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import numpy as np
import torch
from fairseq.data.audio.feature_transforms import (
AudioFeatureTransform,
register_audio_feature_transform,
)
@register_audio_feature_transform("delta_deltas")
class DeltaDeltas(AudioFeatureTransform):
"""Expand delta-deltas features from spectrum."""
@classmethod
def from_config_dict(cls, config=None):
_config = {} if config is None else config
return DeltaDeltas(_config.get("win_length", 5))
def __init__(self, win_length=5):
self.win_length = win_length
def __repr__(self):
return self.__class__.__name__
def __call__(self, spectrogram):
from torchaudio.functional import compute_deltas
assert len(spectrogram.shape) == 2, "spectrogram must be a 2-D tensor."
# spectrogram is T x F, while compute_deltas takes (…, F, T)
spectrogram = torch.from_numpy(spectrogram).transpose(0, 1)
delta = compute_deltas(spectrogram)
delta_delta = compute_deltas(delta)
out_feat = np.concatenate(
[spectrogram, delta.numpy(), delta_delta.numpy()], axis=0
)
out_feat = np.transpose(out_feat)
return out_feat
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