testing 123

Dockerfile

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

README (2).md

@@ -0,0 +1,61 @@
+# WhisperFusion
+
+<h2 align="center">
+  <a href="https://www.youtube.com/watch?v=_PnaP0AQJnk"><img
+src="https://img.youtube.com/vi/_PnaP0AQJnk/0.jpg" style="background-color:rgba(0,0,0,0);" height=300 alt="WhisperFusion"></a>
+  <br><br>Seamless conversations with AI (with ultra-low latency)<br><br>
+</h2>
+
+Welcome to WhisperFusion. WhisperFusion 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. Both LLM and
+Whisper are optimized to run efficiently as TensorRT engines, maximizing
+performance and real-time processing capabilities. While WhiperSpeech is 
+optimized with torch.compile.
+
+## 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 LLM and Whisper are optimized to
+  run as TensorRT engines, ensuring high-performance and low-latency
+  processing.
+- **torch.compile**: WhisperSpeech uses torch.compile to speed up 
+  inference which makes PyTorch code run faster by JIT-compiling PyTorch
+  code into optimized kernels.
+
+## Getting Started
+- We provide a pre-built TensorRT-LLM docker container that has both whisper and
+  phi converted to TensorRT engines and WhisperSpeech model is pre-downloaded to 
+  quickly start interacting with WhisperFusion.
+```bash
+ docker run --gpus all --shm-size 64G -p 6006:6006 -p 8888:8888 -it ghcr.io/collabora/whisperfusion:latest
+```
+
+- Start Web GUI
+```bash
+ cd examples/chatbot/html
+ python -m http.server
+```
+
+## Build Docker Image
+- We provide the docker image for cuda-architecures 89 and 90. If you have a GPU
+  with a different cuda architecture. For e.g. to build for RTX 3090 with cuda-
+  architecture 86
+```bash
+ bash build.sh 86-real
+```
+This should build the `ghcr.io/collabora/whisperfusion:latest` for RTX 3090.
+
+## Contact Us
+
+For questions or issues, please open an issue. Contact us at:
+marcus.edel@collabora.com, jpc@collabora.com,
+vineet.suryan@collabora.com

README.qmd

@@ -0,0 +1,92 @@
+---
+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("```")
+```
+
+# WhisperFusion
+
+<h2 align="center">
+  <a href="https://www.youtube.com/watch?v=_PnaP0AQJnk"><img
+src="https://img.youtube.com/vi/_PnaP0AQJnk/0.jpg" style="background-color:rgba(0,0,0,0);" height=300 alt="WhisperFusion"></a>
+  <br><br>Doing math with WhisperFusion: Ultra-low latency conversations with an AI chatbot<br><br>
+</h2>
+
+Welcome to WhisperFusion. WhisperFusion 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 WhisperFusion
+
+```{python}
+include_file('docker/scripts/setup-whisperfusion.sh')
+```
+
+### Run WhisperFusion 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-whisperfusion.sh')
+```
+
+- On the client side clone the repo, install the requirements and execute `run_client.py`
+```bash
+cd WhisperFusion
+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

assets/mel_filters.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7450ae70723a5ef9d341e3cee628c7cb0177f36ce42c44b7ed2bf3325f0f6d4c
+size 4271

build-dolphin-2_6-phi-2.sh

@@ -0,0 +1,26 @@
+#!/bin/bash -e
+
+## 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 [Dolphin Phi Finetuned](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2) ChatML format with `fp16`
+
+git lfs install
+phi_path=$(huggingface-cli download --repo-type model cognitivecomputations/dolphin-2_6-phi-2)
+name=dolphin-2_6-phi-2
+python3 build.py --dtype=float16                    \
+                 --log_level=verbose                \
+                 --use_gpt_attention_plugin float16 \
+                 --use_gemm_plugin float16          \
+                 --max_batch_size=1                 \
+                 --max_input_len=1024               \
+                 --max_output_len=1024              \
+                 --output_dir=$name                 \
+                 --model_dir="$phi_path" >&1 | tee build.log
+
+dest=/root/scratch-space/models
+mkdir -p "$dest/$name/tokenizer"
+cp -r "$name" "$dest"
+(cd "$phi_path" && cp config.json tokenizer_config.json vocab.json merges.txt "$dest/$name/tokenizer")
+cp -r "$phi_path" "$dest/phi-orig-model"

build-mistral.sh

@@ -0,0 +1,18 @@
+#!/bin/bash -e
+
+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-models.sh

@@ -0,0 +1,7 @@
+#!/bin/bash -e
+
+test -f /etc/shinit_v2 && source /etc/shinit_v2
+
+./build-whisper.sh
+# ./build-mistral.sh
+./build-dolphin-2_6-phi-2.sh

build-phi-2.sh

@@ -0,0 +1,25 @@
+#!/bin/bash -e
+
+## 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-whisper.sh

@@ -0,0 +1,33 @@
+#!/bin/bash -e
+
+## Change working dir to the [whisper example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper) 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.sh

