| | |
|---|
| | | return acoustic_embeds, token_num, alphas, cif_peak
|
|---|
| | |
|
|---|
| | | def forward_chunk(self, hidden, cache=None):
|
|---|
| | | b, t, d = hidden.size()
|
|---|
| | | h = hidden
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | | queries = self.pad(context)
|
|---|
| | |
|---|
| | | alphas = alphas * mask_chunk_predictor
|
|---|
| | |
|
|---|
| | | if cache is not None:
|
|---|
| | | if cache["is_final"]:
|
|---|
| | | alphas[:, cache["stride"] + cache["pad_left"] - 1] += 0.45
|
|---|
| | | if cache["cif_hidden"] is not None:
|
|---|
| | | hidden = torch.cat((cache["cif_hidden"], hidden), 1)
|
|---|
| | | if cache["cif_alphas"] is not None:
|
|---|
| | |
|---|
| | | mask_chunk_peak_predictor[:, :pre_alphas_length] = 1.0
|
|---|
| | | mask_chunk_peak_predictor[:, pre_alphas_length + cache["pad_left"]:pre_alphas_length + cache["stride"] + cache["pad_left"]] = 1.0
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | if mask_chunk_peak_predictor is not None:
|
|---|
| | | cif_peak = cif_peak * mask_chunk_peak_predictor.squeeze(-1)
|
|---|
| | |
|
|---|
