| | |
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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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| | | |
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| | | device = kwargs.get("device", "cuda") |
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| | | if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0: |
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| | | device = "cpu" |
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| | |
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| | | vocab_size = len(tokenizer.token_list) |
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| | | else: |
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| | | vocab_size = -1 |
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| | | |
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| | | # build frontend |
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| | | frontend = kwargs.get("frontend", None) |
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| | | |
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| | | if frontend is not None: |
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| | | frontend_class = tables.frontend_classes.get(frontend) |
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| | | frontend = frontend_class(**kwargs["frontend_conf"]) |
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| | | kwargs["frontend"] = frontend |
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| | | kwargs["input_size"] = frontend.output_size() |
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| | | |
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| | | |
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| | | # build model |
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| | | 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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| | | |
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| | | # init_param |
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| | |
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| | | kwargs.update(cfg) |
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| | | model = self.model if model is None else model |
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| | | model.eval() |
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| | | pdb.set_trace() |
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| | | |
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| | | batch_size = kwargs.get("batch_size", 1) |
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| | | # if kwargs.get("device", "cpu") == "cpu": |
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| | | # batch_size = 1 |
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| | | |
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| | | key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key) |
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| | | pdb.set_trace() |
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| | | |
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| | | speed_stats = {} |
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| | | asr_result_list = [] |
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| | |
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| | | pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None |
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| | | time_speech_total = 0.0 |
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| | | time_escape_total = 0.0 |
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| | | pdb.set_trace() |
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| | | for beg_idx in range(0, num_samples, batch_size): |
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| | | pdb.set_trace() |
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| | | end_idx = min(num_samples, beg_idx + batch_size) |
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| | | data_batch = data_list[beg_idx:end_idx] |
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| | | key_batch = key_list[beg_idx:end_idx] |
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| | | batch = {"data_in": data_batch, "key": key_batch} |
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| | | pdb.set_trace() |
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| | | |
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| | | if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank |
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| | | batch["data_in"] = data_batch[0] |
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| | | batch["data_lengths"] = input_len |
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| | | |
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| | | time1 = time.perf_counter() |
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| | | with torch.no_grad(): |
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| | | pdb.set_trace() |
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| | | results, meta_data = model.inference(**batch, **kwargs) |
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| | | 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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| | | |
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| | | # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item() |
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