@@ -0,0 +1,21 @@
+#!/bin/bash -e
+
+if [ -n "$1" ]; then
+  CUDA_ARCH="$1"
+  BASE_IMAGE_BUILD_ARG="--build-arg CUDA_ARCH=$CUDA_ARCH"
+else
+  BASE_IMAGE_BUILD_ARG=""
+fi
+
+[ -n "$VERBOSE" ] && ARGS="--progress plain"
+
+(
+  cd base-image &&
+  docker build $ARGS $BASE_IMAGE_BUILD_ARG -t ghcr.io/collabora/whisperfusion-base:latest .
+)
+
+mkdir -p scratch-space
+cp -r scripts/build-* scratch-space
+docker run --gpus all --shm-size 64G -v "$PWD"/scratch-space:/root/scratch-space -w /root/scratch-space -it ghcr.io/collabora/whisperfusion-base:latest ./build-models.sh
+
+docker build $ARGS -t ghcr.io/collabora/whisperfusion:latest .

docker/Dockerfile

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

docker/base-image/Dockerfile

@@ -0,0 +1,14 @@
+#ARG BASE_IMAGE=nvcr.io/nvidia/pytorch
+#ARG BASE_TAG=23.10-py3
+ARG BASE_IMAGE=nvcr.io/nvidia/cuda
+ARG BASE_TAG=12.2.2-devel-ubuntu22.04
+
+FROM ${BASE_IMAGE}:${BASE_TAG} as base
+ARG CUDA_ARCH
+WORKDIR /root
+COPY install-deps.sh /root
+ENV CUDA_ARCH=${CUDA_ARCH}
+RUN bash install-deps.sh && rm install-deps.sh
+
+COPY install-trt-llm.sh /root
+RUN bash install-trt-llm.sh && rm install-trt-llm.sh

docker/base-image/install-deps.sh

@@ -0,0 +1,54 @@
+#!/bin/bash -e
+
+apt-get update && apt-get -y install git git-lfs
+git clone --depth=1 -b cuda12.2 https://github.com/makaveli10/TensorRT-LLM.git
+cd TensorRT-LLM
+git checkout main
+git submodule update --init --recursive
+git lfs install
+git lfs pull
+
+# do not reinstall CUDA (our base image provides the same exact versions)
+patch -p1 <<EOF
+diff --git a/docker/common/install_tensorrt.sh b/docker/common/install_tensorrt.sh
+index 2dcb0a6..3a27e03 100644
+--- a/docker/common/install_tensorrt.sh
++++ b/docker/common/install_tensorrt.sh
+@@ -35,19 +35,7 @@ install_ubuntu_requirements() {
+     dpkg -i cuda-keyring_1.0-1_all.deb
+
+     apt-get update
+-    if [[ $(apt list --installed | grep libcudnn8) ]]; then
+-      apt-get remove --purge -y libcudnn8*
+-    fi
+-    if [[ $(apt list --installed | grep libnccl) ]]; then
+-      apt-get remove --purge -y --allow-change-held-packages libnccl*
+-    fi
+-    if [[ $(apt list --installed | grep libcublas) ]]; then
+-      apt-get remove --purge -y --allow-change-held-packages libcublas*
+-    fi
+-    CUBLAS_CUDA_VERSION=$(echo $CUDA_VER | sed 's/\./-/g')
+     apt-get install -y --no-install-recommends libcudnn8=${CUDNN_VER} libcudnn8-dev=${CUDNN_VER}
+-    apt-get install -y --no-install-recommends libnccl2=${NCCL_VER} libnccl-dev=${NCCL_VER}
+-    apt-get install -y --no-install-recommends libcublas-${CUBLAS_CUDA_VERSION}=${CUBLAS_VER} libcublas-dev-${CUBLAS_CUDA_VERSION}=${CUBLAS_VER}
+     apt-get clean
+     rm -rf /var/lib/apt/lists/*
+ }
+EOF
+
+cd docker/common/
+export BASH_ENV=${BASH_ENV:-/etc/bash.bashrc}
+export ENV=${ENV:-/etc/shinit_v2}
+bash install_base.sh
+bash install_cmake.sh
+source $ENV
+bash install_ccache.sh
+# later on TensorRT-LLM will force reinstall this version anyways
+pip3 install --extra-index-url https://download.pytorch.org/whl/cu121 torch
+bash install_tensorrt.sh
+bash install_polygraphy.sh
+source $ENV
+
+cd /root/TensorRT-LLM/docker/common/
+bash install_mpi4py.sh
+source $ENV

docker/base-image/install-trt-llm.sh

@@ -0,0 +1,16 @@
+#!/bin/bash -e
+
+export ENV=${ENV:-/etc/shinit_v2}
+source $ENV
+
+CUDA_ARCH="${CUDA_ARCH:-89-real;90-real}"
+
+cd /root/TensorRT-LLM
+python3 scripts/build_wheel.py --clean --cuda_architectures "$CUDA_ARCH" --trt_root /usr/local/tensorrt
+pip install build/tensorrt_llm-0.7.1-cp310-cp310-linux_x86_64.whl
+mv examples ../TensorRT-LLM-examples
+cd ..
+
+rm -rf TensorRT-LLM
+# we don't need static libraries and they take a lot of space
+(cd /usr && find . -name '*static.a' | grep -v cudart_static | xargs rm -f)

