| | |
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| | | kwargs = download_model(**kwargs) |
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| | | |
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| | | set_all_random_seed(kwargs.get("seed", 0)) |
|---|
| | | |
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| | | |
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| | | device = kwargs.get("device", "cuda") |
|---|
| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0: |
|---|
| | | device = "cpu" |
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| | |
|---|
| | | # build model |
|---|
| | | model_class = tables.model_classes.get(kwargs["model"]) |
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| | | model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size) |
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| | | |
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| | | model.to(device) |
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| | | |
|---|
| | | # init_param |
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| | |
|---|
| | | |
|---|
| | | time1 = time.perf_counter() |
|---|
| | | with torch.no_grad(): |
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| | | pdb.set_trace() |
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| | | results, meta_data = model.inference(**batch, **kwargs) |
|---|
| | | time2 = time.perf_counter() |
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| | | |
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| | | pdb.set_trace() |
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| | | asr_result_list.extend(results) |
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| | | |
|---|
| | | # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item() |
|---|
