Wav2Vec2Config

aults to hidden-size. Only relevant
if add_adapter is True.    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.  Example:   Copied >>> from transformers import Wav2Vec2Config, Wav2Vec2Model
>>> # Initializing a Wav2Vec2 facebook/wav2vec2-base-960h style configuration
>>> configuration = Wav2Vec2Config()
>>> # Initializing a model (with random weights) from the facebook/wav2vec2-base-960h style configuration
>>> model = Wav2Vec2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.c


class transformers.Wav2Vec2CTCTokenizer  < source > ( vocab_file bos_token = '<s>' eos_token = '</s>' unk_token = '<unk>' pad_token = '<pad>' word_delimiter_token = '|' replace_word_delimiter_char = ' ' do_lower_case = False target_lang = None **kwargs  )   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.   target_lang (str, optional) —
A target language the tokenizer should set by default. target_lang has to be defined for multi-lingual,
nested vocabulary such as facebook/mms-1b-all.
**kwargs —
Additional keyword arguments passed along to PreTrainedTokenizer    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.  __call__  < source > ( text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None text_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None text_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None text_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None add_special_tokens: bool = True padding: t


yping.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.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  ) → BatchEncoding  Parameters   text (str, List[str], List[List[str]], optional) —
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 s


et
is_split_into_words=True (to lift the ambiguity with a batch of sequences).   text_pair (str, List[str], List[List[str]], optional) —
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_target (str, List[str], List[List[str]], optional) —
The sequence or batch of sequences to be encoded as target texts. 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_target (str, List[str], List[List[str]], optional) —
The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
list of strings (pretokenized s


tring). 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 add special tokens when encoding the sequences. This will use the underlying
PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are
automatically added to the input ids. This is usefull if you want to add bos or eos tokens
automatically.   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 th


e 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. Requires padding to be activated.
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_typ


e_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. If a pair of sequences of input ids (or a batch
of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead
of returning overflowing tokens.   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) —
W


hether 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
BatchEncoding

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_i


nput_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)
  Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str] = None  )     decode  < source > ( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces:


 bool = None output_char_offsets: bool = False output_word_offsets: bool = False **kwargs  ) → str or Wav2Vec2CTCTokenizerOutput  Parameters   token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) —
List of tokenized input ids. Can be obtained using the __call__ method.   skip_special_tokens (bool, optional, defaults to False) —
Whether or not to remove special tokens in the decoding.   clean_up_tokenization_spaces (bool, optional) —
Whether or not to clean up the tokenization spaces.   output_char_offsets (bool, optional, defaults to False) —
Whether or not to output character offsets. Character offsets can be used in combination with the
sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
Please take a look at the example below to better understand how to make use of output_char_offsets.
   output_word_offsets (bool, optional, defaults to False) —
Whether or not to output word offsets. Word


 offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
Please take a look at the example below to better understand how to make use of output_word_offsets.
   kwargs (additional keyword arguments, optional) —
Will be passed to the underlying model specific decode method.   Returns
str or Wav2Vec2CTCTokenizerOutput

The list of decoded
sentences. Will be a Wav2Vec2CTCTokenizerOutput when
output_char_offsets == True or output_word_offsets == True.
  Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces. Similar to doing self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids)).  Example:   Copied >>> # Let's see how to retrieve time steps for a model
>>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
>>> from datasets import load_dataset
>>


> import datasets
>>> import torch
>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
>>> # load first sample of English common_voice
>>> dataset = load_dataset("common_voice", "en", split="train", streaming=True)
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> dataset_iter = iter(dataset)
>>> sample = next(dataset_iter)
>>> # forward sample through model to get greedily predicted transcription ids
>>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
>>> logits = model(input_values).logits[0]
>>> pred_ids = torch.argmax(logits, axis=-1)
>>> # retrieve word stamps (analogous commands for `output_char_offsets`)
>>> outputs = tokenizer.de