docker/build.sh

@@ -0,0 +1,21 @@
+#!/bin/bash -e
+
+if [ -n "$1" ]; then
+  CUDA_ARCH="$1"
+  BASE_IMAGE_BUILD_ARG="--build-arg CUDA_ARCH=$CUDA_ARCH"
+else
+  BASE_IMAGE_BUILD_ARG=""
+fi
+
+[ -n "$VERBOSE" ] && ARGS="--progress plain"
+
+(
+  cd base-image &&
+  docker build $ARGS $BASE_IMAGE_BUILD_ARG -t ghcr.io/collabora/whisperfusion-base:latest .
+)
+
+mkdir -p scratch-space
+cp -r scripts/build-* scratch-space
+docker run --gpus all --shm-size 64G -v "$PWD"/scratch-space:/root/scratch-space -w /root/scratch-space -it ghcr.io/collabora/whisperfusion-base:latest ./build-models.sh
+
+docker build $ARGS -t ghcr.io/collabora/whisperfusion:latest .

docker/publish.sh

@@ -0,0 +1,4 @@
+#!/bin/bash -e
+
+docker push ghcr.io/collabora/whisperfusion-base:latest
+docker push ghcr.io/collabora/whisperfusion:latest

docker/scripts/build-dolphin-2_6-phi-2.sh

@@ -0,0 +1,26 @@
+#!/bin/bash -e
+
+## 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 [Dolphin Phi Finetuned](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2) ChatML format with `fp16`
+
+git lfs install
+phi_path=$(huggingface-cli download --repo-type model cognitivecomputations/dolphin-2_6-phi-2)
+name=dolphin-2_6-phi-2
+python3 build.py --dtype=float16                    \
+                 --log_level=verbose                \
+                 --use_gpt_attention_plugin float16 \
+                 --use_gemm_plugin float16          \
+                 --max_batch_size=1                 \
+                 --max_input_len=1024               \
+                 --max_output_len=1024              \
+                 --output_dir=$name                 \
+                 --model_dir="$phi_path" >&1 | tee build.log
+
+dest=/root/scratch-space/models
+mkdir -p "$dest/$name/tokenizer"
+cp -r "$name" "$dest"
+(cd "$phi_path" && cp config.json tokenizer_config.json vocab.json merges.txt "$dest/$name/tokenizer")
+cp -r "$phi_path" "$dest/phi-orig-model"

docker/scripts/build-mistral.sh

@@ -0,0 +1,18 @@
+#!/bin/bash -e
+
+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

docker/scripts/build-models.sh

@@ -0,0 +1,7 @@
+#!/bin/bash -e
+
+test -f /etc/shinit_v2 && source /etc/shinit_v2
+
+./build-whisper.sh
+# ./build-mistral.sh
+./build-dolphin-2_6-phi-2.sh

docker/scripts/build-phi-2.sh

@@ -0,0 +1,25 @@
+#!/bin/bash -e
+
+## 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"

docker/scripts/build-whisper.sh

@@ -0,0 +1,33 @@
+#!/bin/bash -e
+
+## Change working dir to the [whisper example dir](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/whisper) 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

docker/scripts/run-whisperfusion.sh

@@ -0,0 +1,16 @@
+#!/bin/bash -e
+
+test -f /etc/shinit_v2 && source /etc/shinit_v2
+
+cd WhisperFusion
+if [ "$1" != "mistral" ]; then
+  exec python3 main.py --phi \
+                  --whisper_tensorrt_path /root/whisper_small_en \
+                  --phi_tensorrt_path /root/dolphin-2_6-phi-2 \
+                  --phi_tokenizer_path /root/dolphin-2_6-phi-2/tokenizer
+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

docker/scripts/setup-whisperfusion.sh

@@ -0,0 +1,27 @@
+#!/bin/bash -e
+
+## Clone this repo and install requirements
+[ -d "WhisperFusion" ] || git clone https://github.com/collabora/WhisperFusion.git
+
+cd WhisperFusion
+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
+
+## 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()'

docker/scripts/setup.sh

@@ -0,0 +1,6 @@
+#!/bin/bash -e
+
+./setup-whisper.sh
+#./setup-mistral.sh
+./setup-phi-2.sh
+./setup-whisperfusion.sh

