| # import whisper | |
| # import gradio as gr | |
| # model = whisper.load_model("small") | |
| # def transcribe(audio): | |
| # #time.sleep(3) | |
| # # 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) | |
| # print(f"Detected language: {max(probs, key=probs.get)}") | |
| # # decode the audio | |
| # options = whisper.DecodingOptions(fp16 = False) | |
| # result = whisper.decode(model, mel, options) | |
| # return result.text | |
| # gr.Interface( | |
| # title = 'Product Recommendation System Text', | |
| # fn=transcribe, | |
| # inputs=[ | |
| # gr.inputs.Audio(source="microphone", type="filepath") | |
| # ], | |
| # outputs=[ | |
| # "textbox" | |
| # ], | |
| # live=True).launch() | |
| import whisper | |
| import gradio as gr | |
| model = whisper.load_model("small") | |
| def transcribe(audio): | |
| # Load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| # Convert to log-Mel spectrogram | |
| mel = whisper.log_mel_spectrogram(audio).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(fp16=False) | |
| result = whisper.decode(model, mel, options) | |
| return result.text | |
| # Update Gradio interface for the new version | |
| gr.Interface( | |
| title='Product Recommendation System Text', | |
| fn=transcribe, | |
| inputs=gr.Audio(type="filepath"), # Removed 'source' argument | |
| outputs=gr.Textbox(), | |
| live=True | |
| ).launch() | |