.

app.py

@@ -5,9 +5,24 @@ import whisper
 
 def speech_to_text(tmp_filename, model_size):
     model = whisper.load_model(model_size)
-    result = model.transcribe(tmp_filename)
 
-    return result["text"]
+    # load audio and pad/trim it to fit 30 seconds
+    audio = whisper.load_audio("audio.mp3")
+    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()
+    result = whisper.decode(model, mel, options)
+
+    # print the recognized text
+    print(result.text)
 
 
 gr.Interface(