examples/chatbot/html/css/all.min.css

@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 5.15.3 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
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Awesome 5 Brands";font-style:normal;font-weight:400;font-display:block;src:url(../webfonts/fa-brands-400.eot);src:url(../webfonts/fa-brands-400.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-brands-400.woff2) format("woff2"),url(../webfonts/fa-brands-400.woff) format("woff"),url(../webfonts/fa-brands-400.ttf) format("truetype"),url(../webfonts/fa-brands-400.svg#fontawesome) format("svg")}.fab{font-family:"Font Awesome 5 Brands"}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:400;font-display:block;src:url(../webfonts/fa-regular-400.eot);src:url(../webfonts/fa-regular-400.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-regular-400.woff2) format("woff2"),url(../webfonts/fa-regular-400.woff) format("woff"),url(../webfonts/fa-regular-400.ttf) format("truetype"),url(../webfonts/fa-regular-400.svg#fontawesome) format("svg")}.fab,.far{font-weight:400}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:900;font-display:block;src:url(../webfonts/fa-solid-900.eot);src:url(../webfonts/fa-solid-900.eot?#iefix) format("embedded-opentype"),url(../webfonts/fa-solid-900.woff2) format("woff2"),url(../webfonts/fa-solid-900.woff) format("woff"),url(../webfonts/fa-solid-900.ttf) format("truetype"),url(../webfonts/fa-solid-900.svg#fontawesome) format("svg")}.fa,.far,.fas{font-family:"Font Awesome 5 Free"}.fa,.fas{font-weight:900}

