paraformer vad punc

游雁

2023-05-11 475064f91451aad868ca33f36c7d52f41b0b8b40

paraformer vad punc

 egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py

@@ -4,8 +4,8 @@
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
    vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    batch_size=64,
)
audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
rec_result = inference_pipeline(audio_in=audio_in)
print(rec_result)

 egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py

@@ -10,7 +10,7 @@
        vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
        punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
        output_dir=output_dir,
        batch_size=8,
        batch_size=64,
    )
    rec_result = inference_pipeline(audio_in=audio_in)
    print(rec_result)

 funasr/bin/asr_inference_launch.py

@@ -291,11 +291,11 @@
    elif mode == "paraformer":
        from funasr.bin.asr_inference_paraformer import inference_modelscope
        inference_pipeline = inference_modelscope(**kwargs)
        return inference_pipeline(kwargs["data_path_and_name_and_type"])
        return inference_pipeline(kwargs["data_path_and_name_and_type"], hotword=kwargs.get("hotword", None))
    elif mode.startswith("paraformer_vad"):
        from funasr.bin.asr_inference_paraformer import inference_modelscope_vad_punc
        inference_pipeline = inference_modelscope_vad_punc(**kwargs)
        return inference_pipeline(kwargs["data_path_and_name_and_type"])
        return inference_pipeline(kwargs["data_path_and_name_and_type"], hotword=kwargs.get("hotword", None))
    elif mode == "mfcca":
        from funasr.bin.asr_inference_mfcca import inference_modelscope
        return inference_modelscope(**kwargs)

 funasr/bin/asr_inference_paraformer.py

@@ -48,6 +48,8 @@
from funasr.bin.vad_inference import Speech2VadSegment
from funasr.bin.punctuation_infer import Text2Punc
from funasr.utils.vad_utils import slice_padding_fbank
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_sentence, ts_prediction_lfr6_standard

class Speech2Text:
    """Speech2Text class
@@ -293,15 +295,14 @@
                    text = self.tokenizer.tokens2text(token)
                else:
                    text = None

                timestamp = []
                if isinstance(self.asr_model, BiCifParaformer):
                    _, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:enc_len[i]*3], 
                                                            us_peaks[i][:enc_len[i]*3], 
                                                            copy.copy(token), 
                                                            vad_offset=begin_time)
                    results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    results.append((text, token, token_int, hyp, [], enc_len_batch_total, lfr_factor))
                results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))


        # assert check_return_type(results)
        return results
@@ -471,7 +472,7 @@
        hotword_list_or_file = None
        if param_dict is not None:
            hotword_list_or_file = param_dict.get('hotword')
        if 'hotword' in kwargs:
        if 'hotword' in kwargs and kwargs['hotword'] is not None:
            hotword_list_or_file = kwargs['hotword']
        if hotword_list_or_file is not None or 'hotword' in kwargs:
            speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
@@ -1018,18 +1019,9 @@
    kwargs = vars(args)
    kwargs.pop("config", None)
    kwargs['param_dict'] = param_dict
    inference(**kwargs)
    inference_pipeline = inference_modelscope(**kwargs)
    return inference_pipeline(kwargs["data_path_and_name_and_type"], param_dict=param_dict)


if __name__ == "__main__":
    main()

    # from modelscope.pipelines import pipeline
    # from modelscope.utils.constant import Tasks
    #
    # inference_16k_pipline = pipeline(
    #     task=Tasks.auto_speech_recognition,
    #     model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
    #
    # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
    # print(rec_result)