FunASR/examples/openai_api/README.md

FunASR OpenAI-Compatible API Server

Drop-in replacement for OpenAI's /v1/audio/transcriptions endpoint. Works with any agent framework that supports OpenAI audio API.

Quick Start

pip install funasr fastapi uvicorn python-multipart
python server.py --model sensevoice --device cuda --port 8000

Server starts in ~20s (model loading). Health check: GET /health

Usage with OpenAI SDK (Python)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

# Basic transcription
result = client.audio.transcriptions.create(
    model="sensevoice",  # or "paraformer", "paraformer-en", "fun-asr-nano"
    file=open("meeting.wav", "rb"),
)
print(result.text)

# With timestamps/segments
result = client.audio.transcriptions.create(
    model="sensevoice",
    file=open("meeting.wav", "rb"),
    response_format="verbose_json",
)
# Returns: text, segments (with start/end/speaker), duration

Usage with curl

curl http://localhost:8000/v1/audio/transcriptions \
  -F file=@audio.wav \
  -F model=sensevoice

# With verbose output
curl http://localhost:8000/v1/audio/transcriptions \
  -F file=@audio.wav \
  -F model=sensevoice \
  -F response_format=verbose_json

Available Models

Model	Speed (GPU)	Speed (CPU)	Languages	Features
sensevoice	170x realtime	17x realtime	zh/en/ja/ko/yue	Emotion detection
paraformer	120x realtime	15x realtime	zh/en	Punctuation
paraformer-en	120x realtime	15x realtime	en	English only
fun-asr-nano	17x realtime	3.6x realtime	31 languages	LLM-based, timestamps

API Endpoints

Endpoint	Method	Description
/v1/audio/transcriptions	POST	Transcribe audio (OpenAI-compatible)
/v1/models	GET	List available models
/health	GET	Health check + loaded models
/docs	GET	Interactive API documentation (Swagger)

Agent Framework Integration

Works with: LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, Dify, or any framework using OpenAI audio API.

LangChain Example

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")

def transcribe_for_agent(audio_path: str) -> str:
    """Tool function for LangChain agent."""
    result = client.audio.transcriptions.create(
        model="sensevoice", file=open(audio_path, "rb")
    )
    return result.text

Docker Deployment

# Build
docker build -t funasr-api .

# Run with GPU
docker run --gpus all -p 8000:8000 funasr-api

Configuration

Arg	Default	Description
--host	0.0.0.0	Bind address
--port	8000	Port
--device	cuda	Device (cuda/cpu/mps)
--model	sensevoice	Pre-load model at startup