| | |
|---|
| | | text: (Batch, Length) |
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| | | text_lengths: (Batch,) |
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| | | """ |
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| | | text_lengths = text_lengths.squeeze() |
|---|
| | | speech_lengths = speech_lengths.squeeze() |
|---|
| | | assert text_lengths.dim() == 1, text_lengths.shape |
|---|
| | | # Check that batch_size is unified |
|---|
| | | assert ( |
|---|
| | |
|---|
| | | |
|---|
| | | # force_gatherable: to-device and to-tensor if scalar for DataParallel |
|---|
| | | if self.length_normalized_loss: |
|---|
| | | batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size) |
|---|
| | | batch_size = (text_lengths + self.predictor_bias).sum() |
|---|
| | | loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) |
|---|
| | | return loss, stats, weight |
|---|
| | | |
|---|
| | |
|---|
| | | # predictor forward |
|---|
| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
|---|
| | | encoder_out.device) |
|---|
| | | pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask, |
|---|
| | | ignore_id=self.ignore_id) |
|---|
| | | pre_acoustic_embeds = self.predictor(encoder_out, ys_pad, encoder_out_mask, ignore_id=self.ignore_id)[0] |
|---|
| | | # decoder forward |
|---|
| | | decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True) |
|---|
| | | selected = self._hotword_representation(hotword_pad, |
|---|
