Create app.py

app.py

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+import gradio as gr
+from openai_whisper import whisper
+
+# Load the Whisper model
+model = whisper.load_model("base")
+
+def transcribe(audio_file):
+    # Process the audio file
+    audio = whisper.load_audio(audio_file.name)
+    audio = whisper.pad_or_trim(audio)
+
+    # Make predictions
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    options = whisper.DecodingOptions(fp16=False)
+    result = whisper.decode(model, mel, options)
+
+    # Return the transcription
+    return result.text
+
+# Create the Gradio interface
+iface = gr.Interface(fn=transcribe,
+                     inputs=gr.Audio(source="upload", type="filepath"),
+                     outputs="text",
+                     title="Whisper Transcription",
+                     description="Upload an audio file to transcribe it using OpenAI's Whisper model.")
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()