code(pred_ids, output_word_offsets=True)
>>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate
>>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate
>>> word_offsets = [
...     {
...         "word": d["word"],
...         "start_time": round(d["start_offset"] * time_offset, 2),
...         "end_time": round(d["end_offset"] * time_offset, 2),
...     }
...     for d in outputs.word_offsets
... ]
>>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer:
>>> # https://huggingface.co/datasets/common_voice/viewer/en/train
>>> word_offsets[:3]
[{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.64, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}]  batch_decode  < source > ( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('t


orch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = None output_char_offsets: bool = False output_word_offsets: bool = False **kwargs  ) → List[str] or Wav2Vec2CTCTokenizerOutput  Parameters   sequences (Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) —
List of tokenized input ids. Can be obtained using the __call__ method.   skip_special_tokens (bool, optional, defaults to False) —
Whether or not to remove special tokens in the decoding.   clean_up_tokenization_spaces (bool, optional) —
Whether or not to clean up the tokenization spaces.   output_char_offsets (bool, optional, defaults to False) —
Whether or not to output character offsets. Character offsets can be used in combination with the
sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.
Please take a look at the Example of decode() to better understand how to make
use o


f output_char_offsets. batch_decode() works the same way with batched
output.
   output_word_offsets (bool, optional, defaults to False) —
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.
Please take a look at the Example of decode() to better understand how to make
use of output_word_offsets. batch_decode() works the same way with batched
output.
   kwargs (additional keyword arguments, optional) —
Will be passed to the underlying model specific decode method.   Returns
List[str] or Wav2Vec2CTCTokenizerOutput

The list of decoded
sentences. Will be a Wav2Vec2CTCTokenizerOutput when
output_char_offsets == True or output_word_offsets == True.
  Convert a list of lists of token ids into a list of strings by calling decode.  set_target_lang  < source > ( target_lang: str  )    Set the target language of a nested multi-lingua


class transformers.Wav2Vec2FeatureExtractor  < source > ( feature_size = 1 sampling_rate = 16000 padding_value = 0.0 return_attention_mask = False do_normalize = True **kwargs  )   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 (Hz).   padding_value (float, defaults to 0.0) —
The value that is used to fill the padding values.   do_normalize (bool, optional, defaults to True) —
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.
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.
    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.  __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] = False max_length: typing.Optional[int] = None truncation: bool = False pad_to_multiple_of: typing.Optional[int] = None return_attention_mask: typing.Optional[boo


l] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None sampling_rate: typing.Optional[int] = 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 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).   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?
Wav2Vec2 models that have set config.feat_ex


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


Wav2Vec2Processor

class transformers.Wav2Vec2Processor  < source > ( feature_extractor tokenizer  )   Parameters   feature_extractor (Wav2Vec2FeatureExtractor) —
An instance of Wav2Vec2FeatureExtractor. The feature extractor is a required input.   tokenizer (PreTrainedTokenizer) —
An instance of PreTrainedTokenizer. The tokenizer is a required input.    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 PreTrainedTokenizer.
See the docstring of call() and decode() for more information.  __call__  < source > ( *args **kwargs  )    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 PreTrainedTokenizer’s
call(). Plea


Wav2Vec2Processor

se refer to the docstring of the above two methods for more information.  pad  < source > ( *args **kwargs  )    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 PreTrainedTokenizer’s
pad(). Please refer to the docstring of the above two methods for more information.  from_pretrained  < source > ( pretrained_model_name_or_path **kwargs  )     save_pretrained  < source > ( save_directory push_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 Hugging Face model hub after saving it. You can s


Wav2Vec2Processor

pecify 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 PreTrainedTokenizer’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 PreTrainedTokenizer’s decode(). Please refer
to the docstring of this method for more information


class transformers.Wav2Vec2ProcessorWithLM  < source > ( feature_extractor: FeatureExtractionMixin tokenizer: PreTrainedTokenizerBase decoder: BeamSearchDecoderCTC  )   Parameters   feature_extractor (Wav2Vec2FeatureExtractor) —
An instance of Wav2Vec2FeatureExtractor. The feature extractor is a required input.   tokenizer (Wav2Vec2CTCTokenizer) —
An instance of Wav2Vec2CTCTokenizer. The tokenizer is a required input.   decoder (pyctcdecode.BeamSearchDecoderCTC) —
An instance of pyctcdecode.BeamSearchDecoderCTC. The decoder is a required input.    Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor, a Wav2Vec2 CTC tokenizer and a decoder
with language model support into a single processor for language model boosted speech recognition decoding.  __call__  < source > ( *args **kwargs  )    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_pr


