whisper_dictation
Fast! Offline, privacy-focused, hands-free voice typing, 2-way AI voice chat, AI images, webcam, recorder, voice control, in under 4 GiB of VRAM.
Stars: 160
Whisper Dictation is a fast, offline, privacy-focused tool for voice typing, AI voice chat, voice control, and translation. It allows hands-free operation, launching and controlling apps, and communicating with OpenAI ChatGPT or a local chat server. The tool also offers the option to speak answers out loud and draw pictures. It includes client and server versions, inspired by the Star Trek series, and is designed to keep data off the internet and confidential. The project is optimized for dictation and translation tasks, with voice control capabilities and AI image generation using stable-diffusion API.
README:
Fast! Offline, privacy-focused, hands-free voice typing, AI voice chat, recorder, webcam, voice control, in under 1 Gb of VRAM when optimized for dictation alone. "The works" all AI features now running concurrently on a laptop with under 4 GiB of VRAM!
- Hands-free recording with
record.py, - Speech to text conversion by
whisper.cppWhisper.cpp, - Translate various languages,
- Launch & control apps, with
pyautogui, - Optional OpenAI
ChatGPT, Google Gemini, or custom chat server integration, - Optionally speak answers out loud with
mimic3. - Draw pictures with stable-diffusion-webui.
- Help! Get us a better video card (PayPal donation link).
Freedoms and responsibilities Free and open-source software comes with NO WARRANTIES. You have permission to copy and modify for individual needs in accordance with the included LICENSE.
The ship's computer. Inspired by the Star Trek television series. Talk to your computer. Have it answer back with clear, easy-to-understand speech. Network it throughout the ship. Use your voice to write Captain's Log entries when the internet is down, when satellites are busy, or in the far reaches of the galaxy, "where no man has gone before."
Translation. This app is optimized for dictation. It can do some translation into English. But that's not its primary task. To use it as a full-time translator, start whisper.cpp with --translate and language flags. Use ggml-medium.bin or larger language model in place of ggml-tiny.en.bin.
Voice control. The bot responds to commands.
Say, "Computer, on screen." A window opens up showing the webcam. Say "Computer, take a picture". A picture, "on_screen.jpg" is saved. Say, "Computer, search the web for places to eat". A browser opens up with a list of local restaurants. Say, "Computer, say hello to our guest". After a brief pause, there is a reply, either from your local machine, ChatGPT, or a local area chat server that you set up. A voice, mimic3 says some variation of, "Hello. Pleased to meet you. Welcome to our shop. Let me know how I can be of assistance". It's unique each time. Say, "Computer, open terminal". A terminal window pops up. Say "Computer, draw a picture of a Klingon warship". An image of a warship appears with buttons to save, print, and navigate through previously-generated images.
Fewer dependencies. We saved over 1Gb of downloads and hours of setup by eliminating torch, cuda, cudnn, ffmpeg dependencies. Those older versions can be found in the legacy branch. Get just the main branch to save time.
git clone -b main --single-branch https://github.com/themanyone/whisper_dictation.git
GStreamer is necessary to record temporary audio clips for sending to your local whisper.cpp speech to text (STT) server. It should be available from various package managers.
pip install -r whisper_dictation/requirements.txt
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docs
ln -s server ~/.local/bin/whisper_cpp_server # just put it somewhere in $PATHwhisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777
./whisper_cpp_client.pyIf VRAM is scarce, quantize ggml-tiny.en.bin according to whisper.cpp docs. Or use -ng option to avoid using VRAM altogether. It will lose some performance. But it's not that noticeable with a fast CPU.
If whisper_cpp_server is slow or refuses to start, reboot. Or try and reload the crashed NVIDIA uvm module sudo modprobe -r nvidia_uvm && sudo modprobe nvidia_uvm.
Edit whisper_cpp_client.py client to change server locations from localhost to wherever they reside on the network. You can also change the port numbers. Just make sure servers and clients are in agreement on which port to use.
Test clients and servers.
./whisper.cpp -l en -m ./models/ggml-tiny.en.bin samples/jfk.wav
ln -sf $(pwd)/main whisper_cpp
ln -sf $(pwd)/server whisper_cpp_server
./whisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777Hosting large language models provides some limited access to information, even if the internet is down. But don't trust the answers. Keep all logs and communications behind a good firewall for privacy. And network security is another topic...
Supposing any chat server will do. Many use llama.cpp behind the scenes. Since it is not susceptible to "pickle" code injection, we don't have to recommend special safetensors models. So we'll use that. Understand that this is for a local home server. Running a public server comes with a catalog of other concerns so those folks might prefer to host these in a container, and/or on somebody else's cloud.
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docsFinding free models. Save hundreds on annual subscriptions by running your own AI servers for every task. Look at the leaderboard to see which models perform best in the categories you want. As a rule of thumb, quantized 7B models are about the maximum our 4GiB VRAM can handle. Search for quantized models in .gguf format, or try the ones on our page.
AI Safety. Monitor kids' usage. Be aware that these models, made by the community, are under active development. They are not guaranteed safe for all ages.
