add tensorrt readme

TensorRT_whisper.md

@@ -1,5 +1,6 @@
 # Whisper-TensorRT
 We have only tested the TensorRT backend in docker so, we recommend docker for a smooth TensorRT backend setup.
+**Note**: We use [our fork to setup TensorRT](https://github.com/makaveli10/TensorRT-LLM)
 
 ## Installation
 - Install [docker](https://docs.docker.com/engine/install/)
@@ -16,49 +17,32 @@ docker build --file docker/Dockerfile.tensorrt --tag tensorrt_llm/devel:latest .
 **NOTE**: This could take some time.
 - Next, we run the docker image and mount WhisperLive repo to the containers `/home` directory.
 ```bash
-docker run -it --gpus all --shm-size=64g /path/to/WhisperLive:/home/WhisperLive tensorrt_llm/devel:latest
+docker run -it --gpus all --shm-size=8g \
+       --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
+       -v /path/to/WhisperLive:/home/WhisperLive \
+       tensorrt_llm/devel:latest
 ```
 - Once inside the docker container, make sure to test the installation. 
 ```bash
-export ENV=${ENV:-/etc/shinit_v2}
+# export ENV=${ENV:-/etc/shinit_v2} 
-source $ENV
+# source $ENV
 python -c "import torch; import tensorrt; import tensorrt_llm"
 ```
+**NOTE**: Uncomment and update library paths if imports fail.
 
 ## Whisper TensorRT Engine
-- Change working dir to the [whisper example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper) in TensorRT-LLM.
+- For this demo 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.
 ```bash
-cd /home/TensorRT-LLM/examples/whisper
-``` 
-
-- Download the models from [here](https://github.com/openai/whisper/blob/ba3f3cd54b0e5b8ce1ab3de13e32122d0d5f98ab/whisper/__init__.py#L17C1-L30C2)
-```bash
-# small.en model
-wget --directory-prefix=assets https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt
-
-# small multilingual model
-wget --directory-prefix=assets https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt
-```
-
-- For this demo we build `small.en` and `small` multilingual TensorRT engine.
-```bash
-pip install -r requirements.txt
-
 # convert small.en
-python3 build.py --output_dir whisper_small_en --use_gpt_attention_plugin --use_gemm_plugin  --use_bert_attention_plugin --model_name small.en
+bash build_whisper_tensorrt /path/to/TensorRT-LLM/examples small.en
 
 # convert small multilingual model
-python3 build.py --output_dir whisper_small --use_gpt_attention_plugin --use_gemm_plugin  --use_bert_attention_plugin --model_name small
+bash build_whisper_tensorrt /path/to/TensorRT-LLM/examples small
 ```
 
-- Whisper/small.en tensorrt model engine is saved in `/home/TensorRT-LLM/examples/whisper/whisper_small_en` dir and if you converted the `small` multilingual model it should be saved in `/home/TensorRT-LLM/examples/whisper/whisper_small` dir.
-
 ## Run WhisperLive Server with TensorRT Backend
 ```bash
-
 cd /home/WhisperLive
-bash scripts/setup.sh
-pip install -r requirements.txt
 
 # Required to create mel spectogram
 wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
@@ -66,11 +50,11 @@ wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercont
 # Run English only model
 python3 run_server.py --port 9090 \
                       --backend tensorrt \
-                      --whisper_tensorrt_path /home/TensorRT-LLM/examples/whisper/whisper_small_en
+                      --trt_model_path "path/from/build/step"
 
 # Run Multilingual model
 python3 run_server.py --port 9090 \
                       --backend tensorrt \
-                      --whisper_tensorrt_path /home/TensorRT-LLM/examples/whisper/whisper_small_en \
+                      --trt_model_path "path/from/build/step" \
                       --trt_multilingual
 ```