makaveli10
68 lines
2.4 KiB
Markdown
68 lines
2.4 KiB
Markdown
# Whisper-TensorRT
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We have only tested the TensorRT backend in docker so, we recommend docker for a smooth TensorRT backend setup.
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**Note**: We use [our fork to setup TensorRT](https://github.com/makaveli10/TensorRT-LLM)
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## Installation
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- Install [docker](https://docs.docker.com/engine/install/)
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- Install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
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- Clone this repo.
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```bash
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git clone https://github.com/collabora/WhisperLive.git
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cd WhisperLive
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```
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- Pull the TensorRT-LLM docker image which we prebuilt for WhisperLive TensorRT backend.
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```bash
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docker pull ghcr.io/collabora/whisperbot-base:latest
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```
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- Next, we run the docker image and mount WhisperLive repo to the containers `/home` directory.
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```bash
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docker run -it --gpus all --shm-size=8g \
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--ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
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-p 9090:9090 -v /path/to/WhisperLive:/home/WhisperLive \
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ghcr.io/collabora/whisperbot-base:latest
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```
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- Make sure to test the installation.
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```bash
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# export ENV=${ENV:-/etc/shinit_v2}
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# source $ENV
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python -c "import torch; import tensorrt; import tensorrt_llm"
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```
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**NOTE**: Uncomment and update library paths if imports fail.
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## Whisper TensorRT Engine
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- We build `small.en` and `small` multilingual TensorRT engine. The script logs the path of the directory with Whisper TensorRT engine. We need the model_path to run the server.
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```bash
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# convert small.en
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bash scripts/build_whisper_tensorrt.sh /root/TensorRT-LLM-examples small.en
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# convert small multilingual model
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bash scripts/build_whisper_tensorrt.sh /root/TensorRT-LLM-examples small
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```
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## Run WhisperLive Server with TensorRT Backend
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```bash
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cd /home/WhisperLive
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# Install requirements
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apt update && bash scripts/setup.sh
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pip install -r requirements/server.txt
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# Required to create mel spectogram
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wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
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# Run English only model
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python3 run_server.py --port 9090 \
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--backend tensorrt \
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--trt_model_path "path/to/whisper_trt/from/build/step"
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# Run Multilingual model
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python3 run_server.py --port 9090 \
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--backend tensorrt \
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--trt_model_path "path/to/whisper_trt/from/build/step" \
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--trt_multilingual
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```
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