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