Update app.py

app.py

@@ -9,30 +9,30 @@ model = whisper.load_model("small")
 
 
 #option 1
-# def transcribe(audio):
-#     model = whisper.load_model("base")
-#     result = model.transcribe(audio)
-#     return result["text"]
+def transcribe(audio):
+    model = whisper.load_model("base")
+    result = model.transcribe(audio)
+    return result["text"]
 
 #option 2
-def transcribe(audio):
+# 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
+#     #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
     
     
 def process_text(input_text):