achatbot 0.0.7.11

An open source chat bot for voice (and multimodal) assistants

achatbot

achatbot factory, create chat bots with llm(tools), asr, tts, vad, ocr, detect object etc..

Project Structure

Feature

• demo

  • podcast

    # need GOOGLE_API_KEY in environment variables
    # default use language English
    
    # websit
    python -m demo.content_parser_tts instruct-content-tts \
        "https://en.wikipedia.org/wiki/Large_language_model"
    
    python -m demo.content_parser_tts instruct-content-tts \
        --role-tts-voices zh-CN-YunjianNeural \
        --role-tts-voices zh-CN-XiaoxiaoNeural \
        --language zh \
        "https://en.wikipedia.org/wiki/Large_language_model"
    
    # pdf
    # https://www.apple.com/ios/ios-18/pdf/iOS_18_All_New_Features_Sept_2024.pdf
    python -m demo.content_parser_tts instruct-content-tts \
        "/Users/wuyong/Desktop/iOS_18_All_New_Features_Sept_2024.pdf"
    
    python -m demo.content_parser_tts instruct-content-tts \
        --role-tts-voices zh-CN-YunjianNeural \
        --role-tts-voices zh-CN-XiaoxiaoNeural \
        --language zh \
        "/Users/wuyong/Desktop/iOS_18_All_New_Features_Sept_2024.pdf"
    

• cmd chat bots:

  • local-terminal-chat(be/fe)
  • remote-queue-chat(be/fe)
  • grpc-terminal-chat(be/fe)
  • grpc-speaker
  • http fastapi_daily_bot_serve (with chat bots pipeline)
  • bots with config see notebooks:
    • Run chat bots with colab notebook 🏃

• support transport connector:

  • pipe(UNIX socket),
  • grpc,
  • queue (redis),
  • websocket
  • TCP/IP socket

• chat bot processors:

  • aggreators(llm use, assistant message),
  • ai_frameworks
    • langchain: RAG
    • llamaindex: RAG
    • autoagen: multi Agents
  • realtime voice inference(RTVI),
  • transport:
    • webRTC:
      • daily: audio, video(image)
      • livekit: audio, video(image)
      • agora-audio
    • Websocket server
  • ai processor: llm, tts, asr etc..
    • llm_processor:
      • openai(use openai sdk)
      • google gemini(use google-generativeai sdk)
      • litellm(use openai input/output format proxy sdk)

• core module:

  • local llm:
    • llama-cpp (support text,vision with function-call model)
    • transformers(manual, pipeline) (support text,vision:🦙,Qwen2-vl,Molmo with function-call model)
    • mlx_lm
  • remote api llm: personal-ai(like openai api, other ai provider)

• AI modules:

  • functions:
    • search: search,search1,serper
    • weather: openweathermap
  • speech:
    • asr: sense_voice_asr, whisper_asr, whisper_timestamped_asr, whisper_faster_asr, whisper_transformers_asr, whisper_mlx_asr, lightning_whisper_mlx_asr(!TODO), whisper_groq_asr
    • audio_stream: daily_room_audio_stream(in/out), pyaudio_stream(in/out)
    • detector: porcupine_wakeword,pyannote_vad,webrtc_vad,silero_vad,webrtc_silero_vad
    • player: stream_player
    • recorder: rms_recorder, wakeword_rms_recorder, vad_recorder, wakeword_vad_recorder
    • tts: tts_chat,tts_coqui,tts_cosy_voice,tts_edge,tts_g
    • vad_analyzer: daily_webrtc_vad_analyzer,silero_vad_analyzer
  • vision
    • OCR(Optical Character Recognition):
      • PaddleOCR
      • GOT(the General OCR Theory)
    • Detector:
      • YOLO (You Only Look Once)
      • RT-DETR v2 (RealTime End-to-End Object Detection with Transformers)

• gen modules config(*.yaml, local/test/prod) from env with file: .env u also use HfArgumentParser this module's args to local cmd parse args

• deploy to cloud ☁️ serverless:

  • vercel (frontend ui pages)
  • Cloudflare(frontend ui pages), personal ai workers
  • fastapi-daily-chat-bot on cerebrium (provider aws)
  • fastapi-daily-chat-bot on leptonai
  • aws lambda + api Gateway
  • docker -> k8s/k3s
  • etc...