ocessor() this method forwards all its arguments to
Wav2Vec2CTCTokenizer’s call(). Please refer to the docstring of the above two
methods for more information.  pad  < source > ( *args **kwargs  )    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.  from_pretrained  < source > ( pretrained_model_name_or_path **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
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 featu


re 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
    Instantiate a Wav2Vec2ProcessorWithLM from a pretrained Wav2Vec2 processor. This class method is simply calling Wav2Vec2FeatureExtractor’s
from_pretrained(), Wav2Vec2CTCTokenizer’s
from_pretrained(), and
pyctcdecode.BeamSearchDecoderCTC.load_from_hf_hub. Please refer to the docstrings of the methods above for more information.  save_pretrained  < source > ( save_directory  )     batch_decode  < source > ( logits: ndarray pool: typing.Union[<bound method BaseContext.Pool of <multiprocessing.context.DefaultContext object at 0x7ff0c0d96a60>>, NoneType] = None num_processes: typing.Optional[int] = None beam_width: typing.Optional[int] = None beam_prune_logp: typing.O


ptional[float] = None token_min_logp: typing.Optional[float] = None hotwords: typing.Optional[typing.Iterable[str]] = None hotword_weight: typing.Optional[float] = None alpha: typing.Optional[float] = None beta: typing.Optional[float] = None unk_score_offset: typing.Optional[float] = None lm_score_boundary: typing.Optional[bool] = None output_word_offsets: bool = False n_best: int = 1  )   Parameters   logits (np.ndarray) —
The logits output vector of the model representing the log probabilities for each token.   pool (multiprocessing.Pool, optional) —
An optional user-managed pool. If not set, one will be automatically created and closed. The pool
should be instantiated after Wav2Vec2ProcessorWithLM. Otherwise, the LM won’t be available to the
pool’s sub-processes.
Currently, only pools created with a ‘fork’ context can be used. If a ‘spawn’ pool is passed, it will
be ignored and sequential decoding will be used instead.
   num_processes (int, optional) —
If pool is


 not set, number of processes on which the function should be parallelized over. Defaults
to the number of available CPUs.   beam_width (int, optional) —
Maximum number of beams at each step in decoding. Defaults to pyctcdecode’s DEFAULT_BEAM_WIDTH.   beam_prune_logp (int, optional) —
Beams that are much worse than best beam will be pruned Defaults to pyctcdecode’s DEFAULT_PRUNE_LOGP.   token_min_logp (int, optional) —
Tokens below this logp are skipped unless they are argmax of frame Defaults to pyctcdecode’s
DEFAULT_MIN_TOKEN_LOGP.   hotwords (List[str], optional) —
List of words with extra importance, can be OOV for LM   hotword_weight (int, optional) —
Weight factor for hotword importance Defaults to pyctcdecode’s DEFAULT_HOTWORD_WEIGHT.   alpha (float, optional) —
Weight for language model during shallow fusion   beta (float, optional) —
Weight for length score adjustment of during scoring   unk_score_offset (float, optional) —
Amount of log score offset for unk


nown tokens   lm_score_boundary (bool, optional) —
Whether to have kenlm respect boundaries when scoring   output_word_offsets (bool, optional, defaults to False) —
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.   n_best (int, optional, defaults to 1) —
Number of best hypotheses to return. If n_best is greater than 1, the returned text will be a list
of lists of strings, logit_score will be a list of lists of floats, and lm_score will be a list of
lists of floats, where the length of the outer list will correspond to the batch size and the length of
the inner list will correspond to the number of returned hypotheses . The value should be >= 1.
Please take a look at the Example of decode() to better understand how to
make use of output_word_offsets. batch_decode() works the same way with
batched output.
    Batch decode output logits to a


