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runtime/triton_trtllm/README.md
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## Nvidia Triton Inference Serving Best Practice for Spark TTS
## Best Practices for Serving CosyVoice with NVIDIA Triton Inference Server
### Quick Start
Directly launch the service using docker compose.
Launch the service directly with Docker Compose:
```sh
docker compose up
```
### Build Image
Build the docker image from scratch.
### Build the Docker Image
Build the image from scratch:
```sh
docker build . -f Dockerfile.server -t soar97/triton-spark-tts:25.02
docker build . -f Dockerfile.server -t soar97/triton-cosyvoice:25.06
```
### Create Docker Container
### Run a Docker Container
```sh
your_mount_dir=/mnt:/mnt
docker run -it --name "spark-tts-server" --gpus all --net host -v $your_mount_dir --shm-size=2g soar97/triton-spark-tts:25.02
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.
The `run.sh` script automates various steps using stages. You can run specific stages using:
Run a subset of stages with:
```sh
bash run.sh <start_stage> <stop_stage> [service_type]
```
- `<start_stage>`: The stage to begin execution from (0-5).
- `<stop_stage>`: The stage to end execution at (0-5).
- `[service_type]`: Optional, specifies the service type ('streaming' or 'offline', defaults may apply based on script logic). Required for stages 4 and 5.
- `<start_stage>` – stage to start from (0-5).
- `<stop_stage>` – stage to stop after (0-5).
Stages:
- **Stage 0**: Download Spark-TTS-0.5B model from HuggingFace.
- **Stage 1**: Convert HuggingFace checkpoint to TensorRT-LLM format and build TensorRT engines.
- **Stage 2**: Create the Triton model repository structure and configure model files (adjusts for streaming/offline).
- **Stage 3**: Launch the Triton Inference Server.
- **Stage 4**: Run the gRPC benchmark client.
- **Stage 5**: Run the single utterance client (gRPC for streaming, HTTP for offline).
- **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 Server
Inside the docker container, you can prepare the models and launch the Triton server by running stages 0 through 3. This involves downloading the original model, converting it to TensorRT-LLM format, building the optimized TensorRT engines, creating the necessary model repository structure for Triton, and finally starting the server.
### 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:
```sh
# This runs stages 0, 1, 2, and 3
# Runs stages 0, 1, 2, and 3
bash run.sh 0 3
```
*Note: Stage 2 prepares the model repository differently based on whether you intend to run streaming or offline inference later. You might need to re-run stage 2 if switching service types.*
*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 Client
Run a single inference request. Specify `streaming` or `offline` as the third argument.
**Streaming Mode (gRPC):**
### Single-Utterance HTTP Client
Send a single HTTP inference request:
```sh
bash run.sh 5 5 streaming
```
This executes the `client_grpc.py` script with predefined example text and prompt audio in streaming mode.
**Offline Mode (HTTP):**
```sh
bash run.sh 5 5 offline
bash run.sh 4 4
```
### Benchmark using Dataset
Run the benchmark client against the running Triton server. Specify `streaming` or `offline` as the third argument.
### Benchmark with a Dataset
Benchmark the running Triton server. Pass either `streaming` or `offline` as the third argument.
```sh
# Run benchmark in streaming mode
bash run.sh 4 4 streaming
bash run.sh 5 5
# Run benchmark in offline mode
bash run.sh 4 4 offline
# You can also customize parameters like num_task directly in client_grpc.py or via args if supported
# Example from run.sh (streaming):
# python3 client_grpc.py \
# --server-addr localhost \
# --model-name spark_tts \
# --num-tasks 2 \
# --mode streaming \
# --log-dir ./log_concurrent_tasks_2_streaming_new
# Example customizing dataset (requires modifying client_grpc.py or adding args):
# python3 client_grpc.py --num-tasks 2 --huggingface-dataset yuekai/seed_tts --split-name wenetspeech4tts --mode [streaming|offline]
# 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, using 26 different prompt_audio/target_text [pairs](https://huggingface.co/datasets/yuekai/seed_tts), total audio duration 169 secs.
Decoding on a single L20 GPU with 26 prompt_audio/target_text [pairs](https://huggingface.co/datasets/yuekai/seed_tts) (≈221 s of audio):
| Mode | Note | Concurrency | Avg Latency (ms) | P50 Latency (ms) | RTF |
|------|------|-------------|------------------|------------------|-----|
| Decoupled=False | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 1 | 758.04 | 615.79 | 0.0891 |
| Decoupled=False | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 2 | 1025.93 | 901.68 | 0.0657 |
| Decoupled=False | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 4 | 1914.13 | 1783.58 | 0.0610 |
| Decoupled=True | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 1 | 659.87 | 655.63 | 0.0891 |
| Decoupled=True | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 2 | 1103.16 | 992.96 | 0.0693 |
| Decoupled=True | [Commit](https://github.com/SparkAudio/cosyvoice/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 4 | 1790.91 | 1668.63 | 0.0604 |
### OpenAI-Compatible Server
To launch an OpenAI-compatible service, run:
```sh
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](https://github.com/nvidia-china-sae/mair-hub) for details.
| Mode | Note | Concurrency | Avg Latency | First Chunk Latency (P50) | RTF |
|-------|-----------|-----------------------|---------|----------------|-|
| Offline | [Code Commit](https://github.com/SparkAudio/Spark-TTS/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 1 | 876.24 ms |-| 0.1362|
| Offline | [Code Commit](https://github.com/SparkAudio/Spark-TTS/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 2 | 920.97 ms |-|0.0737|
| Offline | [Code Commit](https://github.com/SparkAudio/Spark-TTS/tree/4d769ff782a868524f29e0be851ca64f8b22ebf1/runtime/triton_trtllm) | 4 | 1611.51 ms |-| 0.0704|
| Streaming | [Code Commit](https://github.com/yuekaizhang/Spark-TTS/commit/0e978a327f99aa49f0735f86eb09372f16410d86) | 1 | 913.28 ms |210.42 ms| 0.1501 |
| Streaming | [Code Commit](https://github.com/yuekaizhang/Spark-TTS/commit/0e978a327f99aa49f0735f86eb09372f16410d86) | 2 | 1009.23 ms |226.08 ms |0.0862 |
| Streaming | [Code Commit](https://github.com/yuekaizhang/Spark-TTS/commit/0e978a327f99aa49f0735f86eb09372f16410d86) | 4 | 1793.86 ms |1017.70 ms| 0.0824 |

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runtime/triton_trtllm/docker-compose.yml
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services:
tts:
image: soar97/triton-spark-tts:25.02
image: soar97/triton-cosyvoice:25.06
shm_size: '1gb'
ports:
- "8000:8000"
@@ -17,4 +17,4 @@ services:
device_ids: ['0']
capabilities: [gpu]
command: >
/bin/bash -c "rm -rf Spark-TTS && git clone https://github.com/SparkAudio/Spark-TTS.git && cd Spark-TTS/runtime/triton_trtllm && bash run.sh 0 3"
/bin/bash -c "pip install modelscope && cd /workspace && git clone https://github.com/FunAudioLLM/CosyVoice.git && cd CosyVoice && git submodule update --init --recursive && cd runtime/triton_trtllm && bash run.sh 0 3"