Service Deployment Architecture

UI (easy to deploy with github like pages)

• vite-react-rtvi-web-voice rtvi web voice chat bots, diff cctv roles etc, u can diy your own role by change the system prompt with DailyRTVIGeneralBot deploy it to cloudflare pages, access https://role-chat.pages.dev/
• chat-bot-rtvi-web-sandbox use this web sandbox to test config, actions with DailyRTVIGeneralBot
• ui/educator-client deploy it to cloudflare pages, access https://educator-client.pages.dev/
• ui/web-client-ui deploy it to cloudflare pages, access https://chat-client-weedge.pages.dev/
• vite-react-web-vision deploy it to cloudflare pages, access https://vision-weedge.pages.dev/
• nextjs-react-web-storytelling deploy it to cloudflare pages, access https://storytelling.pages.dev/
• websocket-demo: websocket audio chat bot demo

Server Deploy (CD)

• deploy/modal(KISS) 👍🏻
• deploy/leptonai 👍🏻
• deploy/cerebrium/fastapi-daily-chat-bot :)
• deploy/aws/fastapi-daily-chat-bot :|
• deploy/docker/fastapi-daily-chat-bot 🏃

Install

[!NOTE] python --version >=3.10 with asyncio-task

[!TIP] use uv + pip to run, install the required dependencies fastly, e.g.: uv pip install achatbot uv pip install "achatbot[fastapi_bot_server]"

pypi

python3 -m venv .venv_achatbot
source .venv_achatbot/bin/activate
pip install achatbot
# optional-dependencies e.g.
pip install "achatbot[fastapi_bot_server]"

local

git clone --recursive https://github.com/ai-bot-pro/chat-bot.git
cd chat-bot
python3 -m venv .venv_achatbot
source .venv_achatbot/bin/activate
bash scripts/pypi_achatbot.sh dev
# optional-dependencies e.g.
pip install "dist/achatbot-{$version}-py3-none-any.whl[fastapi_bot_server]"

