Wav2Vec2

Overview

The Wav2Vec2 model was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

The abstract from the paper is the following:

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.

Tips:

  • Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.

  • Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using Wav2Vec2CTCTokenizer.

This model was contributed by patrickvonplaten.

Wav2Vec2Config

class transformers.Wav2Vec2Config(vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_dropout=0.1, feat_quantizer_dropout=0.0, final_dropout=0.1, layerdrop=0.1, initializer_range=0.02, layer_norm_eps=1e-05, feat_extract_norm='group', feat_extract_activation='gelu', conv_dim=512, 512, 512, 512, 512, 512, 512, conv_stride=5, 2, 2, 2, 2, 2, 2, conv_kernel=10, 3, 3, 3, 3, 2, 2, conv_bias=False, num_conv_pos_embeddings=128, num_conv_pos_embedding_groups=16, do_stable_layer_norm=False, apply_spec_augment=True, mask_time_prob=0.05, mask_time_length=10, mask_feature_prob=0.0, mask_feature_length=10, num_codevectors_per_group=320, num_codevector_groups=2, contrastive_logits_temperature=0.1, num_negatives=100, codevector_dim=256, proj_codevector_dim=256, diversity_loss_weight=0.1, ctc_loss_reduction='sum', ctc_zero_infinity=False, gradient_checkpointing=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, **kwargs)[source]

This is the configuration class to store the configuration of a Wav2Vec2Model. It is used to instantiate an Wav2Vec2 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 Wav2Vec2 facebook/wav2vec2-base-960h architecture.

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

Parameters

  • vocab_size (int, optional, defaults to 32) – Vocabulary size of the Wav2Vec2 model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling Wav2Vec2Model or TFWav2Vec2Model. Vocabulary size of the model. Defines the different tokens that can be represented by the inputs_ids passed to the forward method of Wav2Vec2Model.

  • hidden_size (int, optional, defaults to 768) – Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) – Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 12) – Number of attention heads for each attention layer in the Transformer encoder.

  • intermediate_size (int, optional, defaults to 3072) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • hidden_act (str or function, optional, defaults to "gelu") – The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "selu" and "gelu_new" are supported.

  • hidden_dropout_prob (float, optional, defaults to 0.1) – The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_probs_dropout_prob (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.

  • initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • layer_norm_eps (float, optional, defaults to 1e-12) – The epsilon used by the layer normalization layers.

  • feat_extract_norm (str, optional, defaults to "group") – The norm to be applied to 1D convolutional layers in feature extractor. One of "group" for group normalization of only the first 1D convolutional layer or "layer" for layer normalization of all 1D convolutional layers.

  • feat_extract_dropout (float, optional, defaults to 0.0) – The dropout probabilitiy for all 1D convolutional layers in feature extractor.

  • feat_extract_activation (str, `optional, defaults to "gelu") – The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string, "gelu", "relu", "selu" and "gelu_new" are supported.

  • (obj (feat_quantizer_dropout) – float, optional, defaults to 0.0): The dropout probabilitiy for quantized feature extractor states.

  • conv_dim (Tuple[int], optional, defaults to (512, 512, 512, 512, 512, 512, 512)) – A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the feature extractor. The length of conv_dim defines the number of 1D convolutional layers.

  • conv_stride (Tuple[int], optional, defaults to (5, 2, 2, 2, 2, 2, 2)) – A tuple of integers defining the stride of each 1D convolutional layer in the feature extractor. The length of conv_stride defines the number of convolutional layers and has to match the the length of conv_dim.

  • conv_kernel (Tuple[int], optional, defaults to (10, 3, 3, 3, 3, 3, 3)) – A tuple of integers defining the kernel size of each 1D convolutional layer in the feature extractor. The length of conv_kernel defines the number of convolutional layers and has to match the the length of conv_dim.

  • conv_bias (bool, optional, defaults to False) – Whether the 1D convolutional layers have a bias.

  • num_conv_pos_embeddings (int, optional, defaults to 128) – Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional embeddings layer.

  • num_conv_pos_embedding_groups (int, optional, defaults to 16) – Number of groups of 1D convolutional positional embeddings layer.

