| | |
|---|
| | | rescale_line = line_item[3:(len(line_item) - 1)] |
|---|
| | | vars_list = list(rescale_line) |
|---|
| | | continue |
|---|
| | | means = np.array(means_list).astype(np.float) |
|---|
| | | vars = np.array(vars_list).astype(np.float) |
|---|
| | | means = np.array(means_list).astype(np.float32) |
|---|
| | | vars = np.array(vars_list).astype(np.float32) |
|---|
| | | cmvn = np.array([means, vars]) |
|---|
| | | cmvn = torch.as_tensor(cmvn, dtype=torch.float32) |
|---|
| | | return cmvn |
|---|
| | |
|---|
| | | feats_lens.append(feat_length) |
|---|
| | | |
|---|
| | | feats_lens = torch.as_tensor(feats_lens) |
|---|
| | | feats_pad = pad_sequence(feats, |
|---|
| | | batch_first=True, |
|---|
| | | padding_value=0.0) |
|---|
| | | if batch_size == 1: |
|---|
| | | feats_pad = feats[0][None, :, :] |
|---|
| | | else: |
|---|
| | | feats_pad = pad_sequence(feats, |
|---|
| | | batch_first=True, |
|---|
| | | padding_value=0.0) |
|---|
| | | return feats_pad, feats_lens |
|---|
| | | |
|---|
| | | def forward_fbank( |
|---|
