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import torch
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import torch.nn.functional as F
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import torchaudio
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import torchaudio.transforms as transforms
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class LogMelSpectrogram(torch.nn.Module):
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def __init__(self, sr=24000, frame_length=1920, hop_length=480, n_mel=128, f_min=0, f_max=12000,):
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super().__init__()
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self.frame_length = frame_length
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self.hop_length = hop_length
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self.mel = transforms.MelSpectrogram(
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sample_rate=sr,
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n_fft=frame_length,
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win_length=frame_length,
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hop_length=hop_length,
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center=False,
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power=1.0,
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norm="slaney",
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n_mels=n_mel,
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mel_scale="slaney",
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f_min=f_min,
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f_max=f_max
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)
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@torch.no_grad()
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def forward(self, x, target_length=None):
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x = F.pad(x, ((self.frame_length - self.hop_length) // 2,
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(self.frame_length - self.hop_length) // 2), "reflect")
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mel = self.mel(x)
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target_length = mel.shape[-1] if target_length is None else target_length
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logmel = torch.zeros(mel.shape[0], mel.shape[1], target_length).to(mel.device)
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logmel[:, :, :mel.shape[2]] = mel
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logmel = torch.log(torch.clamp(logmel, min=1e-5))
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return logmel |
