add api

Dockerfile

@@ -0,0 +1,29 @@
+# Use a lightweight Python image
+FROM python:3.9-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git ffmpeg wget && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Clone NeMo from the specific branch and install it
+RUN git clone https://github.com/AI4Bharat/NeMo.git && \
+    cd NeMo && \
+    git checkout nemo-v2 && \
+    bash reinstall.sh
+
+# Copy all code to the working directory
+COPY . .
+
+# Expose the required port
+EXPOSE 7860
+
+# Run the FastAPI app with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,53 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+import nemo.collections.asr as nemo_asr
+import torch
+import shutil
+import os
+import uvicorn
+
+app = FastAPI()
+
+# Set the device (CPU or CUDA if available)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load and configure the ASR model
+model = nemo_asr.models.ASRModel.from_pretrained(
+    "ai4bharat/indicconformer_stt_hi_hybrid_rnnt_large"
+)
+model.freeze()  # Set to inference mode
+model = model.to(device)
+model.cur_decoder = "rnnt"  # Use RNNT decoder
+
+UPLOAD_FOLDER = "./uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)  # Create upload folder if it doesn't exist
+
+
+@app.post("/transcribe/")
+async def transcribe_audio(file: UploadFile = File(...), source_lang: str = "hi"):
+    try:
+        # Save the uploaded audio file to disk
+        file_path = os.path.join(UPLOAD_FOLDER, file.filename)
+        with open(file_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Perform transcription using the provided language ID
+        transcription = model.transcribe(
+            [file_path], batch_size=1, language_id=source_lang
+        )[0]
+
+        # Cleanup the uploaded file
+        os.remove(file_path)
+
+        return {"transcription": transcription}
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"Error during transcription: {str(e)}"
+        )
+
+
+# Run the app if inside a container
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,4 @@
+fastapi
+uvicorn
+torch
+ffmpeg-python