  • do_stable_layer_norm (bool, optional, defaults to False) – Whether do apply stable layer norm architecture of the Transformer encoder. do_stable_layer_norm is True corresponds to applying layer norm before the attention layer, whereas do_stable_layer_norm is False corresponds to applying layer norm after the attention layer.

  • apply_spec_augment (bool, optional, defaults to True) – Whether to apply SpecAugment data augmentation to the outputs of the feature extractor. For reference see SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

  • mask_time_prob (float, optional, defaults to 0.05) – Propability of each feature vector along the time axis to be chosen as the start of the vector span to be masked. Approximately mask_time_prob * sequence_length // mask_time_length feature vectors will be masked along the time axis. This is only relevant if apply_spec_augment is True.

  • mask_time_length (int, optional, defaults to 10) – Length of vector span along the time axis.

  • mask_feature_prob (float, optional, defaults to 0.0) – Propability of each feature vector along the feature axis to be chosen as the start of the vector span to be masked. Approximately mask_time_prob * hidden_size // mask_time_length feature vectors will be masked along the time axis. This is only relevant if apply_spec_augment is True.

  • mask_feature_length (int, optional, defaults to 10) – Length of vector span along the feature axis.

  • num_codevectors_per_group (int, optional, defaults to 320) – Number of entries in each quantization codebook (group).

  • num_codevector_groups (int, optional, defaults to 2) – Number of codevector groups for product codevector quantization.

  • contrastive_logits_temperature (float, optional, defaults to 0.1) – The temperature kappa in the contrastive loss.

  • feat_quantizer_dropout (float, optional, defaults to 0.0) – The dropout probabilitiy for the output of the feature extractor that’s used by the quantizer.

  • num_negatives (int, optional, defaults to 100) – Number of negative samples for the contrastive loss.

  • codevector_dim (int, optional, defaults to 256) – Dimensionality of the quantized feature vectors.

  • proj_codevector_dim (int, optional, defaults to 256) – Dimensionality of the final projection of both the quantized and the transformer features.

  • diversity_loss_weight (int, optional, defaults to 0.1) – The weight of the codebook diversity loss component.

  • ctc_loss_reduction (str, optional, defaults to "sum") – Specifies the reduction to apply to the output of torch.nn.CTCLoss. Only relevant when training an instance of Wav2Vec2ForCTC.

  • ctc_zero_infinity (bool, optional, defaults to False) – Whether to zero infinite losses and the associated gradients of torch.nn.CTCLoss. Infinite losses mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance of Wav2Vec2ForCTC.

  • gradient_checkpointing (bool, optional, defaults to False) – If True, use gradient checkpointing to save memory at the expense of slower backward pass.

Example:

>>> from transformers import Wav2Vec2Model, Wav2Vec2Config

>>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration
>>> configuration = Wav2Vec2Config()

>>> # Initializing a model from the facebook/wav2vec2-base-960h style configuration
>>> model = Wav2Vec2Model(configuration)

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

Wav2Vec2CTCTokenizer

class transformers.Wav2Vec2CTCTokenizer(vocab_file, bos_token='<s>', eos_token='</s>', unk_token='<unk>', pad_token='<pad>', word_delimiter_token='|', do_lower_case=False, **kwargs)[source]

Constructs a Wav2Vec2CTC 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.

Parameters

  • vocab_file (str) – File containing the vocabulary.

  • 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.

  • word_delimiter_token (str, optional, defaults to "|") – The token used for defining the end of a word.

  • do_lower_case (bool, optional, defaults to False) – Whether or not to accept lowercase input and lowercase the output when decoding.

  • **kwargs – Additional keyword arguments passed along to PreTrainedTokenizer

__call__(text: Union[str, List[str], List[List[str]]], text_pair: Optional[Union[str, List[str], List[List[str]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = False, truncation: Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False, max_length: Optional[int] = None, stride: int = 0, is_split_into_words: bool = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, transformers.file_utils.TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs) → transformers.tokenization_utils_base.BatchEncoding

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences.

