| | |
|---|
| | | 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) |
|---|
| | | |
|---|
| | | return decoder_out, pre_token_length |
|---|
| | | |
|---|
| | | # def get_output_size(self): |
|---|
| | | # return self.model.encoders[0].size |
|---|
| | | |
|---|
| | | def get_dummy_inputs(self): |
|---|
| | | speech = torch.randn(2, 30, self.feats_dim) |
|---|
| | | speech_lengths = torch.tensor([6, 30], dtype=torch.int32) |
|---|
| | | return (speech, speech_lengths) |
|---|
| | | |
|---|
| | | def get_dummy_inputs_txt(self, txt_file: str = "/mnt/workspace/data_fbank/0207/12345.wav.fea.txt"): |
|---|
| | | import numpy as np |
|---|
| | | fbank = np.loadtxt(txt_file) |
|---|
| | | fbank_lengths = np.array([fbank.shape[0], ], dtype=np.int32) |
|---|
| | | speech = torch.from_numpy(fbank[None, :, :].astype(np.float32)) |
|---|
| | | speech_lengths = torch.from_numpy(fbank_lengths.astype(np.int32)) |
|---|
| | | return (speech, speech_lengths) |
|---|
| | | |
|---|
| | | def get_input_names(self): |
|---|
| | | return ['speech', 'speech_lengths'] |
|---|
| | | |
|---|
