Merge pull request collabora#5 from jpc/jpc/executable-docs

Docker images and a Quarto README with synchronized docs

Author: jpc

README.md

@@ -1,112 +1,234 @@
 # WhisperBot
-Welcome to WhisperBot. WhisperBot builds upon the capabilities of the [WhisperLive](https://github.com/collabora/WhisperLive) and [WhisperSpeech](https://github.com/collabora/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.
+
+
+Welcome to WhisperBot. WhisperBot builds upon the capabilities of the
+[WhisperLive](https://github.com/collabora/WhisperLive) and
+[WhisperSpeech](https://github.com/collabora/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.
+- **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.
+- **TensorRT Optimization**: Both Mistral and Whisper are optimized to
+  run as TensorRT engines, ensuring high-performance and low-latency
+  processing.
 
 ## Prerequisites
-Install [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/installation.md) 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](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docker/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.
+
+Install
+[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/installation.md)
+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](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docker/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
-- Change working dir to the [whisper example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper) in TensorRT-LLM.
-```bash
-cd 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.
-```bash
-wget --directory-prefix=assets assets/mel_filters.npz https://raw.githubusercontent.com/openai/whisper/main/whisper/assets/mel_filters.npz
-
-# small.en model
+
+> [!NOTE]
+>
+> These steps are included in `docker/scripts/build-whisper.sh`
+
+Change working dir to the [whisper example
+dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper)
+in TensorRT-LLM.
+
+``` bash
+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
+
+``` bash
+# 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
 ```
-- Edit `build.py` to support `small.en`. In order to do that, add `"small.en"` as an item in the list [`choices`](https://github.com/NVIDIA/TensorRT-LLM/blob/a75618df24e97ecf92b8899ca3c229c4b8097dda/examples/whisper/build.py#L58).
-- Build `small.en` TensorRT engine.
-```bash
+
+We have to patch the script to add support for out model size
+(`small.en`):
+
+``` bash
+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:
+
+``` bash
 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
-- Change working dir to [llama example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama) in TensorRT-LLM folder.
-```bash
-cd TensorRT-LLM/examples/llama
+
+> [!NOTE]
+>
+> These steps are included in `docker/scripts/build-mistral.sh`
+
+``` bash
+cd /root/TensorRT-LLM-examples/llama
 ```
-- Convert Mistral to `fp16` TensorRT engine.
-```bash
+
+Build TensorRT for Mistral with `fp16`
+
+``` bash
 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_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
-Note: Phi is only available in main branch and hasnt been released yet. So, make sure to build TensorRT-LLM from main branch.
-- Change working dir to [phi example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/phi) in TensorRT-LLM folder.
-```bash
-cd TensorRT-LLM/examples/phi
+
+> [!NOTE]
+>
+> 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.
+
+``` bash
+cd /root/TensorRT-LLM-examples/phi
 ```
-- Build phi TensorRT engine
-```bash
+
+Build TensorRT for Phi-2 with `fp16`
+
+``` bash
 git lfs install
-git clone https://huggingface.co/microsoft/phi-2
+phi_path=$(huggingface-cli download --repo-type model --revision 834565c23f9b28b96ccbeabe614dd906b6db551a microsoft/phi-2)
 python3 build.py --dtype=float16                    \
                  --log_level=verbose                \
                  --use_gpt_attention_plugin float16 \
                  --use_gemm_plugin float16          \
                  --max_batch_size=16                \
                  --max_input_len=1024               \
                  --max_output_len=1024              \
-                 --output_dir=phi_engine            \
-                 --model_dir=phi-2>&1 | tee build.log
+                 --output_dir=phi-2            \
+                 --model_dir="$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"
 ```
 