Parameters

  • text (str, List[str], List[List[str]]) – The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).

  • text_pair (str, List[str], List[List[str]]) – The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).

  • add_special_tokens (bool, optional, defaults to True) – Whether or not to encode the sequences with the special tokens relative to their model.

  • padding (bool, str or PaddingStrategy, optional, defaults to False) –

    Activates and controls padding. Accepts the following values:

    • 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).

  • truncation (bool, str or TruncationStrategy, optional, defaults to False) –

    Activates and controls truncation. Accepts the following values:

    • True or 'longest_first': Truncate 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. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate 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. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate 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. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • max_length (int, optional) –

    Controls the maximum length to use by one of the truncation/padding parameters.

    If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • stride (int, optional, defaults to 0) – If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.

  • is_split_into_words (bool, optional, defaults to False) – Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.

  • 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).

  • 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.

  • return_token_type_ids (bool, optional) –

    Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

    What are token type IDs?

  • 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 tokenizer’s default, defined by the return_outputs attribute.

    What are attention masks?

  • return_overflowing_tokens (bool, optional, defaults to False) – Whether or not to return overflowing token sequences.

  • return_special_tokens_mask (bool, optional, defaults to False) – Whether or not to return special tokens mask information.

  • return_offsets_mapping (bool, optional, defaults to False) –

    Whether or not to return (char_start, char_end) for each token.

    This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.

  • return_length (bool, optional, defaults to False) – Whether or not to return the lengths of the encoded inputs.

  • verbose (bool, optional, defaults to True) – Whether or not to print more information and warnings.

  • **kwargs – passed to the self.tokenize() method

Returns

A BatchEncoding with the following fields:

  • input_ids – List of token ids to be fed to a model.

    What are input IDs?

  • token_type_ids – List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

    What are token type IDs?

  • attention_mask – List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

    What are attention masks?

  • overflowing_tokens – List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).

  • num_truncated_tokens – Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).

  • special_tokens_mask – List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).

  • length – The length of the inputs (when return_length=True)

Return type

BatchEncoding

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str][source]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters

  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

Wav2Vec2FeatureExtractor

class transformers.Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, return_attention_mask=False, do_normalize=True, **kwargs)[source]

Constructs a Wav2Vec2 feature extractor.

This feature extractor inherits from SequenceFeatureExtractor which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Parameters

  • feature_size (int, defaults to 1) – 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 per second (Hz).

  • padding_value (float, defaults to 0.0) – The value that is used to fill the padding values.

  • do_normalize (bool, optional, defaults to False) – Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, e.g., wav2vec2-lv60.

  • return_attention_mask (bool, optional, defaults to False) –

    Whether or not __call__() should return attention_mask.

    Note

    Wav2Vec2 models that have set config.feat_extract_norm == "group", such as wav2vec2-base, have not been trained using attention_mask. For such models, input_values should simply be padded with 0 and no attention_mask should be passed.

    For Wav2Vec2 models that have set config.feat_extract_norm == "layer", such as wav2vec2-lv60, attention_mask should be passed for batched inference.

__call__(raw_speech: Union[numpy.ndarray, List[float], List[numpy.ndarray], List[List[float]]], padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, transformers.file_utils.TensorType]] = None, sampling_rate: Optional[int] = None, **kwargs) → transformers.feature_extraction_utils.BatchFeature[source]

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

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.

  • padding (bool, str or PaddingStrategy, optional, defaults to False) –

    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).

  • 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?

    Note

    Wav2Vec2 models that have set config.feat_extract_norm == "group", such as wav2vec2-base, have not been trained using attention_mask. For such models, input_values should simply be padded with 0 and no attention_mask should be passed.

    For Wav2Vec2 models that have set config.feat_extract_norm == "layer", such as wav2vec2-lv60, attention_mask should be passed for batched inference.

  • 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) –

Wav2Vec2Processor

class transformers.Wav2Vec2Processor(feature_extractor, tokenizer)[source]

Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single processor.

Wav2Vec2Processor offers all the functionalities of Wav2Vec2FeatureExtractor and Wav2Vec2CTCTokenizer. See the docstring of __call__() and decode() for more information.