udio transcription with language model support. This function makes use of Python’s multiprocessing. Currently, multiprocessing is available only on Unix
systems (see this issue). If you are decoding multiple batches, consider creating a Pool and passing it to batch_decode. Otherwise,
batch_decode will be very slow since it will create a fresh Pool for each call. See usage example below. Example:
See Decoding multiple audios.  decode  < source > ( logits: ndarray beam_width: typing.Optional[int] = None beam_prune_logp: typing.Optional[float] = None token_min_logp: typing.Optional[float] = None hotwords: typing.Optional[typing.Iterable[str]] = None hotword_weight: typing.Optional[float] = None alpha: typing.Optional[float] = None beta: typing.Optional[float] = None unk_score_offset: typing.Optional[float] = None lm_score_boundary: typing.Optional[bool] = None output_word_offsets: bool = False n_best: int = 1  )   Parameters   logits (np.ndarray) —
The logits output ve


ctor of the model representing the log probabilities for each token.   beam_width (int, optional) —
Maximum number of beams at each step in decoding. Defaults to pyctcdecode’s DEFAULT_BEAM_WIDTH.   beam_prune_logp (int, optional) —
A threshold to prune beams with log-probs less than best_beam_logp + beam_prune_logp. The value should
be <= 0. Defaults to pyctcdecode’s DEFAULT_PRUNE_LOGP.   token_min_logp (int, optional) —
Tokens with log-probs below token_min_logp are skipped unless they are have the maximum log-prob for an
utterance. Defaults to pyctcdecode’s DEFAULT_MIN_TOKEN_LOGP.   hotwords (List[str], optional) —
List of words with extra importance which can be missing from the LM’s vocabulary, e.g. [“huggingface”]   hotword_weight (int, optional) —
Weight multiplier that boosts hotword scores. Defaults to pyctcdecode’s DEFAULT_HOTWORD_WEIGHT.   alpha (float, optional) —
Weight for language model during shallow fusion   beta (float, optional) —
Weight for length


score adjustment of during scoring   unk_score_offset (float, optional) —
Amount of log score offset for unknown tokens   lm_score_boundary (bool, optional) —
Whether to have kenlm respect boundaries when scoring   output_word_offsets (bool, optional, defaults to False) —
Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate
and model downsampling rate to compute the time-stamps of transcribed words.   n_best (int, optional, defaults to 1) —
Number of best hypotheses to return. If n_best is greater than 1, the returned text will be a list
of strings, logit_score will be a list of floats, and lm_score will be a list of floats, where the
length of these lists will correspond to the number of returned hypotheses. The value should be >= 1.
Please take a look at the example below to better understand how to make use of output_word_offsets.
    Decode output logits to audio transcription with language model support.  Example:


   Copied >>> # Let's see how to retrieve time steps for a model
>>> from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
>>> from datasets import load_dataset
>>> import datasets
>>> import torch
>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
>>> processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
>>> # load first sample of English common_voice
>>> dataset = load_dataset("common_voice", "en", split="train", streaming=True)
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> dataset_iter = iter(dataset)
>>> sample = next(dataset_iter)
>>> # forward sample through model to get greedily predicted transcription ids
>>> input_values = processor(sample["audio"]["array"], return_tensors="pt").input_values
>>> with torch.no_grad():
...     logits = model(input_values).logits[0].cpu().numpy()
>>>


# retrieve word stamps (analogous commands for `output_char_offsets`)
>>> outputs = processor.decode(logits, output_word_offsets=True)
>>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate
>>> time_offset = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
>>> word_offsets = [
...     {
...         "word": d["word"],
...         "start_time": round(d["start_offset"] * time_offset, 2),
...         "end_time": round(d["end_offset"] * time_offset, 2),
...     }
...     for d in outputs.word_offsets
... ]
>>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer:
>>> # https://huggingface.co/datasets/common_voice/viewer/en/train
>>> word_offsets[:4]
[{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.66, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}, {'word': 'LOOK', 'start_time': 3.0, 'end_time': 3.1