examples/chatbot/html/css/style.css

@@ -0,0 +1,437 @@
+/* Import Google font - Poppins */
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600&display=swap');
+*{
+  /* margin: 0px;
+  padding: 0px; */
+  box-sizing: border-box;
+  font-family: "Poppins", sans-serif;
+}
+html, body {
+  height: 100%;
+}
+
+html {
+  display: table;
+  /* margin: auto; */
+}
+
+
+body{
+  /* display: flex; */
+  align-items: center;
+  /* justify-content: center; */
+  min-height: 100vh;
+  background: #EBECF2;
+  
+  display: table-cell;
+  vertical-align: middle;
+
+  margin: 0 !important;
+  padding: 0 !important;
+
+  
+
+}
+
+
+.avatar-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto auto 1fr;
+grid-gap: 0px;
+padding-left:0px;
+padding-top: 2px;
+margin-top: 10px;
+}
+
+.text-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto 1fr;
+grid-gap: 0px;
+padding-left:0px;
+padding-top: 8px;
+}
+
+.llm-timing-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto auto 1fr;
+grid-gap: 4px;
+padding-left:0px;
+padding-top: 8px;
+font-size: 12px;
+}
+
+.transcription-timing-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto auto 1fr;
+grid-gap: 4px;
+padding-left:0px;
+padding-top: 8px;
+font-size: 12px;
+}
+
+.whisperspeech-timing-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto 1fr;
+grid-gap: 4px;
+padding-left:0px;
+padding-top: 8px;
+font-size: 12px;
+}
+
+.whisperspeech-audio-container {
+  text-align: left;
+  display: grid;
+  grid-template-columns: 1fr auto 1fr;
+  grid-gap: 4px;
+  padding-left:0px;
+  padding-top: 8px;
+  font-size: 12px;
+  }
+
+.control-container {
+text-align: left;
+display: grid;
+grid-template-columns: auto auto 20px auto auto auto 1fr;
+grid-gap: 4px;
+padding-left:0px;
+font-size: 12px;
+}
+
+
+.avatar {
+border-radius: 50%;
+width: 24px;
+float: left;
+}
+  
+.avatar-name {
+font-size:15px;
+padding-left:4px;
+padding-top:2px
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+::selection{
+  color: #fff;
+}
+.wrapper{
+  width: 430px;
+  background: #fff;
+  border-radius: 5px;
+  padding: 30px;
+  box-shadow: 7px 7px 12px rgba(0,0,0,0.05);
+}
+.wrapper header{
+  /* color: #6990F2; */
+  font-size: 27px;
+  font-weight: 600;
+  text-align: center;
+}
+.wrapper form{
+  height: 167px;
+  display: flex;
+  cursor: pointer;
+  margin: 30px 0;
+  align-items: center;
+  justify-content: center;
+  flex-direction: column;
+  border-radius: 5px;
+  border: 2px dashed #6990F2;
+  background-color: #F7FBFF;
+}
+form :where(i, p){
+  /* color: #6990F2; */
+}
+form i{
+  font-size: 50px;
+}
+form p{
+  margin-top: 15px;
+  font-size: 16px;
+}
+
+.row {
+    border: 0.01em solid lightgray;
+}
+
+section .row{
+  margin-bottom: 10px;
+  background: #E9F0FF;
+  list-style: none;
+  padding: 15px 20px;
+  border-radius: 5px;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+section .row i{
+  /* color: #6990F2; */
+  font-size: 30px;
+}
+section .details span{
+  font-size: 14px;
+}
+.progress-area .row .content{
+  width: 100%;
+  margin-left: 15px;
+}
+.progress-area .details{
+  display: flex;
+  align-items: center;
+  margin-bottom: 7px;
+  justify-content: space-between;
+}
+.progress-area .content .progress-bar{
+  height: 6px;
+  width: 100%;
+  margin-bottom: 4px;
+  background: #fff;
+  border-radius: 30px;
+}
+.content .progress-bar .progress{
+  height: 100%;
+  width: 0%;
+  border-radius: inherit;
+}
+.uploaded-area{
+  max-height: 232px;
+  overflow-y: scroll;
+}
+.uploaded-area.onprogress{
+  max-height: 150px;
+}
+.uploaded-area::-webkit-scrollbar{
+  width: 0px;
+}
+.uploaded-area .row .content{
+  display: flex;
+  align-items: center;
+}
+.uploaded-area .row .details{
+  display: flex;
+  margin-left: 15px;
+  flex-direction: column;
+}
+
+.uploaded-area .row .details .size{
+  color: #404040;
+  font-size: 11px;
+}
+.uploaded-area i.fa-check{
+  font-size: 16px;
+}
+
+
+.upload-container-title {
+text-align: left;
+display: grid;
+grid-template-columns: auto auto 1fr;
+grid-gap: 6px;
+padding-left:0px;
+/* background-color: #F8F8F8; */
+padding-top:0px;
+font-weight: 400;
+}
+
+.name {
+    font-size: 12px;
+    text-align: left;
+}
+
+
+.process-container-chart {
+  text-align: left;
+  display: grid;
+  grid-template-columns: 50% 50%;
+  grid-gap: 6px;
+  padding-left:0px;
+  /* background-color: #F8F8F8; */
+  padding-top:0px;
+  font-weight: 400;
+  }
+
+
+
+
+
+
+
+
+
+
+
+
+
+.radial {
+  width: 16vh;
+  height: 16vh;
+  margin: auto;
+  position: relative;
+}
+.ring {
+  position: absolute;
+  top: 0%;
+  left: 0%;
+  transform: rotateZ(0);
+  width: 100%;
+  height: 100%;
+  color: hsl(40, 100%, 60%);
+}
+.ring:before {
+  pointer-events: none;
+  content: '';
+  border: 1.5vh solid hsla(40, 100%, 60%, 0.25);
+  border-radius: 60vh;
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  left: 0;
+  right: 0;
+}
+.ring .label {
+  position: absolute;
+  width: 7em;
+  text-align: right;
+  font-size: 2.025vh;
+  font-weight: 300;
+  line-height: 1.5vh;
+  right: 50%;
+  transform: translate3d(-20%, 0, 0);
+  text-transform: uppercase;
+  letter-spacing: 0.5em;
+  z-index: 10;
+  color: transparent;
+}
+.dot {
+  width: 1.5vh;
+  height: 50%;
+  position: absolute;
+  top: 0;
+  left: 50%;
+  transform: rotateZ(0deg);
+  transform-origin: 0 100%;
+  transform-style: preserve-3d;
+}
+.dot:before {
+  content: '';
+  display: inline-block;
+  width: 5.4vh;
+  height: 1.5vh;
+  border-radius: 100%;
+  background: hsl(40, 100%, 60%);
+  transform: translate3d(-50%, 0, 0) scale(0);
+  opacity: 0;
+  transition: transform 0.6s, opacity 0.6s;
+}
+.dot.val:before {
+  transform: translate3d(-50%, 0, 0) scale(1);
+  opacity: 1;
+}
+.ring:nth-child(4) {
+  width: 10%;
+  height: 10%;
+  top: 45%;
+  left: 45%;
+  color: hsl(355, 100%, 60%);
+  -webkit-animation-delay: 0.75s;
+          animation-delay: 0.75s;
+}
+.ring:nth-child(4):before {
+  border-color: hsla(355, 100%, 60%, 0.25);
+}
+.ring:nth-child(4) .dot:before {
+  background: hsl(355, 100%, 60%);
+}
+.ring:nth-child(3) {
+  width: 40%;
+  height: 40%;
+  top: 30%;
+  left: 30%;
+  color: hsl(10, 100%, 60%);
+  -webkit-animation-delay: 0.5s;
+          animation-delay: 0.5s;
+}
+.ring:nth-child(3):before {
+  border-color: hsla(10, 100%, 60%, 0.25);
+}
+.ring:nth-child(3) .dot:before {
+  background: hsl(10, 100%, 60%);
+}
+.ring:nth-child(2) {
+  width: 70%;
+  height: 70%;
+  top: 15%;
+  left: 15%;
+  color: hsl(25, 100%, 60%);
+  -webkit-animation-delay: 0.25s;
+          animation-delay: 0.25s;
+}
+.ring:nth-child(2):before {
+  border-color: hsla(25, 100%, 60%, 0.25);
+}
+.ring:nth-child(2) .dot:before {
+  background: hsl(25, 100%, 60%);
+}
+@-webkit-keyframes spin {
+  to {
+    transform: rotateZ(1turn);
+  }
+}
+@keyframes spin {
+  to {
+    transform: rotateZ(1turn);
+  }
+}
+.slides {
+  opacity: 0.35;
+  margin: 5vh 0;
+  text-align: center;
+}
+.slides input {
+  display: inline-block;
+}
+*,
+* :before,
+* :after {
+  box-sizing: border-box;
+  -webkit-backface-visibility: hidden;
+          backface-visibility: hidden;
+}
+
+summary {
+  -webkit-touch-callout: none;
+  -webkit-user-select: none;
+  -khtml-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+
+
+.button-item {
+  cursor: pointer;
+}
+
+.audio-container {
+margin-top:14px;
+}

