| | |
|---|
| | | return cmvn |
|---|
| | | |
|---|
| | | |
|---|
| | | def apply_cmvn(inputs, cmvn_file): # noqa |
|---|
| | | def apply_cmvn(inputs, cmvn): # noqa |
|---|
| | | """ |
|---|
| | | Apply CMVN with mvn data |
|---|
| | | """ |
|---|
| | |
|---|
| | | dtype = inputs.dtype |
|---|
| | | frame, dim = inputs.shape |
|---|
| | | |
|---|
| | | cmvn = load_cmvn(cmvn_file) |
|---|
| | | means = np.tile(cmvn[0:1, :dim], (frame, 1)) |
|---|
| | | vars = np.tile(cmvn[1:2, :dim], (frame, 1)) |
|---|
| | | inputs += torch.from_numpy(means).type(dtype).to(device) |
|---|
| | |
|---|
| | | self.dither = dither |
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| | | self.snip_edges = snip_edges |
|---|
| | | self.upsacle_samples = upsacle_samples |
|---|
| | | self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file) |
|---|
| | | |
|---|
| | | def output_size(self) -> int: |
|---|
| | | return self.n_mels * self.lfr_m |
|---|
| | |
|---|
| | | |
|---|
| | | if self.lfr_m != 1 or self.lfr_n != 1: |
|---|
| | | mat = apply_lfr(mat, self.lfr_m, self.lfr_n) |
|---|
| | | if self.cmvn_file is not None: |
|---|
| | | mat = apply_cmvn(mat, self.cmvn_file) |
|---|
| | | if self.cmvn is not None: |
|---|
| | | mat = apply_cmvn(mat, self.cmvn) |
|---|
| | | feat_length = mat.size(0) |
|---|
| | | feats.append(mat) |
|---|
| | | feats_lens.append(feat_length) |
|---|
| | |
|---|
| | | mat = input[i, :input_lengths[i], :] |
|---|
| | | if self.lfr_m != 1 or self.lfr_n != 1: |
|---|
| | | mat = apply_lfr(mat, self.lfr_m, self.lfr_n) |
|---|
| | | if self.cmvn_file is not None: |
|---|
| | | mat = apply_cmvn(mat, self.cmvn_file) |
|---|
| | | if self.cmvn is not None: |
|---|
| | | mat = apply_cmvn(mat, self.cmvn) |
|---|
| | | feat_length = mat.size(0) |
|---|
| | | feats.append(mat) |
|---|
| | | feats_lens.append(feat_length) |
|---|
