| import whisper | |
| def whisper_(input_audio): | |
| model = whisper.load_model("medium") | |
| # load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(input_audio) | |
| audio = whisper.pad_or_trim(audio) | |
| # make log-Mel spectrogram and move to the same device as the model | |
| mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device) | |
| # detect the spoken language | |
| _, probs = model.detect_language(mel) | |
| print(f"Detected language: {max(probs, key=probs.get)}") | |
| # decode the audio | |
| options = whisper.DecodingOptions() | |
| result = whisper.decode(model, mel, options) | |
| # print the recognized text | |
| text_result = result.text | |
| return text_result | |