| | |
|---|
| | | reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame |
|---|
| | | # print('reserve_frame_idx: ' + str(reserve_frame_idx)) |
|---|
| | | # print('frame_frame: ' + str(frame_from_waveforms)) |
|---|
| | | self.reserve_waveforms = self.waveforms[:, |
|---|
| | | reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length] |
|---|
| | | sample_length = ( |
|---|
| | | frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length |
|---|
| | | self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length] |
|---|
| | | sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length |
|---|
| | | self.waveforms = self.waveforms[:, :sample_length] |
|---|
| | | else: |
|---|
| | | # update self.reserve_waveforms and self.lfr_splice_cache |
|---|
| | |
|---|
| | | for i in range(batch_size): |
|---|
| | | waveform_length = input_lengths[i] |
|---|
| | | waveform = input[i][:waveform_length] |
|---|
| | | waveform = waveform.unsqueeze(0).numpy() |
|---|
| | | waveform = waveform.numpy() |
|---|
| | | mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift) |
|---|
| | | mat = eend_ola_feature.transform(mat) |
|---|
| | | mat = mat.splice(mat, context_size=self.lfr_m) |
|---|
| | | mat = eend_ola_feature.splice(mat, context_size=self.lfr_m) |
|---|
| | | mat = mat[::self.lfr_n] |
|---|
| | | mat = torch.from_numpy(mat) |
|---|
| | | feat_length = mat.size(0) |
|---|
