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| import aiofiles | |
| import nemo | |
| import nemo.collections.asr as nemo_asr | |
| import pyaudioconvert as pac | |
| import soundfile as sf | |
| from pydub import AudioSegment | |
| def transcribe(file, modelName="stt_rw_conformer_transducer_large"): | |
| # with open(file.name, 'wb') as out_file: | |
| # #out_file = file.read() # async read | |
| # content = file.read() | |
| # out_file.write(content) | |
| #out_file.write(content) # async write | |
| #print(out_file.name) | |
| asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained( | |
| model_name="stt_rw_conformer_ctc_large") | |
| #nemo_asr.models.EncDecRNNTBPEModel.from_pretrained( | |
| #model_name=modelName) | |
| # if not file.name.endswith("wav"): | |
| # sound = AudioSegment.from_mp3(out_file.name) | |
| # sound.export(out_file.name, format="wav") | |
| # files = [out_file.name] | |
| # pac.convert_wav_to_16bit_mono(out_file.name,out_file.name) | |
| # print("file loaded is **************",file.file) | |
| # for fname, transcription in zip(files, asr_model.transcribe(paths2audio_files=files)): | |
| # print(f"Audio in {fname} was recognized as: {transcription}") | |
| # print(transcription[0]) | |
| sound = AudioSegment.from_file(file.name) | |
| sound.export("test.wav",format='wav') | |
| pac.convert_wav_to_16bit_mono("Test2.wav","Test2.wav") | |
| transcription= asr_model.transcribe(["Test2.wav"]) | |
| return transcription[0] |