], typing.List\[numpy.ndarray], typing.List\[typing.List\[float]]]padding: typing.Union\[bool, str, transformers.utils.generic.PaddingStrategy] = Falsemax\_length: typing.Optional\[int] = Nonetruncation: bool = Falsepad\_to\_multiple\_of: typing.Optional\[int] = Nonereturn\_tensors: typing.Union\[str, transformers.utils.generic.TensorType, NoneType] = Nonesampling\_rate: typing.Optional\[int] = Nonereturn\_attention\_mask: typing.Optional\[bool] = None\*\*kwargs )

Parameters

* **raw\_speech** (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`) — The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep.
* **padding** (`bool`, `str` or [PaddingStrategy](https://huggingface.co/docs/transformers/v4.34.1/en/internal/file_utils#transformers.utils.PaddingStrategy), _optional_, defaults to `True`) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
  * `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  * `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided.
  * `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths).
* **max\_length** (`int`, _optional_) — Maximum length of the returned list and optionally padding length (see above).
* **truncation** (`bool`) — Activates truncation to cut input sequences longer than _max\_length_ to _max\_length_.
*   **pad\_to\_multiple\_of** (`int`, _optional_) — If set will pad the sequence to a multiple of the provided value.

    This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
*   **return\_attention\_mask** (`bool`, _optional_) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific feature\_extractor’s default.

    [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)

    For Speech2TextTransformer models, `attention_mask` should always be passed for batched inference, to avoid subtle bugs.
* **return\_tensors** (`str` or [TensorType](https://huggingface.co/docs/transformers/v4.34.1/en/internal/file_utils#transformers.TensorType), _optional_) — If set, will return tensors instead of list of python integers. Acceptable values are:
  * `'tf'`: Return TensorFlow `tf.constant` objects.
  * `'pt'`: Return PyTorch `torch.Tensor` objects.
  * `'np'`: Return Numpy `np.ndarray` objects.
* **sampling\_rate** (`int`, _optional_) — The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors.
* **padding\_value** (`float`, defaults to 0.0) — The value that is used to fill the padding values / vectors.

Main method to featurize and prepare for the model one or several sequence(s).

### Speech2TextProcessor

#### class transformers.Speech2TextProcessor

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/processing_speech_to_text.py#L24)

( feature\_extractortokenizer )

Parameters

* **feature\_extractor** (`Speech2TextFeatureExtractor`) — An instance of [Speech2TextFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor). The feature extractor is a required input.
* **tokenizer** (`Speech2TextTokenizer`) — An instance of [Speech2TextTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer). The tokenizer is a required input.

Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a single processor.

[Speech2TextProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextProcessor) offers all the functionalities of [Speech2TextFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor) and [Speech2TextTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer). See the [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextProcessor.__call__) and [decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextProcessor.decode) for more information.

**\_\_call\_\_**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/processing_speech_to_text.py#L47)

( \*args\*\*kwargs )

When used in normal mode, this method forwards all its arguments to Speech2TextFeatureExtractor’s [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor.__call__) and returns its output. If used in the context `as_target_processor()` this method forwards all its arguments to Speech2TextTokenizer’s [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__). Please refer to the doctsring of the above two methods for more information.

**from\_pretrained**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/processing_utils.py#L167)

( pretrained\_model\_name\_or\_path: typing.Union\[str, os.PathLike]cache\_dir: typing.Union\[str, os.PathLike, NoneType] = Noneforce\_download: bool = Falselocal\_files\_only: bool = Falsetoken: typing.Union\[bool, str, NoneType] = Nonerevision: str = 'main'\*\*kwargs )

Parameters

* **pretrained\_model\_name\_or\_path** (`str` or `os.PathLike`) — This can be either:
  * a string, the _model id_ of a pretrained feature\_extractor hosted inside a model repo on boincai.com. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
  * a path to a _directory_ containing a feature extractor file saved using the [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) method, e.g., `./my_model_directory/`.
  * a path or url to a saved feature extractor JSON _file_, e.g., `./my_model_directory/preprocessor_config.json`. \*\*kwargs — Additional keyword arguments passed along to both [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained) and `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`.

