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)) # 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)