Decoding multiple audios

If you are planning to decode multiple batches of audios, you should consider using batch_decode() and passing an instantiated multiprocessing.Pool.
Otherwise, batch_decode() performance will be slower than calling decode() for each audio individually, as it internally instantiates a new Pool for every call. See the example below:
  Copied >>> # Let's see how to use a user-managed pool for batch decoding multiple audios
>>> from multiprocessing import get_context
>>> from transformers import AutoTokenizer, AutoProcessor, AutoModelForCTC
>>> from datasets import load_dataset
>>> import datasets
>>> import torch
>>> # import model, feature extractor, tokenizer
>>> model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm").to("cuda")
>>> processor =


Decoding multiple audios

 AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
>>> # load example dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000))
>>> def map_to_array(batch):
...     batch["speech"] = batch["audio"]["array"]
...     return batch
>>> # prepare speech data for batch inference
>>> dataset = dataset.map(map_to_array, remove_columns=["audio"])
>>> def map_to_pred(batch, pool):
...     inputs = processor(batch["speech"], sampling_rate=16_000, padding=True, return_tensors="pt")
...     inputs = {k: v.to("cuda") for k, v in inputs.items()}
...     with torch.no_grad():
...         logits = model(**inputs).logits
...     transc


Decoding multiple audios

ription = processor.batch_decode(logits.cpu().numpy(), pool).text
...     batch["transcription"] = transcription
...     return batch
>>> # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`.
>>> #       otherwise, the LM won't be available to the pool's sub-processes
>>> # select number of processes and batch_size based on number of CPU cores available and on dataset size
>>> with get_context("fork").Pool(processes=2) as pool:
...     result = dataset.map(
...         map_to_pred, batched=True, batch_size=2, fn_kwargs={"pool": pool}, remove_columns=["speech"]
...     )
>>> result["transcription"][:2]
['MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL', "NOR IS MISTER COULTER'S MANNER LESS INTERESTING THAN HIS MATTER


Wav2Vec2 specific outputs

class transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput  < source > ( text: typing.Union[typing.List[typing.List[str]], typing.List[str], str] logit_score: typing.Union[typing.List[typing.List[float]], typing.List[float], float] = None lm_score: typing.Union[typing.List[typing.List[float]], typing.List[float], float] = None word_offsets: typing.Union[typing.List[typing.List[typing.List[typing.Dict[str, typing.Union[int, str]]]]], typing.List[typing.List[typing.Dict[str, typing.Union[int, str]]]], typing.List[typing.Dict[str, typing.Union[int, str]]]] = None  )   Parameters   text (list of str or str) —
Decoded logits in text from. Usually the speech transcription.   logit_score (list of float or float) —
Total logit score of the beams associated with produced text.   lm_score (list of float) —
Fused lm_score of the beams associated with produced text.   word_offsets (list of List[Dict[str, Union[int, str]]] or List[Dict[str,


Wav2Vec2 specific outputs

 Union[int, str]]]) —
Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets
can be used to compute time stamps for each word.    Output type of Wav2Vec2DecoderWithLM, with transcription.
 class transformers.modeling_outputs.Wav2Vec2BaseModelOutput  < source > ( last_hidden_state: FloatTensor = None extract_features: FloatTensor = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   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.   extract_features (torch.FloatTensor of shape (batch_size, sequence_length, conv_dim[-1])) —
Sequence of extracted feature vectors of the last convolutional layer of the model.   hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True i


Wav2Vec2 specific outputs

s 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.    Base class for models that have been trained with the Wav2Vec2 loss objective.
 class transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None projected_states: FloatTensor = None projected_quantized_states: FloatTensor = None code


Wav2Vec2 specific outputs

vector_perplexity: FloatTensor = None hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None contrastive_loss: typing.Optional[torch.FloatTensor] = None diversity_loss: typing.Optional[torch.FloatTensor] = None  )   Parameters   loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) —
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the official
paper . (classification) loss.   projected_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) —
Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked
projected quantized states.   projected_quantized_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) —
Quantized extracted feature vectors projected to config.proj_codevect


Wav2Vec2 specific outputs

or_dim representing the positive
target vectors for contrastive loss.   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.   contrastive_loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) —
The contrastive loss (L_m) as stated


