| | |
|---|
| | |
|
|---|
| | | return acoustic_embeds, token_num, alphas, cif_peak
|
|---|
| | |
|
|---|
| | | def forward_chunk(self, hidden, cache=None):
|
|---|
| | | def forward_chunk(self, hidden, cache=None, **kwargs):
|
|---|
| | | is_final = kwargs.get("is_final", False)
|
|---|
| | | batch_size, len_time, hidden_size = hidden.shape
|
|---|
| | | h = hidden
|
|---|
| | | context = h.transpose(1, 2)
|
|---|
| | |
|---|
| | |
|
|---|
| | | if cache is not None and "chunk_size" in cache:
|
|---|
| | | alphas[:, :cache["chunk_size"][0]] = 0.0
|
|---|
| | | if "is_final" in cache and not cache["is_final"]:
|
|---|
| | | if not is_final:
|
|---|
| | | alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
|
|---|
| | | if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
|
|---|
| | | cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
|
|---|
| | | cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
|
|---|
| | | hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
|
|---|
| | | alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
|
|---|
| | | if cache is not None and "is_final" in cache and cache["is_final"]:
|
|---|
| | | if cache is not None and is_final:
|
|---|
| | | tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
|
|---|
| | | tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
|
|---|
| | | tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
|
|---|
| | |
|---|
| | |
|
|---|
| | | max_token_len = max(token_length)
|
|---|
| | | if max_token_len == 0:
|
|---|
| | | return hidden, torch.stack(token_length, 0)
|
|---|
| | | return hidden, torch.stack(token_length, 0), None, None
|
|---|
| | | list_ls = []
|
|---|
| | | for b in range(batch_size):
|
|---|
| | | pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
|
|---|
| | |
|---|
| | | cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
|
|---|
| | | cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
|
|---|
| | | cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
|
|---|
| | | return torch.stack(list_ls, 0), torch.stack(token_length, 0)
|
|---|
| | | return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None
|
|---|
| | |
|
|---|
| | |
|
|---|
| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
|
|---|
