| | |
|---|
| | | enc, enc_len = self.encoder(**batch) |
|---|
| | | mask = self.make_pad_mask(enc_len)[:, None, :] |
|---|
| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask) |
|---|
| | | pre_token_length = pre_token_length.round().long() |
|---|
| | | pre_token_length = pre_token_length.round().type(torch.int32) |
|---|
| | | |
|---|
| | | decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length) |
|---|
| | | decoder_out = torch.log_softmax(decoder_out, dim=-1) |
|---|
| | | sample_ids = decoder_out.argmax(dim=-1) |
|---|
| | | # sample_ids = decoder_out.argmax(dim=-1) |
|---|
| | | |
|---|
| | | return decoder_out, sample_ids |
|---|
| | | |
|---|
| | | # def get_output_size(self): |
|---|
| | | # return self.model.encoders[0].size |
|---|
| | | return decoder_out, pre_token_length |
|---|
| | | |
|---|
| | | def get_dummy_inputs(self): |
|---|
| | | speech = torch.randn(2, 30, self.feats_dim) |
|---|
