Update app.py

app.py

@@ -1,34 +1,41 @@
-import whisper
-import gradio as gr
-import time
-
-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()
-    result = whisper.decode(model, mel, options)
-    return result.text
-
-
-gr.Interface(
-    title = 'OpenAI-Whisper Audio to Text Web UI',
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(type="filepath")
-    ],
-    outputs=[
-        "textbox"
-    ],
-    live=True).launch()
+import whisper
+import gradio as gr
+import time
+
+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()
+    result = whisper.decode(model, mel, options)
+    return result.text
+
+
+# gr.Interface(
+#     title = 'OpenAI-Whisper Audio to Text Web UI',
+#     fn=transcribe,
+#     inputs=[
+#         gr.inputs.Audio(type="filepath")
+#     ],
+#     outputs=[
+#         "textbox"
+#     ],
+#     live=True).launch()
+
+gr.Interface(
+    fn=transcribe,
+    inputs=[gr.inputs.Audio(type="file")],  # Use "file" instead of "filepath"
+    outputs="text",
+    title='OpenAI-Whisper Audio to Text Web UI',
+    live=True).launch()