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| import whisper | |
| import gradio as gr | |
| model = whisper.load_model("medium") | |
| def transcribe(audio): | |
| # load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(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).to(model.device) | |
| # detect the spoken language | |
| _, probs = model.detect_language(mel) | |
| detected_language = max(probs, key=probs.get) | |
| task = 'transcribe' if detected_language == 'en' else 'translate' | |
| print(f"Detected language: {detected_language}") | |
| # decode the audio | |
| options = whisper.DecodingOptions(task = task, fp16 = False, language=detected_language) | |
| result = whisper.decode(model, mel, options) | |
| return result.text | |
| gr.Interface( | |
| title = 'Whisper ASR With Auto Punctuation and Auto Translation Into En', | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath") | |
| ], | |
| outputs=[ | |
| "textbox" | |
| ] | |
| ).launch() |