Instantiate a processor associated with a pretrained model.

This class method is simply calling the feature extractor [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained), image processor [ImageProcessingMixin](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/image_processor#transformers.ImageProcessingMixin) and the tokenizer `~tokenization_utils_base.PreTrainedTokenizer.from_pretrained` methods. Please refer to the docstrings of the methods above for more information.

**save\_pretrained**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/processing_utils.py#L93)

( save\_directorypush\_to\_hub: bool = False\*\*kwargs )

Parameters

* **save\_directory** (`str` or `os.PathLike`) — Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist).
* **push\_to\_hub** (`bool`, _optional_, defaults to `False`) — Whether or not to push your model to the BOINC AI model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).
* **kwargs** (`Dict[str, Any]`, _optional_) — Additional key word arguments passed along to the [push\_to\_hub()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/processors#transformers.ProcessorMixin.push_to_hub) method.

Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/nougat#transformers.NougatProcessor.from_pretrained) method.

This class method is simply calling [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained) and [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained). Please refer to the docstrings of the methods above for more information.

**batch\_decode**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/processing_speech_to_text.py#L86)

( \*args\*\*kwargs )

This method forwards all its arguments to Speech2TextTokenizer’s [batch\_decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.batch_decode). Please refer to the docstring of this method for more information.

**decode**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/processing_speech_to_text.py#L93)

( \*args\*\*kwargs )

This method forwards all its arguments to Speech2TextTokenizer’s [decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.decode). Please refer to the docstring of this method for more information.

### Speech2TextModel

#### class transformers.Speech2TextModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_speech_to_text.py#L1136)

( config: Speech2TextConfig )

Parameters

* **config** ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Speech2Text Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_speech_to_text.py#L1158)

( input\_features: typing.Optional\[torch.LongTensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor] = Nonedecoder\_attention\_mask: typing.Optional\[torch.LongTensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor] = Noneencoder\_outputs: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor]]] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor]]] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor] = Noneuse\_cache: typing.Optional\[bool] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None ) → [transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

Parameters

* **input\_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`) — Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, _e.g._ via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [AutoFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoFeatureExtractor) should be used for extracting the fbank features, padding and conversion into a tensor of type `torch.FloatTensor`. See [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor.__call__)
*   **attention\_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, _optional_) — Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`:

    * 1 for tokens that are **not masked**,
    * 0 for tokens that are **masked**.

    [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
*   **decoder\_input\_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using `SpeechToTextTokenizer`. See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details.

    [What are decoder input IDs?](https://huggingface.co/docs/transformers/glossary#decoder-input-ids)

    SpeechToText uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
*   **decoder\_attention\_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.

    If you want to change padding behavior, you should read `modeling_speech_to_text._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
* **head\_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **decoder\_head\_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **cross\_attn\_head\_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **encoder\_outputs** (`tuple(tuple(torch.FloatTensor)`, _optional_) — Tuple consists of (`last_hidden_state`, _optional_: `hidden_states`, _optional_: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
*   **past\_key\_values** (`tuple(tuple(torch.FloatTensor))`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

    If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
* **decoder\_inputs\_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, _optional_) — Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
* **use\_cache** (`bool`, _optional_) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
* **output\_attentions** (`bool`, _optional_) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, _optional_) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, _optional_) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

Returns

[transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

A [transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) and inputs.

* **loss** (`torch.FloatTensor` of shape `(1,)`, _optional_, returned when `labels` is provided) — Language modeling loss.
* **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
*   **past\_key\_values** (`tuple(tuple(torch.FloatTensor))`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
*   **decoder\_hidden\_states** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
*   **decoder\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
*   **cross\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
* **encoder\_last\_hidden\_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
*   **encoder\_hidden\_states** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
*   **encoder\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The [Speech2TextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.



