Source code for transformers.models.speech_to_text.feature_extraction_speech_to_text
# coding=utf-8
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"""
Feature extractor class for Speech2Text
"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
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 Speech2TextFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Speech2Text feature extractor.
This feature extractor inherits from :class:`~transformers.Speech2TextFeatureExtractor` which contains most of the
main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts mel-filter bank features from raw speech using TorchAudio and applies utterance-level cepstral
mean and variance normalization to the extracted features.
Args:
feature_size (:obj:`int`, defaults to 80):
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).
num_mel_bins (:obj:`int`, defaults to 80):
Number of Mel-frequency bins.
padding_value (:obj:`float`, defaults to 0.0):
The value that is used to fill the padding vectors.
do_ceptral_normalize (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to apply utterance-level cepstral mean and variance normalization to extracted features.
normalize_means (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to zero-mean normalize the extracted features.
normalize_vars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to unit-variance normalize the extracted features.
"""
model_input_names = ["input_features", "attention_mask"]
def __init__(
self,
feature_size=80,
sampling_rate=16000,
num_mel_bins=80,
padding_value=0.0,
do_ceptral_normalize=True,
normalize_means=True,
normalize_vars=True,
**kwargs
):
super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
self.num_mel_bins = num_mel_bins
self.do_ceptral_normalize = do_ceptral_normalize
self.normalize_means = normalize_means
self.normalize_vars = normalize_vars
self.return_attention_mask = True
def _extract_fbank_features(
self,
waveform: np.ndarray,
) -> np.ndarray:
"""
Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
and hence the waveform should not be normalized before feature extraction.
"""
waveform = waveform * (2 ** 15) # Kaldi compliance: 16-bit signed integers
waveform = torch.from_numpy(waveform).unsqueeze(0)
features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate)
return features.numpy()
@staticmethod
def utterance_cmvn(
x: np.ndarray,
input_length: int,
normalize_means: Optional[bool] = True,
normalize_vars: Optional[bool] = True,
padding_value: float = 0.0,
) -> np.ndarray:
# make sure we normalie float32 arrays
mean = x[:input_length].mean(axis=0)
square_sums = (x[:input_length] ** 2).sum(axis=0)
if normalize_means:
x = np.subtract(x, mean)
if normalize_vars:
var = square_sums / x[:input_length].shape[0] - mean ** 2
std = np.sqrt(np.maximum(var, 1e-10))
x = np.divide(x, std)
if x.shape[0] > input_length:
x[input_length:] = padding_value
# make sure array is in float32
x = x.astype(np.float32)
return x
def normalize(
self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
) -> List[np.ndarray]:
lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars, self.padding_value)
for x, n in zip(input_features, lengths)
]
[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_tensors: Optional[Union[str, TensorType]] = None,
sampling_rate: Optional[int] = None,
return_attention_mask: Optional[bool] = 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:`True`):
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::
For Speech2TextTransoformer models, :obj:`attention_mask` should alwys be passed for batched
inference, to avoid subtle bugs.
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 :obj:`raw_speech` input was sampled. It is strongly recommended to pass
:obj:`sampling_rate` at the forward call to prevent silent errors.
padding_value (:obj:`float`, defaults to 0.0):
The value that is used to fill the padding values / vectors.
"""
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)))
)
if is_batched and not isinstance(raw_speech[0], np.ndarray):
raw_speech = [np.asarray(speech) for speech in raw_speech]
elif not is_batched and not isinstance(raw_speech, np.ndarray):
raw_speech = np.asarray(raw_speech)
elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.float64:
raw_speech = raw_speech.astype(np.float32)
# always return batch
if not is_batched:
raw_speech = [raw_speech]
# extract fbank features
features = [self._extract_fbank_features(waveform) for waveform in raw_speech]
# convert into correct format for padding
encoded_inputs = BatchFeature({"input_features": features})
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,
**kwargs,
)
# make sure list is in array format
input_features = padded_inputs.get("input_features")
if isinstance(input_features[0], list):
padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]
attention_mask = padded_inputs.get("attention_mask")
if attention_mask is not None:
padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.bool) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
attention_mask = (
np.array(attention_mask, dtype=np.bool)
if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
else None
)
padded_inputs["input_features"] = self.normalize(
padded_inputs["input_features"], attention_mask=attention_mask
)
if return_tensors is not None:
padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
return padded_inputs