| | |
|---|
| | | |
|---|
| | | meta_data = {} |
|---|
| | | chunk_size = kwargs.get("chunk_size", [0, 10, 5]) |
|---|
| | | chunk_stride_samples = chunk_size[1] * 960 # 600ms |
|---|
| | | chunk_stride_samples = int(chunk_size[1] * 960) # 600ms |
|---|
| | | |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | cfg = {"is_final": kwargs.get("is_final", False)} |
|---|
| | |
|---|
| | | audio_fs=kwargs.get("fs", 16000), |
|---|
| | | data_type=kwargs.get("data_type", "sound"), |
|---|
| | | tokenizer=tokenizer, |
|---|
| | | **cfg, |
|---|
| | | cache=cfg, |
|---|
| | | ) |
|---|
| | | _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0])) |
|---|
| | | |
|---|
| | | n = len(audio_sample) // chunk_stride_samples + int(_is_final) |
|---|
| | | m = len(audio_sample) % chunk_stride_samples * (1-int(_is_final)) |
|---|
| | | n = int(len(audio_sample) // chunk_stride_samples + int(_is_final)) |
|---|
| | | m = int(len(audio_sample) % chunk_stride_samples * (1-int(_is_final))) |
|---|
| | | tokens = [] |
|---|
| | | for i in range(n): |
|---|
| | | kwargs["is_final"] = _is_final and i == n -1 |
|---|