Wav2Vec2 specific outputs

 in the official paper .   diversity_loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) —
The diversity loss (L_d) as stated in the official paper .    Output type of Wav2Vec2ForPreTraining, with potential hidden states and attentions.
 class transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput  < source > ( last_hidden_state: Array = None extract_features: Array = None hidden_states: typing.Optional[typing.Tuple[jax.Array]] = None attentions: typing.Optional[typing.Tuple[jax.Array]] = None  )   Parameters   last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states at the output of the last layer of the model.   extract_features (jnp.ndarray of shape (batch_size, sequence_length, last_conv_dim)) —
Sequence of extracted feature vectors of the last convolutional layer of the model with last_conv_dim
being the dimension of the last convolution


Wav2Vec2 specific outputs

al layer.   hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of jnp.ndarray (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.    Output type of FlaxWav2Vec2BaseModelOutput, with potential hidden states and attentions.  replace  < source > ( **updates  )    “Returns a new object replacing the specified fields with new values.
 class transformers.models.wav2vec


Wav2Vec2 specific outputs

2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput  < source > ( projected_states: Array = None projected_quantized_states: Array = None codevector_perplexity: Array = None hidden_states: typing.Optional[typing.Tuple[jax.Array]] = None attentions: typing.Optional[typing.Tuple[jax.Array]] = None  )   Parameters   loss (optional, returned when model is in train mode, jnp.ndarray of shape (1,)) —
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the official
paper . (classification) loss.   projected_states (jnp.ndarray of shape (batch_size, sequence_length, config.proj_codevector_dim)) —
Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked
projected quantized states.   projected_quantized_states (jnp.ndarray of shape (batch_size, sequence_length, config.proj_codevector_dim)) —
Quantized extracted feature vectors projected to config.proj_codevector_dim representing th


Wav2Vec2 specific outputs

e positive
target vectors for contrastive loss.   hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of jnp.ndarray (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.    Output type of FlaxWav2Vec2ForPreTrainingOutput, with potential hidden states and attentions.  replace  < source > ( **updates  )    “Returns a new object replacing the specified fields with new


Wav2Vec2Model

class transformers.Wav2Vec2Model  < source > ( config: Wav2Vec2Config  )   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.    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 m


Wav2Vec2Model

atter related to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None mask_time_indices: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)  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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.


Wav2Vec2Model

 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?
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 return


Wav2Vec2Model

ed
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.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Wav2Vec2BaseModelOutput 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.
extract_features (torch.FloatTensor of shape (batch_size, sequence_length, conv_dim[-1])) — Sequence of extracted feature vectors of the last convolutiona


Wav2Vec2Model

l 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.
  The Wav2Vec2Model forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one shou


Wav2Vec2Model

ld 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 AutoProcessor, Wav2Vec2Model
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 292,


Wav2Vec2ForCTC

class transformers.Wav2Vec2ForCTC  < source > ( config target_lang: typing.Optional[str] = None  )   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.    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


Wav2Vec2ForCTC

 and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None  ) → transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)  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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.   attention_mask (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing co


Wav2Vec2ForCTC

nvolution 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?
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 fo


Wav2Vec2ForCTC

r
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
transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.CausalLMOutput 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.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTenso


Wav2Vec2ForCTC

r 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(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 model at the output of each layer plus the optional 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.
  The Wav2Vec2ForCTC forward meth


Wav2Vec2ForCTC

od, 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 AutoProcessor, Wav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_i


Wav2Vec2ForCTC

ds = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids
>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
53.48  load_adapter  < source > ( target_lang: str force_load = True **kwargs  )   Parameters   target_lang (str) —
Has to be a language id of an existing adapter weight. Adapter weights are stored in the format
adapter..safetensors or adapter..bin   force_load (bool, defaults to True) —
Whether the weights shall be loaded even if target_lang matches self.target_lang.   cache_dir (Union[str, os.PathLike], optional) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.   force_download (bool, optional, defaults to Fals