Run chat bots

Run chat bots with colab notebook

Chat Bot	optional-dependencies	Colab	Device	Pipeline Desc
daily_bot livekit_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream, sense_voice_asr, groq | together api llm(text), tts_edge		CPU (free, 2 cores)	e.g.: daily | livekit room in stream -> silero (vad) -> sense_voice (asr) -> groq | together (llm) -> edge (tts) -> daily | livekit room out stream
generate_audio2audio	remote_queue_chat_bot_be_worker		T4(free)	e.g.: pyaudio in stream -> silero (vad) -> sense_voice (asr) -> qwen (llm) -> cosy_voice (tts) -> pyaudio out stream
daily_describe_vision_tools_bot livekit_describe_vision_tools_bot	e.g.: daily_room_audio_stream |livekit_room_audio_stream deepgram_asr, goole_gemini, tts_edge		CPU(free, 2 cores)	e.g.: daily |livekit room in stream -> silero (vad) -> deepgram (asr) -> google gemini -> edge (tts) -> daily |livekit room out stream
daily_describe_vision_bot livekit_describe_vision_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream sense_voice_asr, llm_transformers_manual_vision_qwen, tts_edge		- Qwen2-VL-2B-Instruct T4(free) - Qwen2-VL-7B-Instruct L4 - Llama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily | livekit room in stream -> silero (vad) -> sense_voice (asr) -> qwen-vl (llm) -> edge (tts) -> daily | livekit room out stream
daily_chat_vision_bot livekit_chat_vision_bot	e.g.: daily_room_audio_stream |livekit_room_audio_stream sense_voice_asr, llm_transformers_manual_vision_qwen, tts_edge		- Qwen2-VL-2B-Instruct T4(free) - Qwen2-VL-7B-Instruct L4 - Ll ama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily | livekit room in stream -> silero (vad) -> sense_voice (asr) -> llm answer guide qwen-vl (llm) -> edge (tts) -> daily | livekit room out stream
daily_chat_tools_vision_bot livekit_chat_tools_vision_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream sense_voice_asr, groq api llm(text), tools: - llm_transformers_manual_vision_qwen, tts_edge		- Qwen2-VL-2B-Instruct<br /> T4(free) - Qwen2-VL-7B-Instruct L4 - Llama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily | livekit room in stream -> silero (vad) -> sense_voice (asr) ->llm with tools qwen-vl -> edge (tts) -> daily | livekit room out stream
daily_annotate_vision_bot livekit_annotate_vision_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream vision_yolo_detector tts_edge		T4(free)	e.g.: daily | livekit room in stream vision_yolo_detector -> edge (tts) -> daily | livekit room out stream
daily_detect_vision_bot livekit_detect_vision_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream vision_yolo_detector tts_edge		T4(free)	e.g.: daily | livekit room in stream vision_yolo_detector -> edge (tts) -> daily | livekit room out stream
daily_ocr_vision_bot livekit_ocr_vision_bot	e.g.: daily_room_audio_stream | livekit_room_audio_stream sense_voice_asr, vision_transformers_got_ocr tts_edge		T4(free)	e.g.: daily | livekit room in stream -> silero (vad) -> sense_voice (asr) vision_transformers_got_ocr -> edge (tts) -> daily | livekit room out stream
daily_month_narration_bot	e.g.: daily_room_audio_stream groq |together api llm(text), hf_sd, together api (image) tts_edge		when use sd model with diffusers T4(free) cpu+cuda (slow) L4 cpu+cuda A100 all cuda	e.g.: daily room in stream -> together (llm) -> hf sd gen image model -> edge (tts) -> daily room out stream
daily_storytelling_bot	e.g.: daily_room_audio_stream groq |together api llm(text), hf_sd, together api (image) tts_edge		cpu (2 cores) when use sd model with diffusers T4(free) cpu+cuda (slow) L4 cpu+cuda A100 all cuda	e.g.: daily room in stream -> together (llm) -> hf sd gen image model -> edge (tts) -> daily room out stream
websocket_server_bot fastapi_websocket_server_bot	e.g.: websocket_server sense_voice_asr, groq |together api llm(text), tts_edge		cpu(2 cores)	e.g.: websocket protocol in stream -> silero (vad) -> sense_voice (asr) -> together (llm) -> edge (tts) -> websocket protocol out stream
daily_natural_conversation_bot	e.g.: daily_room_audio_stream sense_voice_asr, groq |together api llm(NLP task), gemini-1.5-flash (chat) tts_edge		cpu(2 cores)	e.g.: daily room in stream -> together (llm NLP task) -> gemini-1.5-flash model (chat) -> edge (tts) -> daily room out stream
fastapi_websocket_moshi_bot	e.g.: websocket_server moshi opus stream voice llm		L4	websocket protocol in stream -> silero (vad) -> moshi opus stream voice llm -> websocket protocol out stream

Run local chat bots

[!NOTE]

• run src code, replace achatbot to src, don't need set ACHATBOT_PKG=1 e.g.:

  TQDM_DISABLE=True \
       python -m src.cmd.local-terminal-chat.generate_audio2audio > log/std_out.log
  

• PyAudio need install python3-pyaudio e.g. ubuntu apt-get install python3-pyaudio, macos brew install portaudio see: https://pypi.org/project/PyAudio/

• llm llama-cpp-python init use cpu Pre-built Wheel to install, if want to use other lib(cuda), see: https://github.com/abetlen/llama-cpp-python#installation-configuration

• install pydub need install ffmpeg see: https://www.ffmpeg.org/download.html

1. run pip install "achatbot[local_terminal_chat_bot]" to install dependencies to run local terminal chat bot;

2. create achatbot data dir in $HOME dir mkdir -p ~/.achatbot/{log,config,models,records,videos};

3. cp .env.example .env, and check .env, add key/value env params;

4. select a model ckpt to download:

   • vad model ckpt (default vad ckpt model use silero vad)

   # vad pyannote segmentation ckpt
   huggingface-cli download pyannote/segmentation-3.0  --local-dir ~/.achatbot/models/pyannote/segmentation-3.0 --local-dir-use-symlinks False
   

   • asr model ckpt (default whipser ckpt model use base size)

   # asr openai whisper ckpt
   wget https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt -O ~/.achatbot/models/base.pt
   
   # asr hf openai whisper ckpt for transformers pipeline to load
   huggingface-cli download openai/whisper-base  --local-dir ~/.achatbot/models/openai/whisper-base --local-dir-use-symlinks False
   
