Add gradio app

app.py

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+import gradio as gr
+from faster_whisper import WhisperModel
+
+# Model choice: use smaller models for speed on M1 Pro (try "tiny" or "small")
+MODEL_SIZE = "small"
+
+# compute_type="int8" uses quantized weights for faster CPU inference
+model = WhisperModel(MODEL_SIZE, device="cpu", compute_type="int8")
+
+def transcribe(audio_path):
+    if not audio_path:
+        return ""
+    segments, info = model.transcribe(audio_path, beam_size=1, vad_filter=True)
+    text = "".join([seg.text for seg in segments])
+    return text
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="upload", type="filepath", label="Upload audio file"),
+    outputs=gr.Textbox(label="Transcription"),
+    title="Fast Local Transcription",
+    description=("faster-whisper backend; pick model_size=" + MODEL_SIZE +
+                 " for a balance of speed/accuracy."),
+    allow_flagging=False,
+)
+
+# Expose the Gradio app variable so Spaces can detect API endpoints
+gradio_app = iface
+
+# Enable the request queue so the Space exposes queue-based API endpoints
+try:
+    gradio_app = gradio_app.queue()
+except Exception:
+    # older gradio versions may not support queue(); ignore if unavailable
+    pass
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", share=False)

requirements.txt

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+gradio==6.12.0
+faster-whisper>=0.6
+
+# Notes: faster-whisper may require additional system dependencies (ffmpeg).
+# On macOS (Homebrew): brew install ffmpeg