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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | from funasr.utils.vad_utils import slice_padding_audio_samples |
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| | | from funasr.utils.timestamp_tools import time_stamp_sentence |
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| | | from funasr.download.file import download_from_url |
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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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| | | filelist = [".scp", ".txt", ".json", ".jsonl"] |
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| | | |
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| | | chars = string.ascii_letters + string.digits |
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| | | |
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| | | if isinstance(data_in, str) and data_in.startswith('http'): # url |
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| | | data_in = download_from_url(data_in) |
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| | | if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt; |
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| | | _, file_extension = os.path.splitext(data_in) |
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| | | file_extension = file_extension.lower() |
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| | |
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| | | data_list = [data_in] |
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| | | key_list = [key] |
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| | | elif isinstance(data_in, (list, tuple)): |
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| | | if data_type is not None and isinstance(data_type, (list, tuple)): |
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| | | if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs |
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| | | data_list_tmp = [] |
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| | | for data_in_i, data_type_i in zip(data_in, data_type): |
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| | | key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i) |
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| | |
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| | | for item in zip(*data_list_tmp): |
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| | | data_list.append(item) |
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| | | else: |
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| | | # [audio sample point, fbank] |
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| | | # [audio sample point, fbank, text] |
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| | | data_list = data_in |
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| | | key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))] |
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| | | else: # raw text; audio sample point, fbank; bytes |
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| | |
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| | | kwargs = self.kwargs if kwargs is None else kwargs |
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| | | kwargs.update(cfg) |
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| | | model = self.model if model is None else model |
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| | | |
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| | | data_type = kwargs.get("data_type", "sound") |
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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=data_type, key=key) |
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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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| | | speed_stats = {} |
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| | | asr_result_list = [] |
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| | | batch_size = int(kwargs.get("batch_size_s", 300))*1000 |
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| | | batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000 |
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| | | kwargs["batch_size"] = batch_size |
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| | | data_type = kwargs.get("data_type", "sound") |
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| | | key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type) |
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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)) |
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| | | results_ret_list = [] |
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| | | time_speech_total_all_samples = 0.0 |
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