Create app.py

app.py

@@ -0,0 +1,55 @@
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from transformers import MoonshineForConditionalGeneration, AutoProcessor
+import torch
+import librosa
+import io
+import os
+
+app = FastAPI()
+
+# Check for GPU availability
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Load the model and processor
+try:
+    model = MoonshineForConditionalGeneration.from_pretrained('UsefulSensors/moonshine-tiny').to(device).to(torch_dtype)
+    processor = AutoProcessor.from_pretrained('UsefulSensors/moonshine-tiny')
+except Exception as e:
+    print(f"Error loading model or processor: {e}")
+    exit()
+
+@app.post("/transcribe/")
+async def transcribe_audio(file: UploadFile = File(...)):
+    """
+    Transcribes an uploaded audio file.
+    """
+    if not file.filename.lower().endswith(('.mp3', '.wav', '.ogg', '.flac', '.m4a')): #add more formats as needed
+        raise HTTPException(status_code=400, detail="Invalid file format. Supported formats: .mp3, .wav, .ogg, .flac, .m4a")
+
+    try:
+        audio_bytes = await file.read()
+        audio_array, sampling_rate = librosa.load(io.BytesIO(audio_bytes), sr=processor.feature_extractor.sampling_rate)
+
+        inputs = processor(
+            audio_array,
+            return_tensors="pt",
+            sampling_rate=processor.feature_extractor.sampling_rate
+        )
+        inputs = inputs.to(device, torch_dtype)
+
+        token_limit_factor = 6.5 / processor.feature_extractor.sampling_rate
+        seq_lens = inputs.attention_mask.sum(dim=-1)
+        max_length = int((seq_lens * token_limit_factor).max().item())
+
+        generated_ids = model.generate(**inputs, max_length=max_length)
+        transcription = processor.decode(generated_ids[0], skip_special_tokens=True)
+
+        return {"transcription": transcription}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing audio: {e}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)