Example:

Copied

```
>>> import torch
>>> from transformers import Speech2TextModel, AutoFeatureExtractor
>>> from datasets import load_dataset

>>> model = Speech2TextModel.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(
...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
... )
>>> input_features = inputs.input_features
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 256]
```

### Speech2TextForConditionalGeneration

#### class transformers.Speech2TextForConditionalGeneration

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_speech_to_text.py#L1267)

( config: Speech2TextConfig )

Parameters

* **config** ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The Speech2Text Model with a language modeling head. Can be used for summarization. This model inherits from [PreTrainedModel](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_speech_to_text.py#L1291)

( input\_features: typing.Optional\[torch.LongTensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor] = Nonedecoder\_attention\_mask: typing.Optional\[torch.LongTensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor] = Noneencoder\_outputs: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor]]] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor]]] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor] = Nonelabels: typing.Optional\[torch.LongTensor] = Noneuse\_cache: typing.Optional\[bool] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None ) → [transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

Parameters

* **input\_features** (`torch.FloatTensor` of shape `(batch_size, sequence_length, feature_size)`) — Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, _e.g._ via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [AutoFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoFeatureExtractor) should be used for extracting the fbank features, padding and conversion into a tensor of type `torch.FloatTensor`. See [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor.__call__)
*   **attention\_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, _optional_) — Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`:

    * 1 for tokens that are **not masked**,
    * 0 for tokens that are **masked**.

    [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
*   **decoder\_input\_ids** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using `SpeechToTextTokenizer`. See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details.

    [What are decoder input IDs?](https://huggingface.co/docs/transformers/glossary#decoder-input-ids)

    SpeechToText uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
*   **decoder\_attention\_mask** (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.

    If you want to change padding behavior, you should read `modeling_speech_to_text._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
* **head\_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **decoder\_head\_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **cross\_attn\_head\_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **encoder\_outputs** (`tuple(tuple(torch.FloatTensor)`, _optional_) — Tuple consists of (`last_hidden_state`, _optional_: `hidden_states`, _optional_: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
*   **past\_key\_values** (`tuple(tuple(torch.FloatTensor))`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

    If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
* **decoder\_inputs\_embeds** (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, _optional_) — Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
* **use\_cache** (`bool`, _optional_) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
* **output\_attentions** (`bool`, _optional_) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, _optional_) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, _optional_) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.
* **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, _optional_) — Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

Returns

[transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

A [transformers.modeling\_outputs.Seq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) and inputs.

* **loss** (`torch.FloatTensor` of shape `(1,)`, _optional_, returned when `labels` is provided) — Language modeling loss.
* **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
*   **past\_key\_values** (`tuple(tuple(torch.FloatTensor))`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
*   **decoder\_hidden\_states** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
*   **decoder\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
*   **cross\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
* **encoder\_last\_hidden\_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
*   **encoder\_hidden\_states** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
*   **encoder\_attentions** (`tuple(torch.FloatTensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The [Speech2TextForConditionalGeneration](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.



Example:

Copied

```
>>> import torch
>>> from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
>>> from datasets import load_dataset

>>> model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

>>> inputs = processor(
...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
... )
>>> input_features = inputs.input_features

>>> generated_ids = model.generate(inputs=input_features)

>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
'mister quilter is the apostle of the middle classes and we are glad to welcome his gospel'
```

### TFSpeech2TextModel

#### class transformers.TFSpeech2TextModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py#L1205)

( \*args\*\*kwargs )

Parameters

* **config** ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

The bare Speech2Text Model outputting raw hidden-states without any specific head on top. This model inherits from [TFPreTrainedModel](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

* having all inputs as keyword arguments (like PyTorch models), or
* having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

