Speech2Text

Speech2Text

Overview

The Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq 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, CoVoST 2, MuST-C.

This model was contributed by valhalla. The original code can be found here.

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 class is responsible for extracting the log-mel filter-bank features. The Speech2TextProcessor wraps Speech2TextFeatureExtractor and 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 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 to look for Speech2Text checkpoints.

Speech2TextConfig

class transformers.Speech2TextConfig

<source>

( 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

  • 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) 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) 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. 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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from 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>

( 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 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 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

Construct an Speech2Text tokenizer.

This tokenizer inherits from 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>

( token_ids_0token_ids_1 = None )

Build model inputs from a sequence by appending eos_token_id.

get_special_tokens_mask

<source>

( 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>

( 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?

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

save_vocabulary

<source>

( save_directory: strfilename_prefix: typing.Optional[str] = None )

Speech2TextFeatureExtractor

class transformers.Speech2TextFeatureExtractor

<source>

( 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 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>

( 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, 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?

    For Speech2TextTransformer models, attention_mask should always be passed for batched inference, to avoid subtle bugs.

  • return_tensors (str or 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>

( feature_extractortokenizer )

Parameters

  • feature_extractor (Speech2TextFeatureExtractor) — An instance of Speech2TextFeatureExtractor. The feature extractor is a required input.

  • tokenizer (Speech2TextTokenizer) — An instance of 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 offers all the functionalities of Speech2TextFeatureExtractor and Speech2TextTokenizer. See the call() and decode() for more information.

__call__

<source>

( *args**kwargs )

When used in normal mode, this method forwards all its arguments to Speech2TextFeatureExtractor’s call() and returns its output. If used in the context as_target_processor() this method forwards all its arguments to Speech2TextTokenizer’s call(). Please refer to the doctsring of the above two methods for more information.

from_pretrained

<source>

( 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() 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() 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(), image processor 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>

( 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() method.

Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it can be reloaded using the from_pretrained() method.

This class method is simply calling save_pretrained() and save_pretrained(). Please refer to the docstrings of the methods above for more information.

batch_decode

<source>

( *args**kwargs )

This method forwards all its arguments to Speech2TextTokenizer’s batch_decode(). Please refer to the docstring of this method for more information.

decode

<source>

( *args**kwargs )

This method forwards all its arguments to Speech2TextTokenizer’s decode(). Please refer to the docstring of this method for more information.

Speech2TextModel

class transformers.Speech2TextModel

<source>

( config: Speech2TextConfig )

Parameters

  • config (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() 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. 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 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>

( 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 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 should be used for extracting the fbank features, padding and conversion into a tensor of type torch.FloatTensor. See 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?

  • 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() and PreTrainedTokenizer.call() for details.

    What are 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 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 instead of a plain tuple.

Returns

transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A 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) 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 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>

( config: Speech2TextConfig )

Parameters

  • config (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() method to load the model weights.

The Speech2Text Model with a language modeling head. Can be used for summarization. This model inherits from 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 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>

( 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 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 should be used for extracting the fbank features, padding and conversion into a tensor of type torch.FloatTensor. See 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?

  • 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() and PreTrainedTokenizer.call() for details.

    What are 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 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 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 or tuple(torch.FloatTensor)

A 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) 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 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>

( *args**kwargs )

Parameters

  • config (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() 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. 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 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 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>

( 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 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 should be used for extracting the fbank features, padding and conversion into a tensor of floats. See 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?

  • 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are 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 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 or tuple(tf.Tensor)

A 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) 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 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>

( *args**kwargs )

Parameters

  • config (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() method to load the model weights.

The Speech2Text Model with a language modeling head. Can be used for summarization. This model inherits from 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 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 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>

( 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 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 should be used for extracting the fbank features, padding and conversion into a tensor of floats. See 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?

  • 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are 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 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 or tuple(tf.Tensor)

A 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) 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 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)
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