| | |
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| | | from funasr.models.campplus.cluster_backend import ClusterBackend |
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| | | except: |
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| | | print("If you want to use the speaker diarization, please `pip install hdbscan`") |
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| | | |
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| | | import pdb |
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| | | |
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| | | def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None): |
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| | | """ |
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| | |
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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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| | | |
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| | | |
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| | | speed_stats = {} |
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| | | asr_result_list = [] |
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| | | num_samples = len(data_list) |
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| | |
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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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| | | |
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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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