Create app.py

app.py

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+import gradio as gr
+import whisper
+
+# Initialize the Whisper model
+model = whisper.load_model("large")
+
+def transcribe(audio_file):
+    audio = whisper.load_audio(audio_file.name)
+    audio = whisper.pad_or_trim(audio)
+    # Generate a mel spectrogram
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    # Options for decoding the spectrogram
+    options = whisper.DecodingOptions()
+    # Perform the transcription
+    result = whisper.decode(model, mel, options)
+    return result.text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.inputs.Audio(source="upload", type="file", label="Upload your audio file"),
+    outputs="text",
+    title="Whisper ASR",
+    description="Upload an audio file and it will be transcribed using OpenAI's Whisper model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()