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89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
from dataclasses import dataclass
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from typing import Union
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import numpy as np
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import torch
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from audio import mel_spectrogram
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from lhotse.features.base import FeatureExtractor, register_extractor
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from lhotse.utils import Seconds, compute_num_frames
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@dataclass
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class MatchaFbankConfig:
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n_fft: int
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n_mels: int
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sampling_rate: int
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hop_length: int
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win_length: int
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f_min: float
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f_max: float
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@register_extractor
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class MatchaFbank(FeatureExtractor):
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name = "MatchaFbank"
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config_type = MatchaFbankConfig
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def __init__(self, config):
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super().__init__(config=config)
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@property
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def device(self) -> Union[str, torch.device]:
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return self.config.device
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def feature_dim(self, sampling_rate: int) -> int:
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return self.config.n_mels
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def extract(
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self,
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samples: np.ndarray,
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sampling_rate: int,
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) -> torch.Tensor:
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# Check for sampling rate compatibility.
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expected_sr = self.config.sampling_rate
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assert sampling_rate == expected_sr, (
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f"Mismatched sampling rate: extractor expects {expected_sr}, "
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f"got {sampling_rate}"
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)
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samples = torch.from_numpy(samples)
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assert samples.ndim == 2, samples.shape
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assert samples.shape[0] == 1, samples.shape
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mel = (
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mel_spectrogram(
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samples,
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self.config.n_fft,
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self.config.n_mels,
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self.config.sampling_rate,
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self.config.hop_length,
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self.config.win_length,
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self.config.f_min,
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self.config.f_max,
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center=False,
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)
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.squeeze()
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.t()
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)
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assert mel.ndim == 2, mel.shape
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assert mel.shape[1] == self.config.n_mels, mel.shape
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num_frames = compute_num_frames(
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samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
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)
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if mel.shape[0] > num_frames:
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mel = mel[:num_frames]
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elif mel.shape[0] < num_frames:
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mel = mel.unsqueeze(0)
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mel = torch.nn.functional.pad(
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mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
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).squeeze(0)
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return mel.numpy()
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@property
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def frame_shift(self) -> Seconds:
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return self.config.hop_length / self.config.sampling_rate
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