Initial commit: qwen3-asr-api

Pure OpenAI-compatible STT wrapper for Qwen3-ASR.
Adds entrypoint to official qwenllm/qwen3-asr image for auto-start,
env-based model switching, and Unraid template.

Author: aws-test-ethan

.dockerignore

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+.git/
+.github/
+.gitignore
+*.md
+templates/

.github/workflows/docker-publish.yml

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+name: Build and Publish Docker Image
+
+on:
+  push:
+    branches: [main]
+    tags: ["v*"]
+  pull_request:
+    branches: [main]
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GHCR
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=semver,pattern={{version}}
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+
+      - name: Build and push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: Dockerfile
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

.gitignore

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+models/
+.env

Dockerfile

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+FROM qwenllm/qwen3-asr:latest
+
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+ENV MODEL_ID=Qwen/Qwen3-ASR-0.6B \
+    HOST=0.0.0.0 \
+    PORT=80 \
+    GPU_MEMORY_UTILIZATION=0.8
+
+EXPOSE 80
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
+    CMD curl -sf http://localhost:${PORT}/health || exit 1
+
+ENTRYPOINT ["/entrypoint.sh"]

LICENSE

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README.md

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+# qwen3-asr-api
+
+Pure OpenAI-compatible Speech-to-Text API powered by [Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR).
+
+No extra services, no NGINX, no voiceprint database — just the model served via vLLM with an OpenAI-compatible endpoint.
+
+## What this adds
+
+The official `qwenllm/qwen3-asr` Docker image has no entrypoint (drops to interactive shell). This wrapper adds:
+- Auto-start `qwen-asr-serve` on container launch
+- Environment variable for model switching (no rebuild needed)
+- GPU memory control via env var
+- Unraid Community Applications template
+
+## Quick Start
+
+```bash
+docker run -d --gpus all --shm-size=4g \
+  -p 8000:80 \
+  -v /path/to/models:/root/.cache/huggingface \
+  -e MODEL_ID=Qwen/Qwen3-ASR-0.6B \
+  ghcr.io/hsiang-han/qwen3-asr-api:latest
+```
+
+First start downloads the model (~1-3GB depending on variant).
+
+## Usage (OpenAI-compatible)
+
+```bash
+curl -X POST http://localhost:8000/v1/audio/transcriptions \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@audio.wav" \
+  -F "model=qwen3-asr"
+```
+
+Or with OpenAI SDK:
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
+result = client.audio.transcriptions.create(
+    model="qwen3-asr",
+    file=open("audio.wav", "rb")
+)
+print(result.text)
+```
+
+## Model Options
+
+| Model | VRAM | Speed | Best for |
+|-------|------|-------|----------|
+| `Qwen/Qwen3-ASR-0.6B` | ~2-3GB | RTFx 166 | Low latency, shared GPU |
+| `Qwen/Qwen3-ASR-1.7B` | ~4-6GB | RTFx 148 | Best accuracy |
+
+Switch by changing `MODEL_ID` env var and restarting container.
+
+## Unraid Install
+
+1. Add template repo: `https://github.com/hsiang-han/unraid_templates`
+2. Find "qwen3-asr-api" in Community Applications
+3. Configure MODEL_ID and GPU settings
+4. Start — first launch downloads model, subsequent starts are fast
+
+## Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MODEL_ID` | `Qwen/Qwen3-ASR-0.6B` | Model to serve |
+| `GPU_MEMORY_UTILIZATION` | `0.8` | GPU memory fraction (0.0-1.0) |
+| `HOST` | `0.0.0.0` | Bind address |
+| `PORT` | `80` | Container port |
+
+## License
+
+Apache-2.0 (same as upstream Qwen3-ASR)

docker-compose.yml

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+name: qwen3-asr-api
+services:
+  qwen3-asr-api:
+    image: ghcr.io/hsiang-han/qwen3-asr-api:latest
+    container_name: qwen3-asr-api
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "8000:80"
+    volumes:
+      - /mnt/user/appdata/qwen3-asr-api/models:/root/.cache/huggingface
+    environment:
+      - MODEL_ID=Qwen/Qwen3-ASR-0.6B
+      - GPU_MEMORY_UTILIZATION=0.8
+    shm_size: "4gb"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    restart: unless-stopped

entrypoint.sh

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+#!/bin/bash
+set -e
+
+echo "=== qwen3-asr-api ==="
+echo "Model: ${MODEL_ID}"
+echo "Port:  ${PORT}"
+echo "GPU Memory: ${GPU_MEMORY_UTILIZATION}"
+echo "====================="
+
+exec qwen-asr-serve "${MODEL_ID}" \
+    --host "${HOST}" \
+    --port "${PORT}" \
+    --gpu-memory-utilization "${GPU_MEMORY_UTILIZATION}"