Parameters
__call__(*args, **kwargs)[source]

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor’s __call__() and returns its output. If used in the context as_target_processor() this method forwards all its arguments to Wav2Vec2CTCTokenizer’s __call__(). Please refer to the docstring of the above two methods for more information.

as_target_processor()[source]

Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning Wav2Vec2.

batch_decode(*args, **kwargs)[source]

This method forwards all its arguments to Wav2Vec2CTCTokenizer’s batch_decode(). Please refer to the docstring of this method for more information.

decode(*args, **kwargs)[source]

This method forwards all its arguments to Wav2Vec2CTCTokenizer’s decode(). Please refer to the docstring of this method for more information.

classmethod from_pretrained(pretrained_model_name_or_path, **kwargs)[source]

Instantiate a Wav2Vec2Processor from a pretrained Wav2Vec2 processor.

Note

This class method is simply calling Wav2Vec2FeatureExtractor’s from_pretrained() and Wav2Vec2CTCTokenizer’s from_pretrained(). Please refer to the docstrings of the methods above for more information.

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 huggingface.co. 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 SequenceFeatureExtractor and PreTrainedTokenizer

pad(*args, **kwargs)[source]

When used in normal mode, this method forwards all its arguments to Wav2Vec2FeatureExtractor’s pad() and returns its output. If used in the context as_target_processor() this method forwards all its arguments to Wav2Vec2CTCTokenizer’s pad(). Please refer to the docstring of the above two methods for more information.

save_pretrained(save_directory)[source]

Save a Wav2Vec2 feature_extractor object and Wav2Vec2 tokenizer object to the directory save_directory, so that it can be re-loaded using the from_pretrained() class method.

Note

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

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).

Wav2Vec2Model

class transformers.Wav2Vec2Model(config: transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config)[source]

The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top. Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

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 etc.).

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (Wav2Vec2Config) – 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.

forward(input_values, attention_mask=None, mask_time_indices=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The Wav2Vec2Model forward method, overrides the __call__() special method.

Note

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.

Parameters

  • input_values (torch.FloatTensor of shape (batch_size, sequence_length)) – Float values of input raw speech waveform. Values 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_values, the Wav2Vec2Processor should be used for padding and conversion into a tensor of type torch.FloatTensor. See transformers.Wav2Vec2Processor.__call__() for details.

  • attention_mask (torch.LongTensor 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?

    Warning

    attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, such as wav2vec2-base, attention_mask should not be passed to avoid degraded performance when doing batched inference. For such models input_values should simply be padded with 0 and passed without attention_mask. Be aware that these models also yield slightly different results depending on whether input_values is padded or not.

  • 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

A BaseModelOutput 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 (Wav2Vec2Config) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

  • 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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • 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 after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")

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

>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state

Return type

BaseModelOutput or tuple(torch.FloatTensor)

Wav2Vec2ForCTC

class transformers.Wav2Vec2ForCTC(config)[source]

Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC). Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

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 etc.).

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (Wav2Vec2Config) – 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.

forward(input_values, attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None)[source]

The Wav2Vec2ForCTC forward method, overrides the __call__() special method.

Note

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.

Parameters

  • input_values (torch.FloatTensor of shape (batch_size, sequence_length)) – Float values of input raw speech waveform. Values 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_values, the Wav2Vec2Processor should be used for padding and conversion into a tensor of type torch.FloatTensor. See transformers.Wav2Vec2Processor.__call__() for details.

  • attention_mask (torch.LongTensor 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?

    Warning

    attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, such as wav2vec2-base, attention_mask should not be passed to avoid degraded performance when doing batched inference. For such models input_values should simply be padded with 0 and passed without attention_mask. Be aware that these models also yield slightly different results depending on whether input_values is padded or not.

  • 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, target_length), optional) – Labels for connectionist temporal classification. Note that target_length has to be smaller or equal to the sequence length of the output logits. Indices are selected in [-100, 0, ..., config.vocab_size - 1]. All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size - 1].

