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WhisperBot

Welcome to WhisperBot. WhisperBot builds upon the capabilities of the WhisperLive and WhisperSpeech by integrating Mistral, a Large Language Model (LLM), on top of the real-time speech-to-text pipeline. WhisperLive relies on OpenAI Whisper, a powerful automatic speech recognition (ASR) system. Both Mistral and Whisper are optimized to run efficiently as TensorRT engines, maximizing performance and real-time processing capabilities.

Features

  • Real-Time Speech-to-Text: Utilizes OpenAI WhisperLive to convert spoken language into text in real-time.

  • Large Language Model Integration: Adds Mistral, a Large Language Model, to enhance the understanding and context of the transcribed text.

  • TensorRT Optimization: Both Mistral and Whisper are optimized to run as TensorRT engines, ensuring high-performance and low-latency processing.

Prerequisites

Install TensorRT-LLM to build Whisper and Mistral TensorRT engines. The README builds a docker image for TensorRT-LLM. Instead of building a docker image, we can also refer to the README and the Dockerfile.multi to install the required packages in the base pytroch docker image. Just make sure to use the correct base image as mentioned in the dockerfile and everything should go nice and smooth.

Build Whisper TensorRT Engine

These steps are included in docker/scripts/build-whisper.sh

Change working dir to the whisper example dir in TensorRT-LLM.

cd /root/TensorRT-LLM-examples/whisper

Currently, by default TensorRT-LLM only supports large-v2 and large-v3. In this repo, we use small.en.

Download the required assets

# the sound filter definitions
wget --directory-prefix=assets https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
# the small.en model weights
wget --directory-prefix=assets https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt

We have to patch the script to add support for out model size (small.en):

patch <<EOF
--- build.py.old  2024-01-17 17:47:47.508545842 +0100
+++ build.py  2024-01-17 17:47:41.404941926 +0100
@@ -58,6 +58,7 @@
                         choices=[
                             "large-v3",
                             "large-v2",
+                            "small.en",
                         ])
     parser.add_argument('--quantize_dir', type=str, default="quantize/1-gpu")
     parser.add_argument('--dtype',
EOF

Finally we can build the TensorRT engine for the small.en Whisper model:

pip install -r requirements.txt
python3 build.py --output_dir whisper_small_en --use_gpt_attention_plugin --use_gemm_plugin --use_layernorm_plugin  --use_bert_attention_plugin --model_name small.en
mkdir -p /root/scratch-space/models
cp -r whisper_small_en /root/scratch-space/models

Build Mistral TensorRT Engine

These steps are included in docker/scripts/build-mistral.sh

cd /root/TensorRT-LLM-examples/llama

Build TensorRT for Mistral with fp16

python build.py --model_dir teknium/OpenHermes-2.5-Mistral-7B \
                --dtype float16 \
                --remove_input_padding \
                --use_gpt_attention_plugin float16 \
                --enable_context_fmha \
                --use_gemm_plugin float16 \
                --output_dir ./tmp/mistral/7B/trt_engines/fp16/1-gpu/ \
                --max_input_len 5000 \
                --max_batch_size 1
mkdir -p /root/scratch-space/models
cp -r tmp/mistral/7B/trt_engines/fp16/1-gpu /root/scratch-space/models/mistral

Build Phi TensorRT Engine

These steps are included in docker/scripts/build-phi-2.sh

Note: Phi is only available in main branch and hasnt been released yet. So, make sure to build TensorRT-LLM from main branch.

cd /root/TensorRT-LLM-examples/phi

Build TensorRT for Phi-2 with fp16

git lfs install
phi_path=$(huggingface-cli download --repo-type model --revision 834565c23f9b28b96ccbeabe614dd906b6db551a microsoft/phi-2)
python3 build.py --dtype=float16                    \
                 --log_level=verbose                \
                 --use_gpt_attention_plugin float16 \
                 --use_gemm_plugin float16          \
                 --max_batch_size=16                \
                 --max_input_len=1024               \
                 --max_output_len=1024              \
                 --output_dir=phi-2            \
                 --model_dir="$phi_path" >&1 | tee build.log
dest=/root/scratch-space/models
mkdir -p "$dest/phi-2/tokenizer"
cp -r phi-2 "$dest"
(cd "$phi_path" && cp config.json tokenizer_config.json vocab.json merges.txt "$dest/phi-2/tokenizer")
cp -r "$phi_path" "$dest/phi-orig-model"

Build WhisperBot

These steps are included in docker/scripts/setup-whisperbot.sh

Clone this repo and install requirements

[ -d "WhisperBot" ] || git clone https://github.com/collabora/WhisperBot.git
cd WhisperBot
apt update
apt install ffmpeg portaudio19-dev -y

Install torchaudio matching the PyTorch from the base image

pip install --extra-index-url https://download.pytorch.org/whl/cu121 torchaudio

Install all the other dependencies normally

pip install -r requirements.txt
pip install openai-whisper whisperspeech soundfile

force update huggingface_hub (tokenizers 0.14.1 spuriously require and ancient <=0.18 version)

pip install -U huggingface_hub
huggingface-cli download collabora/whisperspeech t2s-small-en+pl.model s2a-q4-tiny-en+pl.model
huggingface-cli download charactr/vocos-encodec-24khz
mkdir -p /root/.cache/torch/hub/checkpoints/
curl -L -o /root/.cache/torch/hub/checkpoints/encodec_24khz-d7cc33bc.th https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
mkdir -p /root/.cache/whisper-live/
curl -L -o /root/.cache/whisper-live/silero_vad.onnx https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx
python -c 'from transformers.utils.hub import move_cache; move_cache()'

Run WhisperBot with Whisper and Mistral/Phi-2

Take the folder path for Whisper TensorRT model, folder_path and tokenizer_path for Mistral/Phi-2 TensorRT from the build phase. If a huggingface model is used to build mistral/phi-2 then just use the huggingface repo name as the tokenizer path.

These steps are included in docker/scripts/run-whisperbot.sh

test -f /etc/shinit_v2 && source /etc/shinit_v2
cd WhisperBot
if [ "$1" != "mistral" ]; then
  exec python3 main.py --phi \
                  --whisper_tensorrt_path /root/whisper_small_en \
                  --phi_tensorrt_path /root/phi-2 \
                  --phi_tokenizer_path /root/phi-2
else
  exec python3 main.py --mistral \
                  --whisper_tensorrt_path /root/models/whisper_small_en \
                  --mistral_tensorrt_path /root/models/mistral \
                  --mistral_tokenizer_path teknium/OpenHermes-2.5-Mistral-7B
fi
  • On the client side clone the repo, install the requirements and execute run_client.py
cd WhisperBot
pip install -r requirements.txt
python3 run_client.py

Contact Us

For questions or issues, please open an issue. Contact us at: marcus.edel@collabora.com, jpc@collabora.com, vineet.suryan@collabora.com