2.8 KiB
2.8 KiB
Steps:
- Prepare model repo files
- git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online-onnx.git
- Convert lfr_cmvn_pe.onnx model. For example: python export_lfr_cmvn_pe_onnx.py
- If you export to onnx, you should have several model files in
${MODEL_DIR}:
├── README.md
└── model_repo_paraformer_large_online
├── cif_search
│ ├── 1
│ │ └── model.py
│ └── config.pbtxt
├── decoder
│ ├── 1
│ │ └── decoder.onnx
│ └── config.pbtxt
├── encoder
│ ├── 1
│ │ └── model.onnx
│ └── config.pbtxt
├── feature_extractor
│ ├── 1
│ │ └── model.py
│ ├── config.pbtxt
│ └── config.yaml
├── lfr_cmvn_pe
│ ├── 1
│ │ └── lfr_cmvn_pe.onnx
│ ├── am.mvn
│ ├── config.pbtxt
│ └── export_lfr_cmvn_pe_onnx.py
└── streaming_paraformer
├── 1
└── config.pbtxt
- Follow below instructions to launch triton server
# using docker image Dockerfile/Dockerfile.server
docker build . -f Dockerfile/Dockerfile.server -t triton-paraformer:23.01
docker run -it --rm --name "paraformer_triton_server" --gpus all -v <path_host/model_repo_paraformer_large_online>:/workspace/ --shm-size 1g --net host triton-paraformer:23.01
# launch the service
cd /workspace
tritonserver --model-repository model_repo_paraformer_large_online \
--pinned-memory-pool-byte-size=512000000 \
--cuda-memory-pool-byte-size=0:1024000000
Performance benchmark with a single A10
- FP32, onnx, paraformer larger online,Our chunksize is 10 * 960 / 16000 = 0.6 s, so we should care about the perf of latency less than 0.6s so that it can be a realtime application.
| Concurrency | Throughput | Latency_p50 (ms) | Latency_p90 (ms) | Latency_p95 (ms) | Latency_p99 (ms) |
|---|---|---|---|---|---|
| 20 | 309.252 | 56.913 | 76.267 | 85.598 | 138.462 |
| 40 | 391.058 | 97.911 | 145.509 | 150.545 | 185.399 |
| 60 | 426.269 | 138.244 | 185.855 | 201.016 | 236.528 |
| 80 | 431.781 | 170.991 | 227.983 | 252.453 | 412.273 |
| 100 | 473.351 | 206.205 | 262.612 | 288.964 | 463.337 |