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| ''' | |
| Reference: https://github.com/alibaba-damo-academy/FunASR | |
| pip install funasr | |
| pip install modelscope | |
| pip install -U rotary_embedding_torch | |
| ''' | |
| try: | |
| from funasr import AutoModel | |
| except: | |
| print("如果想使用FunASR,请先安装funasr,若使用Whisper,请忽略此条信息") | |
| import sys | |
| sys.path.append('..') | |
| from src.cost_time import calculate_time | |
| class FunASR: | |
| def __init__(self) -> None: | |
| self.model = AutoModel(model="paraformer-zh", model_revision="v2.0.4", | |
| vad_model="fsmn-vad", vad_model_revision="v2.0.4", | |
| punc_model="ct-punc-c", punc_model_revision="v2.0.4", | |
| # spk_model="cam++", spk_model_revision="v2.0.2", | |
| ) | |
| def transcribe(self, audio_file): | |
| res = self.model.generate(input=audio_file, | |
| batch_size_s=300) | |
| print(res) | |
| return res[0]['text'] | |
| if __name__ == "__main__": | |
| import os | |
| # 创建ASR对象并进行语音识别 | |
| audio_file = "output.wav" # 音频文件路径 | |
| if not os.path.exists(audio_file): | |
| os.system('edge-tts --text "hello" --write-media output.wav') | |
| asr = FunASR() | |
| print(asr.transcribe(audio_file)) |