| | |
|---|
| | | ind: int |
|---|
| | | """ |
|---|
| | | with autocast(False): |
|---|
| | | pdb.set_trace() |
|---|
| | | # Data augmentation |
|---|
| | | if self.specaug is not None and self.training: |
|---|
| | | speech, speech_lengths = self.specaug(speech, speech_lengths) |
|---|
| | | pdb.set_trace() |
|---|
| | | # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN |
|---|
| | | if self.normalize is not None: |
|---|
| | | speech, speech_lengths = self.normalize(speech, speech_lengths) |
|---|
| | | pdb.set_trace() |
|---|
| | | # Forward encoder |
|---|
| | | # feats: (Batch, Length, Dim) |
|---|
| | | # -> encoder_out: (Batch, Length2, Dim2) |
|---|
| | |
|---|
| | | else: |
|---|
| | | # extract fbank feats |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | pdb.set_trace() |
|---|
| | | sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000), |
|---|
| | | data_type=kwargs.get("data_type", "sound"), |
|---|
| | | tokenizer=tokenizer) |
|---|
| | |
|---|
| | | meta_data["load_data"] = f"{time2 - time1:0.3f}" |
|---|
| | | audio_sample_list = sample_list[0] |
|---|
| | | ocr_sample_list = sample_list[1] |
|---|
| | | pdb.set_trace() |
|---|
| | | speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), |
|---|
| | | frontend=frontend) |
|---|
| | | pdb.set_trace() |
|---|
| | | time3 = time.perf_counter() |
|---|
| | | meta_data["extract_feat"] = f"{time3 - time2:0.3f}" |
|---|
| | | meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000 |
|---|
| | | frame_shift = 10 |
|---|
| | | meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift / 1000 |
|---|
| | | |
|---|
| | | speech = speech.to(device=kwargs["device"]) |
|---|
| | | speech_lengths = speech_lengths.to(device=kwargs["device"]) |
|---|