Wav2Vec2ForCTC

e) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.   resume_download (bool, optional, defaults to False) —
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.   proxies (Dict[str, str], optional) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.   local_files_only(bool, optional, defaults to False) —
Whether or not to only look at local files (i.e., do not try to download the model).   token (str or bool, optional) —
The token to use as HTTP bearer authorization for remote files. If True, or not specified, will use
the token generated when running huggingface-cli login (stored in ~/.huggingface).   revision (str, optional, defaults to "main") —
The specific model version to use. It can be a branch name, a tag name, or a


Wav2Vec2ForCTC

commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision can be any
identifier allowed by git.
To test a pull request you made on the Hub, you can pass `revision=“refs/pr/“.
   mirror (str, optional) —
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.    Load a language adapter model from a pre-trained adapter model. Activate the special “offline-mode” to
use this method in a firewalled environment.  Examples:   Copied >>> from transformers import Wav2Vec2ForCTC, AutoProcessor
>>> ckpt = "facebook/mms-1b-all"
>>> processor = AutoProcessor.from_pretrained(ckpt)
>>> model = Wav2Vec2ForCTC.from_pretrained(ckpt, target_lang="eng")
>>> # set specific language
>>> processor.tokenizer.set_target_lang("spa")
>>> model.load_adapter("spa"


class transformers.Wav2Vec2ForSequenceClassification  < source > ( config  )   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.    Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting. 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 re


gular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None  ) → transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)  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 AutoProcessor should be used for padding and
conversion into


 a tensor of type torch.FloatTensor. See 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?
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, option


al) —
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,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.SequenceClassifierOutput 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.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatT


ensor), 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.
  The Wav2Vec2ForSequenceClassification 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 AutoFeatureExtractor, Wav2Vec2ForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo"


, "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
>>> model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'_unknown_'
>>> # compute loss - target_label is e.g. "down"
>>> target_label = model.config.id2label[0]
>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2


class transformers.Wav2Vec2ForAudioFrameClassification  < source > ( config  )   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.    Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization. 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 reg


ular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)  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 AutoProcessor should


 be used for padding and
conversion into a tensor of type torch.FloatTensor. See 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?
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 de


pending 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,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.TokenClassifierOutpu


t or tuple(torch.FloatTensor)

A transformers.modeling_outputs.TokenClassifierOutput 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.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
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 model


 at the output of each layer plus the optional 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.
  The Wav2Vec2ForAudioFrameClassification 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 AutoFeatureExtractor, Wav2Vec2


ForAudioFrameClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sd")
>>> model = Wav2Vec2ForAudioFrameClassification.from_pretrained("anton-l/wav2vec2-base-superb-sd")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> probabilities = torch.sigmoid(logits[0])
>>> # labels is a one-hot array of shape (num_frames, num_speakers)
>>> labels = (probabilities > 0.5).long()
>>> labels[0].tolist()
[0, 0


Wav2Vec2ForXVector

class transformers.Wav2Vec2ForXVector  < source > ( config  )   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.    Wav2Vec2 Model with an XVector feature extraction head on top for tasks like Speaker Verification. 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 documenta


Wav2Vec2ForXVector

tion for all matter related to general usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None  ) → transformers.modeling_outputs.XVectorOutput or tuple(torch.FloatTensor)  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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() f


Wav2Vec2ForXVector

or 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?
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 attenti


Wav2Vec2ForXVector

on 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,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If
config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.XVectorOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.XVectorOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements


Wav2Vec2ForXVector

depending on the configuration (Wav2Vec2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, config.xvector_output_dim)) — Classification hidden states before AMSoftmax.
embeddings (torch.FloatTensor of shape (batch_size, config.xvector_output_dim)) — Utterance embeddings used for vector similarity-based retrieval.
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


Wav2Vec2ForXVector

 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.
  The Wav2Vec2ForXVector 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 AutoFeatureExtractor, Wav2Vec2ForXVector
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = data