   # asr hf faster whisper (CTranslate2)
   huggingface-cli download Systran/faster-whisper-base  --local-dir ~/.achatbot/models/Systran/faster-whisper-base --local-dir-use-symlinks False
   
   # asr SenseVoice ckpt
   huggingface-cli download FunAudioLLM/SenseVoiceSmall  --local-dir ~/.achatbot/models/FunAudioLLM/SenseVoiceSmall --local-dir-use-symlinks False
   

   • llm model ckpt (default llamacpp ckpt(ggml) model use qwen-2 instruct 1.5B size)

   # llm llamacpp Qwen2-Instruct
   huggingface-cli download Qwen/Qwen2-1.5B-Instruct-GGUF qwen2-1_5b-instruct-q8_0.gguf  --local-dir ~/.achatbot/models --local-dir-use-symlinks False
   
   # llm llamacpp Qwen1.5-chat
   huggingface-cli download Qwen/Qwen1.5-7B-Chat-GGUF qwen1_5-7b-chat-q8_0.gguf  --local-dir ~/.achatbot/models --local-dir-use-symlinks False
   
   # llm llamacpp phi-3-mini-4k-instruct
   huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf Phi-3-mini-4k-instruct-q4.gguf --local-dir ~/.achatbot/models --local-dir-use-symlinks False
   
   

   • tts model ckpt (default whipser ckpt model use base size)

   # tts chatTTS
   huggingface-cli download 2Noise/ChatTTS  --local-dir ~/.achatbot/models/2Noise/ChatTTS --local-dir-use-symlinks False
   
   # tts coquiTTS
   huggingface-cli download coqui/XTTS-v2  --local-dir ~/.achatbot/models/coqui/XTTS-v2 --local-dir-use-symlinks False
   
   # tts cosy voice
   git lfs install
   git clone https://www.modelscope.cn/iic/CosyVoice-300M.git ~/.achatbot/models/CosyVoice-300M
   git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git ~/.achatbot/models/CosyVoice-300M-SFT
   git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git ~/.achatbot/models/CosyVoice-300M-Instruct
   #git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git ~/.achatbot/models/CosyVoice-ttsfrd
   
   

5. run local terminal chat bot with env; e.g.

   • use dufault env params to run local chat bot

   ACHATBOT_PKG=1 TQDM_DISABLE=True \
       python -m achatbot.cmd.local-terminal-chat.generate_audio2audio > ~/.achatbot/log/std_out.log
   

Run remote http fastapi daily chat bots

1. run pip install "achatbot[fastapi_daily_bot_server]" to install dependencies to run http fastapi daily chat bot;

2. run below cmd to start http server, see api docs: http://0.0.0.0:4321/docs

   ACHATBOT_PKG=1 python -m achatbot.cmd.http.server.fastapi_daily_bot_serve
   

3. run chat bot processor, e.g.

   • run a daily langchain rag bot api, with ui/educator-client

   [!NOTE] need process youtube audio save to local file with pytube, run pip install "achatbot[pytube,deep_translator]" to install dependencies and transcribe/translate to text, then chunks to vector store, and run langchain rag bot api; run data process:

   ACHATBOT_PKG=1 python -m achatbot.cmd.bots.rag.data_process.youtube_audio_transcribe_to_tidb
   

   or download processed data from hf dataset weege007/youtube_videos, then chunks to vector store .

   curl -XPOST "http://0.0.0.0:4321/bot_join/chat-bot/DailyLangchainRAGBot" \
    -H "Content-Type: application/json" \
    -d $'{"config":{"llm":{"model":"llama-3.1-70b-versatile","messages":[{"role":"system","content":""}],"language":"zh"},"tts":{"tag":"cartesia_tts_processor","args":{"voice_id":"eda5bbff-1ff1-4886-8ef1-4e69a77640a0","language":"zh"}},"asr":{"tag":"deepgram_asr_processor","args":{"language":"zh","model":"nova-2"}}}}' | jq .
   

   • run a simple daily chat bot api, with ui/web-client-ui (default language: zh)

   curl -XPOST "http://0.0.0.0:4321/bot_join/DailyBot" \
    -H "Content-Type: application/json" \
    -d '{}' | jq .
   