* a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
* a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
* a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

**call**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py#L1217)

( input\_features: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonedecoder\_input\_ids: np.ndarray | tf.Tensor | None = Nonedecoder\_attention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Nonedecoder\_head\_mask: np.ndarray | tf.Tensor | None = Nonecross\_attn\_head\_mask: np.ndarray | tf.Tensor | None = Noneencoder\_outputs: np.ndarray | tf.Tensor | None = Nonepast\_key\_values: Optional\[Tuple\[Tuple\[Union\[np.ndarray, tf.Tensor]]]] = Nonedecoder\_inputs\_embeds: np.ndarray | tf.Tensor | None = Noneuse\_cache: Optional\[bool] = Noneoutput\_attentions: Optional\[bool] = Noneoutput\_hidden\_states: Optional\[bool] = Nonereturn\_dict: Optional\[bool] = Nonetraining: bool = False\*\*kwargs ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or `tuple(tf.Tensor)`

Parameters

* **input\_features** (`tf.Tensor` of shape `(batch_size, sequence_length, feature_size)`) — Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, _e.g._ via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [AutoFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoFeatureExtractor) should be used for extracting the fbank features, padding and conversion into a tensor of floats. See [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor.__call__)
*   **attention\_mask** (`tf.Tensor` of shape `({0})`, _optional_) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

    * 1 for tokens that are **not masked**,
    * 0 for tokens that are **masked**.

    [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
*   **decoder\_input\_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using [Speech2TextTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details.

    [What are decoder input IDs?](https://huggingface.co/docs/transformers/glossary#decoder-input-ids)

    SpeechToText uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

    For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper.
* **decoder\_attention\_mask** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, _optional_) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
* **head\_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **decoder\_head\_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **cross\_attn\_head\_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **encoder\_outputs** (`tf.FloatTensor`, _optional_) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
* **past\_key\_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) — contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
* **decoder\_inputs\_embeds** (`tf.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, _optional_) — Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
* **use\_cache** (`bool`, _optional_) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
* **output\_attentions** (`bool`, _optional_) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
* **output\_hidden\_states** (`bool`, _optional_) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
* **return\_dict** (`bool`, _optional_) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
* **training** (`bool`, _optional_, defaults to `False`) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

Returns

[transformers.modeling\_tf\_outputs.TFSeq2SeqModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or `tuple(tf.Tensor)`

A [transformers.modeling\_tf\_outputs.TFSeq2SeqModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) and inputs.

*   **last\_hidden\_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the decoder of the model.

    If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.
*   **past\_key\_values** (`List[tf.Tensor]`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

    Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.
*   **decoder\_hidden\_states** (`tuple(tf.Tensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
*   **decoder\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
*   **cross\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
* **encoder\_last\_hidden\_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
*   **encoder\_hidden\_states** (`tuple(tf.Tensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
*   **encoder\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The [TFSpeech2TextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.



Example:

Copied

```
>>> from transformers import AutoTokenizer, TFSpeech2TextModel
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/s2t-small-librispeech-asr")
>>> model = TFSpeech2TextModel.from_pretrained("facebook/s2t-small-librispeech-asr")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)

>>> last_hidden_states = outputs.last_hidden_state
```

### TFSpeech2TextForConditionalGeneration

#### class transformers.TFSpeech2TextForConditionalGeneration

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py#L1287)

( \*args\*\*kwargs )

Parameters

* **config** ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

The Speech2Text Model with a language modeling head. Can be used for summarization. This model inherits from [TFPreTrainedModel](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

* having all inputs as keyword arguments (like PyTorch models), or
* having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

* a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
* a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
* a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

