| | |
|---|
| | | audio_sample_list = load_audio_text_image_video(data_in, fs=16000, audio_fs=kwargs.get("fs", 16000), data_type="sound") |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | meta_data["load_data"] = f"{time2 - time1:0.3f}" |
|---|
| | | speech, speech_lengths = extract_feature(audio_sample_list) |
|---|
| | | speech, speech_lengths, speech_times = extract_feature(audio_sample_list) |
|---|
| | | time3 = time.perf_counter() |
|---|
| | | meta_data["extract_feat"] = f"{time3 - time2:0.3f}" |
|---|
| | | meta_data["batch_data_time"] = np.array(speech_lengths).sum().item() / 16000.0 |
|---|
| | | # import pdb; pdb.set_trace() |
|---|
| | | results = [] |
|---|
| | | embeddings = self.forward(speech) |
|---|
| | | for embedding in embeddings: |
|---|
| | | results.append({"spk_embedding":embedding}) |
|---|
| | | meta_data["batch_data_time"] = np.array(speech_times).sum().item() / 16000.0 |
|---|
| | | results = [{"spk_embedding": self.forward(speech)}] |
|---|
| | | return results, meta_data |
|---|
