CosyVoice/runtime/triton_trtllm

Best Practices for Serving CosyVoice with NVIDIA Triton Inference Server

Thanks to the contribution from NVIDIA Yuekai Zhang.

Quick Start

Launch the service directly with Docker Compose:

docker compose up

Build the Docker Image

Build the image from scratch:

docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06

Run a Docker Container

your_mount_dir=/mnt:/mnt
docker run -it --name "cosyvoice-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-cosyvoice:25.06

Understanding run.sh

The run.sh script orchestrates the entire workflow through numbered stages.

Run a subset of stages with:

bash run.sh <start_stage> <stop_stage> [service_type]

• <start_stage> – stage to start from (0-5).
• <stop_stage> – stage to stop after (0-5).

Stages:

• Stage 0 – Download the cosyvoice-2 0.5B model from HuggingFace.
• Stage 1 – Convert the HuggingFace checkpoint to TensorRT-LLM format and build TensorRT engines.
• Stage 2 – Create the Triton model repository and configure the model files (adjusts depending on whether Decoupled=True/False will be used later).
• Stage 3 – Launch the Triton Inference Server.
• Stage 4 – Run the single-utterance HTTP client.
• Stage 5 – Run the gRPC benchmark client.

Export Models to TensorRT-LLM and Launch the Server

Inside the Docker container, prepare the models and start the Triton server by running stages 0-3:

# Runs stages 0, 1, 2, and 3
bash run.sh 0 3

Note: Stage 2 prepares the model repository differently depending on whether you intend to run with Decoupled=False or Decoupled=True. Rerun stage 2 if you switch the service type.

Single-Utterance HTTP Client

Send a single HTTP inference request:

bash run.sh 4 4

Benchmark with a Dataset

Benchmark the running Triton server. Pass either streaming or offline as the third argument.

bash run.sh 5 5

# You can also customise parameters such as num_task and dataset split directly:
# python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts_cosy2 --split-name test_zh --mode [streaming|offline]

[!TIP] Only offline CosyVoice TTS is currently supported. Setting the client to streaming simply enables NVIDIA Triton’s decoupled mode so that responses are returned as soon as they are ready.

Benchmark Results

Decoding on a single L20 GPU with 26 prompt_audio/target_text pairs (≈221 s of audio):

Mode	Note	Concurrency	Avg Latency (ms)	P50 Latency (ms)	RTF
Decoupled=False	Commit	1	758.04	615.79	0.0891
Decoupled=False	Commit	2	1025.93	901.68	0.0657
Decoupled=False	Commit	4	1914.13	1783.58	0.0610
Decoupled=True	Commit	1	659.87	655.63	0.0891
Decoupled=True	Commit	2	1103.16	992.96	0.0693
Decoupled=True	Commit	4	1790.91	1668.63	0.0604

OpenAI-Compatible Server

To launch an OpenAI-compatible service, run:

git clone https://github.com/yuekaizhang/Triton-OpenAI-Speech.git
pip install -r requirements.txt
# After the Triton service is up, start the FastAPI bridge:
python3 tts_server.py --url http://localhost:8000 --ref_audios_dir ./ref_audios/ --port 10086 --default_sample_rate 24000
# Test with curl
bash test/test_cosyvoice.sh

Acknowledgements

This section originates from the NVIDIA CISI project. We also provide other multimodal resources—see mair-hub for details.