| |
| |
| |
| |
|
|
|
|
| import torch |
| import torch.nn as nn |
| from torchlibrosa import LogmelFilterBank, Spectrogram |
|
|
|
|
| class AudioFeature(nn.Module): |
|
|
| def __init__(self, audio_config): |
| super().__init__() |
| self.mel_trans = Spectrogram(n_fft=audio_config["n_fft"], |
| hop_length=audio_config["hop_length"], |
| win_length=audio_config["n_fft"], |
| window='hann', |
| center=True, |
| pad_mode='reflect', |
| freeze_parameters=True) |
|
|
| self.log_trans = LogmelFilterBank(sr=audio_config["sr"], |
| n_fft=audio_config["n_fft"], |
| n_mels=audio_config["n_mels"], |
| fmin=audio_config["f_min"], |
| fmax=audio_config["f_max"], |
| ref=1.0, |
| amin=1e-10, |
| top_db=None, |
| freeze_parameters=True) |
|
|
| def forward(self, input): |
|
|
| mel_feats = self.mel_trans(input) |
| log_mel = self.log_trans(mel_feats) |
| return log_mel |
|
|
