| | |
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| | | from funasr.bin.vad_inference import Speech2VadSegment |
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| | | from funasr.bin.punctuation_infer import Text2Punc |
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| | | from funasr.utils.vad_utils import slice_padding_fbank |
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| | | from funasr.tasks.vad import VADTask |
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| | | from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard |
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| | | |
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| | | class Speech2Text: |
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| | | """Speech2Text class |
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| | |
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| | | text = self.tokenizer.tokens2text(token) |
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| | | else: |
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| | | text = None |
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| | | |
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| | | timestamp = [] |
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| | | if isinstance(self.asr_model, BiCifParaformer): |
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| | | _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:enc_len[i]*3], |
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| | | us_peaks[i][:enc_len[i]*3], |
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| | | copy.copy(token), |
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| | | vad_offset=begin_time) |
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| | | results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor)) |
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| | | else: |
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| | | results.append((text, token, token_int, hyp, [], enc_len_batch_total, lfr_factor)) |
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| | | results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor)) |
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| | | |
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| | | |
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| | | # assert check_return_type(results) |
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| | | return results |
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| | |
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| | | hotword_list_or_file = None |
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| | | if param_dict is not None: |
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| | | hotword_list_or_file = param_dict.get('hotword') |
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| | | if 'hotword' in kwargs: |
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| | | if 'hotword' in kwargs and kwargs['hotword'] is not None: |
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| | | hotword_list_or_file = kwargs['hotword'] |
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| | | if hotword_list_or_file is not None or 'hotword' in kwargs: |
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| | | speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file) |
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| | |
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| | | kwargs = vars(args) |
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| | | kwargs.pop("config", None) |
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| | | kwargs['param_dict'] = param_dict |
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| | | inference(**kwargs) |
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| | | inference_pipeline = inference_modelscope(**kwargs) |
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| | | return inference_pipeline(kwargs["data_path_and_name_and_type"], param_dict=param_dict) |
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| | | |
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| | | |
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| | | if __name__ == "__main__": |
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| | | main() |
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| | | |
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| | | # from modelscope.pipelines import pipeline |
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| | | # from modelscope.utils.constant import Tasks |
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| | | # |
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| | | # inference_16k_pipline = pipeline( |
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| | | # task=Tasks.auto_speech_recognition, |
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| | | # model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') |
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| | | # |
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| | | # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') |
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| | | # print(rec_result) |
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