Returns

A BaseModelOutput 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 (Wav2Vec2Config) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

  • 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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • 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 after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> import torch
>>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

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

>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)

>>> transcription = processor.decode(predicted_ids[0])

>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"

>>> # wrap processor as target processor to encode labels
>>> with processor.as_target_processor():
>>>     labels = processor(target_transcription, return_tensors="pt").input_ids

>>> loss = model(input_values, labels=labels).loss

Return type

BaseModelOutput or tuple(torch.FloatTensor)

Wav2Vec2ForPreTraining

class transformers.Wav2Vec2ForPreTraining(config: transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config)[source]

Wav2Vec2 Model with a quantizer and VQ head on top. Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

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 etc.).

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (Wav2Vec2Config) – 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.

forward(input_values, attention_mask=None, mask_time_indices=None, output_attentions=None, output_hidden_states=None, return_dict=None)[source]

The Wav2Vec2ForPreTraining forward method, overrides the __call__() special method.

Note

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.

Parameters

  • input_values (torch.FloatTensor of shape (batch_size, sequence_length)) – Float values of input raw speech waveform. Values 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_values, the Wav2Vec2Processor should be used for padding and conversion into a tensor of type torch.FloatTensor. See transformers.Wav2Vec2Processor.__call__() for details.

  • attention_mask (torch.LongTensor 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?

    Warning

    attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. For all models whose processor has config.return_attention_mask == False, such as wav2vec2-base, attention_mask should not be passed to avoid degraded performance when doing batched inference. For such models input_values should simply be padded with 0 and passed without attention_mask. Be aware that these models also yield slightly different results depending on whether input_values is padded or not.

  • 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.

  • mask_time_indices (torch.BoolTensor of shape (batch_size, sequence_length), optional) – Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in config.proj_codevector_dim space.

Returns

A BaseModelOutput 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 (Wav2Vec2Config) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

  • 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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the initial embedding outputs.

  • 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 after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> import torch
>>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("patrickvonplaten/wav2vec2-base")
>>> model = Wav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")


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


>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = feature_extractor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1

>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
>>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2, device=model.device)

>>> with torch.no_grad():
...     outputs = model(input_values, mask_time_indices=mask_time_indices)

>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = torch.cosine_similarity(
...     outputs.projected_states, outputs.projected_quantized_states, dim=-1
... )

>>> # show that cosine similarity is much higher than random
>>> assert cosine_sim[mask_time_indices].mean() > 0.5

>>> # for contrastive loss training model should be put into train mode
>>> model.train()
>>> loss = model(input_values, mask_time_indices=mask_time_indices).loss

Return type

BaseModelOutput or tuple(torch.FloatTensor)

TFWav2Vec2Model

class transformers.TFWav2Vec2Model(*args, **kwargs)[source]

The bare TFWav2Vec2 Model transformer outputing 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.

Note

TF 2.0 models accepts two formats as inputs:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

  • a single Tensor with input_values only and nothing else: model(inputs_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_values, attention_mask]) or model([input_values, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_values": input_values, "token_type_ids": token_type_ids})

Parameters

config (Wav2Vec2Config) – 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.

call(input_values: tensorflow.python.framework.ops.Tensor, attention_mask: Optional[tensorflow.python.framework.ops.Tensor] = None, token_type_ids: Optional[tensorflow.python.framework.ops.Tensor] = None, position_ids: Optional[tensorflow.python.framework.ops.Tensor] = None, head_mask: Optional[tensorflow.python.framework.ops.Tensor] = None, inputs_embeds: Optional[tensorflow.python.framework.ops.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, **kwargs: Any) → Union[transformers.modeling_tf_outputs.TFBaseModelOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]

The TFWav2Vec2Model forward method, overrides the __call__() special method.

Note

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.

Parameters

  • input_values (np.ndarray, tf.Tensor, List[tf.Tensor] Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape ({0})) –

    Indices of input sequence tokens in the vocabulary.

    Indices can be obtained using BertTokenizer. See transformers.PreTrainedTokenizer.__call__() and transformers.PreTrainedTokenizer.encode() for details.

    What are input IDs?

