| | |
|---|
| | | if len(speech_lengths.size()) > 1: |
|---|
| | | speech_lengths = speech_lengths[:, 0] |
|---|
| | | |
|---|
| | | batch_size = speech.shape[0] |
|---|
| | | batch_size, frames, _ = speech.shape |
|---|
| | | |
|---|
| | | # audio encoder |
|---|
| | | encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths) |
|---|
| | |
|---|
| | | stats["acc"] = acc_att |
|---|
| | | |
|---|
| | | stats["loss"] = torch.clone(loss.detach()) |
|---|
| | | stats["batch_size"] = batch_size |
|---|
| | | stats["batch_size_x_frames"] = frames * batch_size |
|---|
| | | stats["batch_size_real_frames"] = speech_lengths.sum().item() |
|---|
| | | stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"] |
|---|
| | | stats["batch_size_x_tokens"] = token_num * batch_size |
|---|
| | | stats["batch_size_real_tokens"] = attention_mask.sum().item() |
|---|
| | | stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"] |
|---|
| | | |
|---|
| | | # force_gatherable: to-device and to-tensor if scalar for DataParallel |
|---|
| | | if self.length_normalized_loss: |
|---|