Wav2Vec2ForXVector

set.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sv")
>>> model = Wav2Vec2ForXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(
...     [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
... )
>>> with torch.no_grad():
...     embeddings = model(**inputs).embeddings
>>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
>>> # the resulting embeddings can be used for cosine similarity-based retrieval
>>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
>>> similarity = cosine_sim(embeddings[0], embeddings[1])
>>> threshold = 0.7  # the optimal threshold is dataset-dependent
>>> if similarity < threshold:
...     print("Speakers are not the same!")
>>> round(similarity.item(), 2)


class transformers.Wav2Vec2ForPreTraining  < source > ( config: Wav2Vec2Config  )   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.    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 gen


eral usage and
behavior.  forward  < source > ( input_values: typing.Optional[torch.Tensor] attention_mask: typing.Optional[torch.Tensor] = None mask_time_indices: typing.Optional[torch.BoolTensor] = None sampled_negative_indices: typing.Optional[torch.BoolTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor)  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 AutoProcessor should be used for padding and
conversio


n into a tensor of type torch.FloatTensor. See 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?
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.   sampled_negative_indices (torch.BoolTensor of shape (batch_size, sequence_length, num_negatives), optional) —
Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
Required input for pre-training.   Returns
transformers.


models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor)

A transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput 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.
loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) — Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the official
paper . (classification) loss.
projected_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) — Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked
projected quantized states.
projected_quantized_states (torch.FloatTensor of shape (batch_size, sequence_length,


config.proj_codevector_dim)) — Quantized extracted feature vectors projected to config.proj_codevector_dim representing the positive
target vectors for contrastive loss.
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
hea


ds.
contrastive_loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) — The contrastive loss (L_m) as stated in the official paper .
diversity_loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) — The diversity loss (L_d) as stated in the official paper .
  The Wav2Vec2ForPreTraining 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 AutoFeatureExtractor, Wav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
>>> from datas


ets import load_dataset
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
>>> model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> input_values = feature_extractor(ds[0]["audio"]["array"], 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).item()
>>> mask_time_indices = _compute_mask_indices(
...     shape=(batch_size, sequence_length), mask_prob=0.2, mask_length=2
... )
>>> sampled_negative_indices = _sample_negative_indices(
...     features_shape=(batch_size, sequence_length),
...     num_negatives=model.config.num_negatives,
...     mask_time_indices=mask_time_indices,
... )
>>> mask_tim


e_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
>>> sampled_negative_indices = torch.tensor(
...     data=sampled_negative_indices, device=input_values.device, dtype=torch.long
... )
>>> 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
>>> cosine_sim[mask_time_indices.to(torch.bool)].mean() > 0.5
tensor(True)
>>> # for contrastive loss training model should be put into train mode
>>> model = model.train()
>>> loss = model(
...     input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
... ).lo


TFWav2Vec2Model

class transformers.TFWav2Vec2Model  < source > ( *args **kwargs  )   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.    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. TensorFlow models and layers in transformers accept two formats as input: having all inputs as k


TFWav2Vec2Model

eyword 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_values only and nothing else: model(input_values) 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


TFWav2Vec2Model

, 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}) 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_values: tf.Tensor attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)  Parameters   input_values (np.ndarray, tf.Tensor, List[tf.Tensor] Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each e


TFWav2Vec2Model

xample must have the shape ({0})) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
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_positio


TFWav2Vec2Model

n_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, o


TFWav2Vec2Model

ptional) —
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.TFBaseModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.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_stat


TFWav2Vec2Model

e (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.
  The TFWav2Vec2Model forward method, overrides the __call__ special method. Alth


TFWav2Vec2Model

ough 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 AutoProcessor, TFWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.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("hf-internal-testing/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_sta


class transformers.TFWav2Vec2ForSequenceClassification  < source > ( *args **kwargs  )     call  < source > ( input_values: tf.Tensor attention_mask: tf.Tensor | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None return_dict: bool | None = None labels: tf.Tensor | None = None training: bool = False  )


TFWav2Vec2ForCTC

class transformers.TFWav2Vec2ForCTC  < source > ( *args **kwargs  )   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.    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. TensorFlow models and layers in transformers accept two formats as input: having all inputs as


TFWav2Vec2ForCTC

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_values only and nothing else: model(input_values) 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,