Spaces:
Runtime error
Runtime error
File size: 8,614 Bytes
1ce5e18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Feature extractor class for Audio Spectrogram Transformer.
"""
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 ...utils import TensorType, logging
logger = logging.get_logger(__name__)
class ASTFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Audio Spectrogram Transformer (AST) feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] 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, pads/truncates them to a fixed
length and normalizes them using a mean and standard deviation.
Args:
feature_size (`int`, *optional*, defaults to 1):
The feature dimension of the extracted features.
sampling_rate (`int`, *optional*, defaults to 16000):
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
num_mel_bins (`int`, *optional*, defaults to 128):
Number of Mel-frequency bins.
max_length (`int`, *optional*, defaults to 1024):
Maximum length to which to pad/truncate the extracted features.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the log-Mel features using `mean` and `std`.
mean (`float`, *optional*, defaults to -4.2677393):
The mean value used to normalize the log-Mel features. Uses the AudioSet mean by default.
std (`float`, *optional*, defaults to 4.5689974):
The standard deviation value used to normalize the log-Mel features. Uses the AudioSet standard deviation
by default.
return_attention_mask (`bool`, *optional*, defaults to `False`):
Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`.
"""
model_input_names = ["input_values", "attention_mask"]
def __init__(
self,
feature_size=1,
sampling_rate=16000,
num_mel_bins=128,
max_length=1024,
padding_value=0.0,
do_normalize=True,
mean=-4.2677393,
std=4.5689974,
return_attention_mask=False,
**kwargs,
):
super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
self.num_mel_bins = num_mel_bins
self.max_length = max_length
self.do_normalize = do_normalize
self.mean = mean
self.std = std
self.return_attention_mask = return_attention_mask
def _extract_fbank_features(
self,
waveform: np.ndarray,
max_length: int,
) -> 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)
fbank = ta_kaldi.fbank(
waveform,
htk_compat=True,
sample_frequency=self.sampling_rate,
use_energy=False,
window_type="hanning",
num_mel_bins=self.num_mel_bins,
dither=0.0,
frame_shift=10,
)
n_frames = fbank.shape[0]
difference = max_length - n_frames
# pad or truncate, depending on difference
if difference > 0:
pad_module = torch.nn.ZeroPad2d((0, 0, 0, difference))
fbank = pad_module(fbank)
elif difference < 0:
fbank = fbank[0:max_length, :]
fbank = fbank.numpy()
return fbank
def normalize(self, input_values: np.ndarray) -> np.ndarray:
return (input_values - (self.mean)) / (self.std * 2)
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
sampling_rate: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
Main method to featurize and prepare for the model one or several sequence(s).
Args:
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.
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.
return_tensors (`str` or [`~utils.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.
"""
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"
f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
f" {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_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
)
if is_batched:
raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech]
elif not is_batched and not isinstance(raw_speech, np.ndarray):
raw_speech = np.asarray(raw_speech, dtype=np.float32)
elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64):
raw_speech = raw_speech.astype(np.float32)
# always return batch
if not is_batched:
raw_speech = [raw_speech]
# extract fbank features and pad/truncate to max_length
features = [self._extract_fbank_features(waveform, max_length=self.max_length) for waveform in raw_speech]
# convert into BatchFeature
padded_inputs = BatchFeature({"input_values": features})
# make sure list is in array format
input_values = padded_inputs.get("input_values")
if isinstance(input_values[0], list):
padded_inputs["input_values"] = [np.asarray(feature, dtype=np.float32) for feature in input_values]
# normalization
if self.do_normalize:
padded_inputs["input_values"] = [self.normalize(feature) for feature in input_values]
if return_tensors is not None:
padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
return padded_inputs
|