  • attention_mask (np.ndarray or 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?

  • token_type_ids (np.ndarray or tf.Tensor of shape ({0}), optional) –

    Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    What are token type IDs?

  • position_ids (np.ndarray or tf.Tensor of shape ({0}), optional) –

    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

    What are position IDs?

  • head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (np.ndarray or tf.Tensor of shape ({0}, hidden_size), optional) – Optionally, instead of passing input_values you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_values indices into associated vectors than the model’s internal embedding lookup matrix.

  • 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

A TFBaseModelOutput 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 (Wav2Vec2Config) 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 model.

  • hidden_states (tuple(tf.FloatTensor), 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 model at the output of each layer plus the initial embedding outputs.

  • 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 after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> from transformers import Wav2Vec2Processor, TFWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")

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

>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state

Return type

TFBaseModelOutput or tuple(tf.Tensor)

TFWav2Vec2ForCTC

class transformers.TFWav2Vec2ForCTC(*args, **kwargs)[source]

TFWav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC).

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.

Note

TF 2.0 models accepts two formats as inputs:

  • having all inputs as keyword arguments (like PyTorch models), or

  • having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

  • a single Tensor with input_values only and nothing else: model(inputs_ids)

  • a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_values, attention_mask]) or model([input_values, attention_mask, token_type_ids])

  • a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_values": input_values, "token_type_ids": token_type_ids})

Parameters

config (Wav2Vec2Config) – 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.

call(input_values: tensorflow.python.framework.ops.Tensor, attention_mask: Optional[tensorflow.python.framework.ops.Tensor] = None, token_type_ids: Optional[tensorflow.python.framework.ops.Tensor] = None, position_ids: Optional[tensorflow.python.framework.ops.Tensor] = None, head_mask: Optional[tensorflow.python.framework.ops.Tensor] = None, inputs_embeds: Optional[tensorflow.python.framework.ops.Tensor] = None, output_attentions: Optional[bool] = None, labels: Optional[tensorflow.python.framework.ops.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, **kwargs: Any) → Union[transformers.modeling_tf_outputs.TFCausalLMOutput, Tuple[tensorflow.python.framework.ops.Tensor]][source]

The TFWav2Vec2ForCTC forward method, overrides the __call__() special method.

Note

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.

Parameters

  • input_values (np.ndarray, tf.Tensor, List[tf.Tensor] Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape ({0})) –

    Indices of input sequence tokens in the vocabulary.

    Indices can be obtained using BertTokenizer. See transformers.PreTrainedTokenizer.__call__() and transformers.PreTrainedTokenizer.encode() for details.

    What are input IDs?

  • attention_mask (np.ndarray or 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?

  • token_type_ids (np.ndarray or tf.Tensor of shape ({0}), optional) –

    Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:

    • 0 corresponds to a sentence A token,

    • 1 corresponds to a sentence B token.

    What are token type IDs?

  • position_ids (np.ndarray or tf.Tensor of shape ({0}), optional) –

    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].

    What are position IDs?

  • head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • inputs_embeds (np.ndarray or tf.Tensor of shape ({0}, hidden_size), optional) – Optionally, instead of passing input_values you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_values indices into associated vectors than the model’s internal embedding lookup matrix.

  • 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 or np.ndarray of shape (batch_size, sequence_length), optional) – Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_values 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

A TFCausalLMOutput 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 (Wav2Vec2Config) 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 (for next-token prediction).

  • 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).

  • 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 model at the output of each layer plus the initial embedding outputs.

  • 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 after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

>>> import tensorflow as tf
>>> from transformers import Wav2Vec2Processor, TFWav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf

>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

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

>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)

>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1
>>> logits = model(input_values).logits >>> predicted_ids = tf.argmax(logits, axis=-1)

>>> transcription = processor.decode(predicted_ids[0])

>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"

>>> # wrap processor as target processor to encode labels
>>> with processor.as_target_processor():
>>>     labels = processor(transcription, return_tensors="tf").input_values

>>> loss = model(input_values, labels=labels).loss

Return type

TFCausalLMOutput or tuple(tf.Tensor)