-## Run WhisperBot
-- Clone this repo and install requirements.
-```bash
-git clone https://github.com/collabora/WhisperBot.git
+## Build WhisperBot
+
+> [!NOTE]
+>
+> These steps are included in `docker/scripts/setup-whisperbot.sh`
+
+Clone this repo and install requirements
+
+``` bash
+[ -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
+
+``` bash
+pip install --extra-index-url https://download.pytorch.org/whl/cu121 torchaudio
+```
+
+Install all the other dependencies normally
+
+``` bash
 pip install -r requirements.txt
+pip install openai-whisper whisperspeech soundfile
 ```
 
-### Whisper + Mistral
-- Take the folder path for Whisper TensorRT model, folder_path and tokenizer_path for Mistral TensorRT from the build phase. If a huggingface model is used to build mistral then just use the huggingface repo name as the tokenizer path.
-```bash
-python3 main.py --mistral
-                --whisper_tensorrt_path /root/TensorRT-LLM/examples/whisper/whisper_small_en \
-                --mistral_tensorrt_path /root/TensorRT-LLM/examples/llama/tmp/mistral/7B/trt_engines/fp16/1-gpu/ \
-                --mistral_tokenizer_path teknium/OpenHermes-2.5-Mistral-7B
+force update huggingface_hub (tokenizers 0.14.1 spuriously require and
+ancient \<=0.18 version)
+
+``` bash
+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()'
 ```
 
-### Whisper + Phi
-- Take the folder path for Whisper TensorRT model, folder_path and tokenizer_path for Phi TensorRT from the build phase. If a huggingface model is used to build phi then just use the huggingface repo name as the tokenizer path.
-```bash
-python3 main.py --phi
-                --whisper_tensorrt_path /root/TensorRT-LLM/examples/whisper/whisper_small_en \
-                --phi_tensorrt_path /root/TensorRT-LLM/examples/phi/phi_engine \
-                --phi_tokenizer_path /root/TensorRT-LLM/examples/phi/phi-2
+### 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.
+
+> [!NOTE]
+>
+> These steps are included in `docker/scripts/run-whisperbot.sh`
+
+``` bash
+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`
-```bash
+- On the client side clone the repo, install the requirements and
+  execute `run_client.py`
+
+``` bash
 cd WhisperBot
 pip install -r requirements.txt
 python3 run_client.py
 ```
 
-
 ## Contact Us
-For questions or issues, please open an issue.
-Contact us at: [email protected], [email protected], [email protected]
+
+For questions or issues, please open an issue. Contact us at:
+[email protected], [email protected],
+[email protected]

README.qmd

@@ -0,0 +1,86 @@
+---
+format: gfm
+execute:
+  echo: false
+  output: asis
+---
+
+```{python}
+#| include: false
+def include_file(fname):
+  with open(fname) as f:
+    print(f'''
+:::{{.callout-note}}
+These steps are included in `{fname}`
+:::
+''')
+    code = False
+    for l in f:
+      if l.startswith('#!'):
+        continue
+      if l.startswith('## '):
+        if code: print("```"); code=False
+        print(l[3:])
+      elif l.strip():
+        if not code: print("```bash"); code=True
+        print(l.rstrip())
+    if code: print("```")
+```
+
+# WhisperBot
+
+Welcome to WhisperBot. WhisperBot builds upon the capabilities of the [WhisperLive](https://github.com/collabora/WhisperLive) and [WhisperSpeech](https://github.com/collabora/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](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/installation.md) 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](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docker/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
+
+```{python}
+include_file('docker/scripts/build-whisper.sh')
+```
+
+### Build Mistral TensorRT Engine
+
+```{python}
+include_file('docker/scripts/build-mistral.sh')
+```
+
+### Build Phi TensorRT Engine
+
+```{python}
+include_file('docker/scripts/build-phi-2.sh')
+```
+
+## Build WhisperBot
+
+```{python}
+include_file('docker/scripts/setup-whisperbot.sh')
+```
+
+### 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.
+
+```{python}
+include_file('docker/scripts/run-whisperbot.sh')
+```
+
+- On the client side clone the repo, install the requirements and execute `run_client.py`
+```bash
+cd WhisperBot
+pip install -r requirements.txt
+python3 run_client.py
+```
+
+## Contact Us
+For questions or issues, please open an issue.
+Contact us at: [email protected], [email protected], [email protected]

docker/Dockerfile

@@ -0,0 +1,8 @@
+FROM ghcr.io/collabora/whisperbot-base:latest as base
+
+WORKDIR /root
+COPY scripts/setup-whisperbot.sh scripts/run-whisperbot.sh scratch-space/models /root/
+RUN ./setup-whisperbot.sh
+
+CMD ./run-whisperbot.sh
+