examples/chatbot/html/index.html

@@ -0,0 +1,52 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Collabora - WhisperFusion: The Power of WhisperLive, LLM's, and WhisperSpeech</title>
+  <link rel="stylesheet" href="css/style.css">
+  <link rel="stylesheet" href="css/all.min.css"/>
+</head>
+<body>
+  <div style="background-color: white;width:100vw;margin-top:0;">
+  <div style="max-width: 500px; margin: 0 auto !important; float: none !important;padding-top:20px;padding-bottom:30px;font-family: 'Poppins', sans-serif;font-weight: 400;">
+    <div style="width:150px;padding-top:40px;display:flex;max-width: 500px;">
+      <img src="img/Collabora_Logo.svg" \>
+    </div>
+    <br>
+    <h3>WhisperFusion: The Power of WhisperLive, LLM's, and WhisperSpeech</h3>
+    <br>
+    <p>Using state-of-the-art natural language processing techniques, we implemented WhisperFusion a techonlogy demo that combines live transcriptions, LLM's and text-to-speech, in a low-latency pipeline. For more details about the demo, checkout <a style="color:#6990F2" href="https://github.com/collabora/WhisperFusion">https://github.com/collabora/WhisperFusion</a>.</p>
+  </div>
+</div>
+  <center>
+    <div id="main-wrapper" class="message-wrapper" style="max-width: 500px;"></div>
+
+    <br><br>
+    <div id="control-container" class="control-container" style="width:500px;height:50px;background-color: #5C2983;border-radius: 20px;">
+      <div style="margin-top:10px;padding-left:20px">
+        <img class="button-item" onclick="startRecording()"  onmouseleave='this.src="img/microphone.png"'  onmouseover='this.src="img/microphone-hover.png"' style="width:20px;padding-top:4px" src="img/microphone-white.png" \>
+      </div>
+      <div style="margin-top:10px">
+        <img class="button-item" onclick="stopRecording()" id="recording-stop-btn" style="width:20px;display:none;padding-top:4px" src="img/stop.png" \>
+      </div>
+      <div>
+        <div id="instructions-text" style="width:440px;height:40px;border-radius: 14px;color:white;padding:4px;font-size:14px;text-align:center;padding-top:15px">Click the microphone to start</div>
+      </div>
+      <div style="margin-top:10px">
+        <img id="recording-dot" class="avatar" src="img/record.png" style="display:none;padding-top:2px" \>
+      </div>
+      <div id="recording-line" style="display:none;margin-top:20px;width:280px;height:4px;background-color: #6990F2;border-radius: 2px;padding-top:6px">
+      </div>
+      <div style="margin-top:2px;padding-left:10px;padding-top:0px">
+        <p id="recording-time" style="display:none;">00:00</p>
+      </div>
+      <div></div>
+    </div>
+    <br><br>
+  </center>
+
+</body>
+
+<script src="js/main.js"></script>
+</html>

examples/chatbot/html/js/audio-processor.js

@@ -0,0 +1,31 @@
+class AudioStreamProcessor extends AudioWorkletProcessor {
+  constructor() {
+    super();
+    this.chunkSize = 4096;
+    this.buffer = new Float32Array(this.chunkSize);
+    this.bufferPointer = 0;
+  }
+
+  process(inputs, outputs, parameters) {
+    const input = inputs[0];
+    const output = outputs[0];
+
+    for (let i = 0; i < input[0].length; i++) {
+      this.buffer[this.bufferPointer++] = input[0][i];
+
+      if (this.bufferPointer >= this.chunkSize) {
+
+        this.port.postMessage(this.buffer);
+        this.bufferPointer = 0;
+      }
+    }
+
+    for (let channel = 0; channel < input.length; ++channel) {
+      output[channel].set(input[channel]);
+    }
+
+    return true;
+  }
+}
+
+registerProcessor("audio-stream-processor", AudioStreamProcessor);

