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# coding=utf-8
# Copyright 2023 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 Music Audio Efficient Spectrogram Transformer.
"""
from typing import List, Optional, Union
import numpy as np
import torch
from transformers.audio_utils import mel_filter_bank, spectrogram, window_function
from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
from transformers.feature_extraction_utils import BatchFeature
from transformers.utils import TensorType, logging
logger = logging.get_logger(__name__)
class MAESTFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Music Audio Efficient Spectrogram Transformer (MAEST) 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 audio, 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 96):
Number of Mel-frequency bins.
max_length (`int`, *optional*, defaults to 1876):
Maximum length to which to pad/truncate the extracted features. Set to -1 to deactivate the functionallity.
padding_value (`int`, *optional*, defaults to 0.0):
The value used to pad the input waveform.
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 2.06755686098554):
The mean value used to normalize the log-Mel features. Uses the Discogs20 mean by default.
std (`float`, *optional*, defaults to 1.268292820667291):
The standard deviation value used to normalize the log-Mel features. Uses the Discogs20 standard deviation
by default.
return_attention_mask (`bool`, *optional*, defaults to `False`):
Whether or not [`~ASTFeatureExtractor.__call__`] should return `attention_mask`.
n_fft (`int`, *optional*, defaults to 512):
Length of the FFT window.
hop_length (`int`, *optional*, defaults to 256):
Number of samples between successive frames.
log_compression (`str`, *optional*, defaults to `"logC"`):
Type of log compression to apply to the mel-spectrogram. Can be one of [`None`, `log`, `logC`].
"""
model_input_names = ["input_values", "attention_mask"]
def __init__(
self,
feature_size=1,
sampling_rate=16000,
num_mel_bins=96,
max_length=1876,
padding_value=0.0,
do_normalize=True,
mean=2.06755686098554,
std=1.268292820667291,
return_attention_mask=False,
n_fft=512,
hop_length=256,
log_compression="logC",
**kwargs,
):
super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
self.sampling_rate = sampling_rate
self.n_fft = n_fft
self.hop_length = hop_length
self.log_compression = log_compression
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-spectrogram features using audio_utils.
"""
window = window_function(
window_length=self.n_fft,
name="hann",
)
mel_fb = mel_filter_bank(
num_frequency_bins=self.n_fft // 2 + 1,
num_mel_filters=self.num_mel_bins,
min_frequency=0,
max_frequency=self.sampling_rate / 2,
sampling_rate=self.sampling_rate,
norm="slaney",
mel_scale="slaney",
)
melspec = spectrogram(
waveform,
window=window,
frame_length=self.n_fft,
hop_length=self.hop_length,
power=2,
mel_filters=mel_fb,
min_value=1e-30,
mel_floor=1e-30,
pad_mode="constant",
).T
if not self.log_compression:
pass
elif self.log_compression == "log":
melspec = np.log(melspec + np.finfo(float).eps)
elif self.log_compression == "logC":
melspec = np.log10(1 + melspec * 10000)
else:
raise ValueError(
f"`log_compression` can only be one of [None, 'log', 'logC'], but got: {self.log_compression}"
)
melspec = torch.Tensor(melspec)
n_frames = melspec.shape[0]
if max_length > 0:
difference = max_length - n_frames
# pad or truncate, depending on difference
if difference > 0:
pad_module = torch.nn.ZeroPad2d((0, 0, 0, difference))
melspec = pad_module(melspec)
elif difference < 0:
melspec = melspec[0:max_length, :]
return melspec.numpy()
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
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