Run remote rpc chat bot worker

1. run pip install "achatbot[remote_rpc_chat_bot_be_worker]" to install dependencies to run rpc chat bot BE worker; e.g. :
   • use dufault env params to run rpc chat bot BE worker

ACHATBOT_PKG=1 RUN_OP=be TQDM_DISABLE=True \
    TTS_TAG=tts_edge \
    python -m achatbot.cmd.grpc.terminal-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log

2. run pip install "achatbot[remote_rpc_chat_bot_fe]" to install dependencies to run rpc chat bot FE;

ACHATBOT_PKG=1 RUN_OP=fe \
    TTS_TAG=tts_edge \
    python -m achatbot.cmd.grpc.terminal-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log

Run remote queue chat bot worker

1. run pip install "achatbot[remote_queue_chat_bot_be_worker]" to install dependencies to run queue chat bot worker; e.g.:

   • use default env params to run

   ACHATBOT_PKG=1 REDIS_PASSWORD=$redis_pwd RUN_OP=be TQDM_DISABLE=True \
       python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log
   

   • sense_voice(asr) -> qwen (llm) -> cosy_voice (tts) u can login redislabs create 30M free databases; set REDIS_HOST,REDIS_PORT and REDIS_PASSWORD to run, e.g.:

    ACHATBOT_PKG=1 RUN_OP=be \
      TQDM_DISABLE=True \
      REDIS_PASSWORD=$redis_pwd \
      REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
      REDIS_PORT=14241 \
      ASR_TAG=sense_voice_asr \
      ASR_LANG=zn \
      ASR_MODEL_NAME_OR_PATH=~/.achatbot/models/FunAudioLLM/SenseVoiceSmall \
      N_GPU_LAYERS=33 FLASH_ATTN=1 \
      LLM_MODEL_NAME=qwen \
      LLM_MODEL_PATH=~/.achatbot/models/qwen1_5-7b-chat-q8_0.gguf \
      TTS_TAG=tts_cosy_voice \
      python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log
   

2. run pip install "achatbot[remote_queue_chat_bot_fe]" to install the required packages to run quueue chat bot frontend; e.g.:

   • use default env params to run (default vad_recorder)

   ACHATBOT_PKG=1 RUN_OP=fe \
       REDIS_PASSWORD=$redis_pwd \
       REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
       REDIS_PORT=14241 \
       python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log
   

   • with wake word

   ACHATBOT_PKG=1 RUN_OP=fe \
       REDIS_PASSWORD=$redis_pwd \
       REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
       REDIS_PORT=14241 \
       RECORDER_TAG=wakeword_rms_recorder \
       python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log
   

   • default pyaudio player stream with tts tag out sample info(rate,channels..), e.g.: (be use tts_cosy_voice out stream info)

    ACHATBOT_PKG=1 RUN_OP=fe \
        REDIS_PASSWORD=$redis_pwd \
        REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
        REDIS_PORT=14241 \
        RUN_OP=fe \
        TTS_TAG=tts_cosy_voice \
        python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log
   

   remote_queue_chat_bot_be_worker in colab examples :

   • sense_voice(asr) -> qwen (llm) -> cosy_voice (tts):

Run remote grpc tts speaker bot

1. run pip install "achatbot[remote_grpc_tts_server]" to install dependencies to run grpc tts speaker bot server;

ACHATBOT_PKG=1 python -m achatbot.cmd.grpc.speaker.server.serve

2. run pip install "achatbot[remote_grpc_tts_client]" to install dependencies to run grpc tts speaker bot client;

ACHATBOT_PKG=1 TTS_TAG=tts_edge IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_g IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_coqui IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_chat IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_cosy_voice IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client

Multimodal Interaction

audio (voice)

• stream-stt (realtime-recorder)

• audio-llm (multimode-chat)

• stream-tts (realtime-(clone)-speaker)

vision (CV)

• stream-ocr (realtime-object-detection)

more

• Embodied Intelligence: Robots that touch the world, perceive and move

License

torchchat is released under the BSD 3 license. (Additional code in this distribution is covered by the MIT and Apache Open Source licenses.) However you may have other legal obligations that govern your use of content, such as the terms of service for third-party models.