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# coding=utf-8
# Copyright 2023 Xiaomi Corporation and 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 CED.
"""
from typing import Optional, Union
import numpy as np
import torch
import torchaudio.transforms as audio_transforms
from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
from transformers.feature_extraction_utils import BatchFeature
from transformers.utils import logging
logger = logging.get_logger(__name__)
class CedFeatureExtractor(SequenceFeatureExtractor):
r"""
CedFeatureExtractor extracts Mel spectrogram features from audio signals.
Args:
f_min (int, *optional*, defaults to 0): Minimum frequency for the Mel filterbank.
sampling_rate (int, *optional*, defaults to 16000):
Sampling rate of the input audio signal.
win_size (int, *optional*, defaults to 512): Window size for the STFT.
center (bool, *optional*, defaults to `True`):
Whether to pad the signal on both sides to center it.
n_fft (int, *optional*, defaults to 512): Number of FFT points for the STFT.
f_max (int, optional, *optional*): Maximum frequency for the Mel filterbank.
hop_size (int, *optional*, defaults to 160): Hop size for the STFT.
feature_size (int, *optional*, defaults to 64): Number of Mel bands to generate.
padding_value (float, *optional*, defaults to 0.0): Value for padding.
Returns:
BatchFeature: A BatchFeature object containing the extracted features.
"""
def __init__(
self,
f_min: int = 0,
sampling_rate: int = 16000,
win_size: int = 512,
center: bool = True,
n_fft: int = 512,
f_max: Optional[int] = None,
hop_size: int = 160,
feature_size: int = 64,
padding_value: float = 0.0,
**kwargs,
):
super().__init__(
feature_size=feature_size,
sampling_rate=sampling_rate,
padding_value=padding_value,
**kwargs,
)
self.f_min = f_min
self.win_size = win_size
self.center = center
self.n_fft = n_fft
self.f_max = f_max
self.hop_size = hop_size
def __call__(
self,
x: Union[np.ndarray, torch.Tensor],
sampling_rate: Optional[int] = None,
return_tensors="pt",
) -> BatchFeature:
r"""
Extracts Mel spectrogram features from an audio signal tensor.
Args:
x: Input audio signal tensor.
Returns:
BatchFeature: A dictionary containing the extracted features.
"""
if sampling_rate is None:
sampling_rate = self.sampling_rate
if return_tensors != "pt":
raise NotImplementedError(
"Only return_tensors='pt' is currently supported."
)
mel_spectrogram = audio_transforms.MelSpectrogram(
f_min=self.f_min,
sample_rate=sampling_rate,
win_length=self.win_size,
center=self.center,
n_fft=self.n_fft,
f_max=self.f_max,
hop_length=self.hop_size,
n_mels=self.feature_size,
)
amplitude_to_db = audio_transforms.AmplitudeToDB(top_db=120)
x = torch.from_numpy(x).float() if isinstance(x, np.ndarray) else x.float()
if x.dim() == 1:
x = x.unsqueeze(0)
x = mel_spectrogram(x)
x = amplitude_to_db(x)
return BatchFeature({"input_values": x})
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