examples/chatbot/html/js/main.js

@@ -0,0 +1,340 @@
+let websocket_uri = 'ws://localhost:6006';
+let websocket_audio_uri = 'ws://localhost:8888';
+
+let bufferSize = 4096,
+    AudioContext,
+    context,
+    processor,
+    input,
+    websocket;
+var intervalFunction = null;
+var recordingTime = 0;
+var server_state = 0;
+var websocket_audio = null;
+let audioContext_tts = null;
+var you_name = "Marcus"
+
+var audioContext = null;
+var audioWorkletNode = null;
+var audio_state = 0;
+var available_transcription_elements = 0;
+var available_llm_elements = 0;
+var available_audio_elements = 0;
+var llm_outputs = [];
+var new_transcription_element_state = true;
+var audio_sources = [];
+var audio_source = null;
+
+initWebSocket();
+
+const zeroPad = (num, places) => String(num).padStart(places, '0')
+
+const generateUUID = () => {
+    let dt = new Date().getTime();
+    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
+        const r = (dt + Math.random() * 16) % 16 | 0;
+        dt = Math.floor(dt / 16);
+        return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
+    });
+};
+
+function recording_timer() {
+    recordingTime++;
+    document.getElementById("recording-time").innerHTML = zeroPad(parseInt(recordingTime / 60), 2) + ":" + zeroPad(parseInt(recordingTime % 60), 2) + "s";
+}
+
+const start_recording = async () => {
+    console.log(audioContext)
+    try {
+        if (audioContext) {
+            
+            await audioContext.resume();
+            
+            const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+            
+            if (!audioContext) return;
+            console.log(audioContext?.state);
+
+            await audioContext.audioWorklet.addModule("js/audio-processor.js");
+
+            const source = audioContext.createMediaStreamSource(stream);
+            audioWorkletNode = new AudioWorkletNode(audioContext, "audio-stream-processor");
+
+            audioWorkletNode.port.onmessage = (event) => {
+                if (server_state != 1) {
+                  console.log("server is not ready!!")
+                  return;
+                }
+                const audioData = event.data;
+                if (websocket && websocket.readyState === WebSocket.OPEN && audio_state == 0) {
+                    websocket.send(audioData.buffer);
+                    console.log("send data")
+                }
+            };
+
+            source.connect(audioWorkletNode);
+        }
+    } catch (e) {
+        console.log("Error", e);
+    }
+};
+
+const handleStartRecording = async () => {
+    start_recording();
+};
+
+const startRecording = async () => {
+    document.getElementById("instructions-text").style.display = "none";
+    document.getElementById("control-container").style.backgroundColor = "white";
+
+    AudioContext = window.AudioContext || window.webkitAudioContext;
+    audioContext = new AudioContext({ latencyHint: 'interactive', sampleRate: 16000 });
+
+    audioContext_tts = new AudioContext({ sampleRate: 24000 });
+
+    document.getElementById("recording-stop-btn").style.display = "block";
+    document.getElementById("recording-dot").style.display = "block";
+    document.getElementById("recording-line").style.display = "block";
+    document.getElementById("recording-time").style.display = "block";
+    
+    intervalFunction = setInterval(recording_timer, 1000);
+
+    await handleStartRecording();
+};
+
+function stopRecording() {
+    audio_state = 1;
+    clearInterval(intervalFunction);
+}
+
+function initWebSocket() {
+    websocket_audio = new WebSocket(websocket_audio_uri);
+    websocket_audio.binaryType = "arraybuffer";
+
+    websocket_audio.onopen = function() { }
+    websocket_audio.onclose = function(e) { }
+    websocket_audio.onmessage = function(e) {
+        available_audio_elements++;
+        
+        let float32Array = new Float32Array(e.data);
+        let audioBuffer = audioContext_tts.createBuffer(1, float32Array.length, 24000);
+        audioBuffer.getChannelData(0).set(float32Array);
+
+        new_whisper_speech_audio_element("audio-" + available_audio_elements, Math.floor(audioBuffer.duration));
+
+        audio_sources.push(audioBuffer);
+
+        audio_source = audioContext_tts.createBufferSource();
+        audio_source.buffer = audioBuffer;
+        audio_source.connect(audioContext_tts.destination);
+        audio_source.start();
+
+        window.scrollTo({ top: document.body.scrollHeight, behavior: 'smooth' });
+    }
+
+    websocket = new WebSocket(websocket_uri);
+    websocket.binaryType = "arraybuffer";
+
+    console.log("Websocket created.");
+  
+    websocket.onopen = function() {
+      console.log("Connected to server.");
+      
+      websocket.send(JSON.stringify({
+        uid: generateUUID(),
+        multilingual: false,
+        language: "en",
+        task: "transcribe"
+      }));
+    }
+    
+    websocket.onclose = function(e) {
+      console.log("Connection closed (" + e.code + ").");
+    }
+    
+    websocket.onmessage = function(e) {
+      var data = JSON.parse(e.data);
+
+      if ("message" in data) {
+        if (data["message"] == "SERVER_READY") {
+            server_state = 1;
+        }
+      } else if ("segments" in data) {
+        if (new_transcription_element_state) {
+            available_transcription_elements = available_transcription_elements + 1;
+
+            var img_src = "0.png";
+            if (you_name.toLowerCase() == "marcus") {
+                you_name = "Marcus";
+                img_src = "0.png";
+            } else if (you_name.toLowerCase() == "vineet") {
+                you_name = "Vineet";
+                img_src = "1.png";
+            } else if (you_name.toLowerCase() == "jakub") {
+                you_name = "Jakub";
+                img_src = "2.png";
+            }
+
+            new_transcription_element(you_name, img_src);