**call**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py#L1311)

( input\_features: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonedecoder\_input\_ids: np.ndarray | tf.Tensor | None = Nonedecoder\_attention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Nonedecoder\_head\_mask: np.ndarray | tf.Tensor | None = Nonecross\_attn\_head\_mask: np.ndarray | tf.Tensor | None = Noneencoder\_outputs: np.ndarray | tf.Tensor | None = Nonepast\_key\_values: Optional\[Tuple\[Tuple\[Union\[np.ndarray, tf.Tensor]]]] = Nonedecoder\_inputs\_embeds: np.ndarray | tf.Tensor | None = Nonelabels: np.ndarray | tf.Tensor | None = Noneuse\_cache: Optional\[bool] = Noneoutput\_attentions: Optional\[bool] = Noneoutput\_hidden\_states: Optional\[bool] = Nonereturn\_dict: Optional\[bool] = Nonetraining: Optional\[bool] = False\*\*kwargs ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or `tuple(tf.Tensor)`

Parameters

* **input\_features** (`tf.Tensor` of shape `(batch_size, sequence_length, feature_size)`) — Float values of fbank features extracted from the raw speech waveform. Raw speech waveform can be obtained by loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, _e.g._ via the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the [AutoFeatureExtractor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoFeatureExtractor) should be used for extracting the fbank features, padding and conversion into a tensor of floats. See [**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor.__call__)
*   **attention\_mask** (`tf.Tensor` of shape `({0})`, _optional_) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

    * 1 for tokens that are **not masked**,
    * 0 for tokens that are **masked**.

    [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
*   **decoder\_input\_ids** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, _optional_) — Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using [Speech2TextTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details.

    [What are decoder input IDs?](https://huggingface.co/docs/transformers/glossary#decoder-input-ids)

    SpeechToText uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

    For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper.
* **decoder\_attention\_mask** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, _optional_) — will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
* **head\_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **decoder\_head\_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **cross\_attn\_head\_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, _optional_) — Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **encoder\_outputs** (`tf.FloatTensor`, _optional_) — hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
* **past\_key\_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) — contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
* **decoder\_inputs\_embeds** (`tf.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, _optional_) — Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
* **use\_cache** (`bool`, _optional_) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
* **output\_attentions** (`bool`, _optional_) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
* **output\_hidden\_states** (`bool`, _optional_) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
* **return\_dict** (`bool`, _optional_) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
* **training** (`bool`, _optional_, defaults to `False`) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
* **labels** (`tf.Tensor` of shape `(batch_size, sequence_length)`, _optional_) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

Returns

[transformers.modeling\_tf\_outputs.TFSeq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or `tuple(tf.Tensor)`

A [transformers.modeling\_tf\_outputs.TFSeq2SeqLMOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([Speech2TextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.Speech2TextConfig)) and inputs.

* **loss** (`tf.Tensor` of shape `(n,)`, _optional_, where n is the number of non-masked labels, returned when `labels` is provided) — Language modeling loss.
* **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
*   **past\_key\_values** (`List[tf.Tensor]`, _optional_, returned when `use_cache=True` is passed or when `config.use_cache=True`) — List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads, sequence_length, embed_size_per_head)`).

    Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see `past_key_values` input) to speed up sequential decoding.
*   **decoder\_hidden\_states** (`tuple(tf.Tensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
*   **decoder\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
*   **cross\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
* **encoder\_last\_hidden\_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, _optional_) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
*   **encoder\_hidden\_states** (`tuple(tf.Tensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
*   **encoder\_attentions** (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

    Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The [TFSpeech2TextForConditionalGeneration](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.



Example:

Copied

```
>>> import tensorflow as tf
>>> from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> model = TFSpeech2TextForConditionalGeneration.from_pretrained(
...     "facebook/s2t-small-librispeech-asr", from_pt=True
... )
>>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")


>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch


>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> ds.set_format(type="tf")

>>> input_features = processor(
...     ds["speech"][0], sampling_rate=16000, return_tensors="tf"
... ).input_features  # Batch size 1
>>> generated_ids = model.generate(input_features)

>>> transcription = processor.batch_decode(generated_ids)
```
