Source code for transformers.models.wav2vec2.feature_extraction_wav2vec2

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
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Feature extractor class for Wav2Vec2

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...file_utils import PaddingStrategy, TensorType
from ...utils import logging

logger = logging.get_logger(__name__)

[docs]class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor): r""" Constructs a Wav2Vec2 feature extractor. This feature extractor inherits from :class:`~transformers.feature_extraction_sequence_utils.SequenceFeatureExtractor` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: feature_size (:obj:`int`, defaults to 1): The feature dimension of the extracted features. sampling_rate (:obj:`int`, defaults to 16000): The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz). padding_value (:obj:`float`, defaults to 0.0): The value that is used to fill the padding values. do_normalize (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, `wav2vec2-lv60 <>`__. return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not :meth:`~transformers.Wav2Vec2FeatureExtractor.__call__` should return :obj:`attention_mask`. .. note:: Wav2Vec2 models that have set ``config.feat_extract_norm == "group"``, such as `wav2vec2-base <>`__, have **not** been trained using :obj:`attention_mask`. For such models, :obj:`input_values` should simply be padded with 0 and no :obj:`attention_mask` should be passed. For Wav2Vec2 models that have set ``config.feat_extract_norm == "layer"``, such as `wav2vec2-lv60 <>`__, :obj:`attention_mask` should be passed for batched inference. """ model_input_names = ["input_values", "attention_mask"] def __init__( self, feature_size=1, sampling_rate=16000, padding_value=0.0, return_attention_mask=False, do_normalize=True, **kwargs ): super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize @staticmethod def zero_mean_unit_var_norm( input_values: List[np.ndarray], attention_mask: List[np.ndarray], padding_value: float = 0.0 ) -> List[np.ndarray]: """ Every array in the list is normalized to have zero mean and unit variance """ if attention_mask is not None: attention_mask = np.array(attention_mask, np.int32) normed_input_values = [] for vector, length in zip(input_values, attention_mask.sum(-1)): normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) if length < normed_slice.shape[0]: normed_slice[length:] = padding_value normed_input_values.append(normed_slice) else: normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] return normed_input_values
[docs] def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, truncation: bool = False, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, sampling_rate: Optional[int] = None, **kwargs ) -> BatchFeature: """ Main method to featurize and prepare for the model one or several sequence(s). sequences. Args: raw_speech (:obj:`np.ndarray`, :obj:`List[float]`, :obj:`List[np.ndarray]`, :obj:`List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the returned list and optionally padding length (see above). truncation (:obj:`bool`): Activates truncation to cut input sequences longer than `max_length` to `max_length`. pad_to_multiple_of (:obj:`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 (:obj:`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? <../glossary.html#attention-mask>`__ .. note:: Wav2Vec2 models that have set ``config.feat_extract_norm == "group"``, such as `wav2vec2-base <>`__, have **not** been trained using :obj:`attention_mask`. For such models, :obj:`input_values` should simply be padded with 0 and no :obj:`attention_mask` should be passed. For Wav2Vec2 models that have set ``config.feat_extract_norm == "layer"``, such as `wav2vec2-lv60 <>`__, :obj:`attention_mask` should be passed for batched inference. return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`): If set, will return tensors instead of list of python integers. Acceptable values are: * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects. * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects. * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects. sampling_rate (:obj:`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 (:obj:`float`, defaults to 0.0): """ if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of {self.sampling_rate}." f"Please make sure that the provided `raw_speech` input was sampled with {self.sampling_rate} and not {sampling_rate}." ) else: logger.warning( "It is strongly recommended to pass the ``sampling_rate`` argument to this function." "Failing to do so can result in silent errors that might be hard to debug." ) is_batched = bool( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list))) ) # always return batch if not is_batched: raw_speech = [raw_speech] # convert into correct format for padding encoded_inputs = BatchFeature({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, truncation=truncation, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) # convert input values to correct format input_values = padded_inputs["input_values"] if not isinstance(input_values[0], np.ndarray): padded_inputs["input_values"] = [np.asarray(array, dtype=np.float32) for array in input_values] elif ( not isinstance(input_values, np.ndarray) and isinstance(input_values[0], np.ndarray) and input_values[0].dtype is np.dtype(np.float64) ): padded_inputs["input_values"] = [array.astype(np.float32) for array in input_values] elif isinstance(input_values, np.ndarray) and input_values.dtype is np.dtype(np.float64): padded_inputs["input_values"] = input_values.astype(np.float32) # convert attention_mask to correct format attention_mask = padded_inputs.get("attention_mask") if attention_mask is not None: padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] # zero-mean and unit-variance normalization if self.do_normalize: attention_mask = ( attention_mask if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD else None ) padded_inputs["input_values"] = self.zero_mean_unit_var_norm( padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value ) if return_tensors is not None: padded_inputs = padded_inputs.convert_to_tensors(return_tensors) return padded_inputs