+            new_text_element("<p>" +  data["segments"][0].text + "</p>", "transcription-" + available_transcription_elements);
+            new_transcription_element_state = false;
+        }
+        document.getElementById("transcription-" + available_transcription_elements).innerHTML = "<p>" + data["segments"][0].text + "</p>"; 
+
+        if (data["eos"] == true) {
+            new_transcription_element_state = true;
+        }
+      } else if ("llm_output" in data) {
+            new_transcription_element("Phi-2", "Phi.svg");
+            new_text_element("<p>" +  data["llm_output"][0] + "</p>", "llm-" + available_transcription_elements);
+      }
+
+      window.scrollTo({ top: document.body.scrollHeight, behavior: 'smooth' });
+    }
+}
+
+function new_transcription_element(speaker_name, speaker_avatar) {
+    var avatar_container = document.createElement("div");
+    avatar_container.className = "avatar-container";
+
+    var avatar_img = document.createElement("div");
+    avatar_img.innerHTML = "<img class='avatar' src='img/" + speaker_avatar + "' \>";
+
+    var avatar_name = document.createElement("div");
+    avatar_name.className = "avatar-name";
+    avatar_name.innerHTML = speaker_name;
+
+    var dummy_element = document.createElement("div");
+
+    avatar_container.appendChild(avatar_img);
+    avatar_container.appendChild(avatar_name);
+    avatar_container.appendChild(dummy_element);
+
+    document.getElementById("main-wrapper").appendChild(avatar_container);
+}
+
+function new_text_element(text, id) {
+    var text_container = document.createElement("div");
+    text_container.className = "text-container";
+    text_container.style.maxWidth = "500px";
+
+    var text_element = document.createElement("div");
+    text_element.id = id;
+    text_element.innerHTML = "<p>" + text + "</p>";
+
+    var dummy_element = document.createElement("div");
+
+    text_container.appendChild(text_element);
+    text_container.appendChild(dummy_element);
+
+    document.getElementById("main-wrapper").appendChild(text_container);
+}
+
+function new_transcription_time_element(time) {
+    var text_container = document.createElement("div");
+    text_container.className = "transcription-timing-container";
+    text_container.style.maxWidth = "500px";
+
+    var text_element = document.createElement("div");
+    text_element.innerHTML = "<span>WhisperLive - Transcription time: " + time + "ms</span>";
+
+    var dummy_element = document.createElement("div");
+
+    text_container.appendChild(text_element);
+    text_container.appendChild(dummy_element);
+
+    document.getElementById("main-wrapper").appendChild(text_container);
+}
+
+function new_llm_time_element(time) {
+    var text_container = document.createElement("div");
+    text_container.className = "llm-timing-container";
+    text_container.style.maxWidth = "500px";
+
+    var first_response_text_element = document.createElement("div");
+    first_response_text_element.innerHTML = "<span>Phi-2 first response time: " + time + "ms</span>";
+
+    var complete_response_text_element = document.createElement("div");
+    complete_response_text_element.innerHTML = "<span>Phi-2 complete response time: " + time + "ms</span>";
+
+    var dummy_element = document.createElement("div");
+
+    text_container.appendChild(first_response_text_element);
+    text_container.appendChild(complete_response_text_element);
+    text_container.appendChild(dummy_element);
+
+    document.getElementById("main-wrapper").appendChild(text_container);
+}
+
+function new_whisper_speech_audio_element(id, duration) {
+    var audio_container = document.createElement("div");
+    audio_container.className = "whisperspeech-audio-container";
+    audio_container.style.maxWidth = "500px";
+
+    var audio_div_element = document.createElement("div");
+    var audio_element = document.createElement("audio");
+    audio_element.style.paddingTop = "20px";
+
+    if (duration > 10)
+        duration = 10;
+    audio_element.src = "static/" + duration + ".mp3";
+
+    audio_element.id = id;
+    audio_element.onplay = function() {
+        console.log(this.id)
+        var id = this.id.split("-")[1] - 1;
+
+        if (audio_source) {
+            audio_source.disconnect();
+        }
+
+        audio_source = audioContext_tts.createBufferSource();
+        audio_source.buffer = audio_sources[id];
+        audio_source.connect(audioContext_tts.destination);
+        audio_source.start()
+    };
+    audio_element.onpause = function() {
+        this.currentTime = 0;
+        console.log(this.id)
+        var id = this.id.split("-")[1] - 1;
+        if (audio_source) {
+            audio_source.stop();
+        }
+    };
+    audio_element.controls = true;
+
+    audio_div_element.appendChild(audio_element);
+
+    var dummy_element_a = document.createElement("div");
+    var dummy_element_b = document.createElement("div");
+
+    audio_container.appendChild(dummy_element_a);
+    audio_container.appendChild(audio_div_element);
+    audio_container.appendChild(dummy_element_b);
+
+    document.getElementById("main-wrapper").appendChild(audio_container);
+}
+
+function new_whisper_speech_time_element(time) {
+    var text_container = document.createElement("div");
+    text_container.className = "whisperspeech-timing-container";
+    text_container.style.maxWidth = "500px";
+
+    var text_element = document.createElement("div");
+    text_element.innerHTML = "<span>WhisperSpeech response time: " + time + "ms</span>";
+
+    var dummy_element = document.createElement("div");
+
+    text_container.appendChild(text_element);
+    text_container.appendChild(dummy_element);
+
+    document.getElementById("main-wrapper").appendChild(text_container);
+}
+
+document.addEventListener('DOMContentLoaded', function() {
+    const queryString = window.location.search;
+    const urlParams = new URLSearchParams(queryString);
+    if (urlParams.has('name')) {
+        you_name = urlParams.get('name')
+    }
+ }, false);