High VRAM usage. Use a tool like nvtop (availabl from package managers) to keep an eye on available VRAM.
Context window. The context window is the size of input plus output. Running large models with a large context window (-ctx 8192), while sending graphics layers to the GPU (-ngl 33) for speed, uses lots of VRAM. They might even crash. With small models, it is okay to run the llama-server with -ngl 33 for high speed/high memory usage. With large (> 3GiB) models, use lower values, such as -ngl 17 and -ctx 512, or avoid -ngl altogether.
Help! Get this project off the ground with some better hardware (PayPal donation link).
./llama-server -m models/gemma-2-2b-it-q4_k_m.gguf -c 2048 -ngl 33 --port 8888Use the above API endpoint by simply saying "Computer... What is the capital of France!" etc. Or navigate to its handy web interface at http://localhost:8888 and dictate into that. From there you can adjust settings like temperature to make it more creative, or more strict with its fact checking and self censorship.
If AI is speaking, turn volume down or relocate the mic so it doesn't interact with itself.
Mimic3. If you follow the instructions to configure mimic3 as a service on any linux computer or Raspberry Pi on the network, Speech Dispatcher will speak answers out loud. It has an open port that other network users can use to enable speech on their devices. But they can also make it speak remotely. So it is essentially a Star Trek communicator that works over wifi. Follow the instructions for setting up mimic3 as a Systemd Service.
Developer notes. The mimic3-server is already lightening-fast on CPU. Do not bother compiling it with --cuda flag, which requires old onnxruntime-gpu that is not compatible with CUDA 12+ and won't compile with nvcc12... We got it working! And it just hogs all of VRAM and provides no noticeable speedup.
Female voice. For a pleasant, female voice, use mimic3-download to obtain en_US/vctk_low To accommodate this change, we already edited the params line in our mimic3_client.py, and commented the other line out, like so.
# params = { 'text': text, "lengthScale": "0.6" }
params = { 'text': text, "voice": "en_US/vctk_low" }
So just change it back if you want the default male voice, other languages, or to adjust speech characteristics to taste.
Export the OPENAI_API_KEY and it will give preference to answers from ChatGPT. Edit .bashrc, or another startup file:
export OPENAI_API_KEY=<my API key>We heard OpenAI also has enterprise endoints for ChatGPT that offer some privacy and security. But we have never been contacted by OpenAI and make no claims about its proprietary domains.
- Sign up for a GOOGLE_API_KEY
pip install -q -U google-generativeaiexport GENAI_KEY=<YOUR API_KEY>
Now with sdapi.py, images may be generated locally, or across the network. Requires stable-diffusion-webui. Start webui.sh on the server with --api options. Also use --medvram or --lowvram if your video is as bad as ours. If using remotely, configure our sdapi.py client with the server's address.
Start stable-diffusion webui
webui.sh --api --medvramThe computer responds to commands. You can also call him Peter. (Or Samantha, if using female voice).
Mute button. There is no mute button. Say "pause dictation" to turn off text generation. It will keep listening to commands. Say, "Thank you", "resume dictation", or "Computer, type this out" to have it start typing again. Say "stop listening" or "stop dictation" to quit the program entirely. You could configure a button to mute your mic, but that is no longer necessary.
These actions are defined in whisper_dictation.py. See the source code for the full list. Feel free to edit them too!
Try saying:
- Computer, on screen. (or "start webcam"; opens a webcam window).
- Computer, take a picture. (saves to /tmp/on_screen.jpg)
- Computer, off screen. (or "stop webcam")
- Computer, record audio (records audio.mp3)
- Computer, open terminal.
- Computer, go to thenerdshow.com. (or any website).
- Computer, open a web browser. (opens the default homepage).
- Computer, show us a picture of a Klingon battle cruiser.
- Page up.
- Page down.
- Undo that.
- Copy that.
- Paste it.
- Pause dictation.
- Resume dictation.
- New paragraph. (also submits chat forms :)
- Peter, tell me about the benefits of relaxation.**
- Peter, compose a Facebook post about the sunny weather we're having.
- Stop dictation. (quits program).
whisper_cpp_client.py: A small and efficient Python client that connects to a running Whisper.cpp server on the local machine or across the network.
record.py: Almost hands-free, sound-activated recorder. You can run it separately. It creates a file named audio.wav. Supply optional command line arguments to change the file name, quality, formats, add filters, etc. See ./record.py -h for help. Some formats require gst-plugins-bad or gst-plugins-ugly, depending on your distribution.
This update provides a powerful option that lets you insert various plugins, mixers, filters, controllers, and effects directly into the GStreamer pipeline. See the G-streamer documentation for details. Many audio and video plugins are available. Run gst-inspect-1.0 for a list. The following records in a high quality, lossless format with echo effect and dynamic range compression.
./record.py -gq 'audioecho delay=250000000 intensity=0.25 ! audiodynamic' echo.flac
on_screen.py A simple python library to show and take pictures from the webcam.
sdapi.py The client we made to connect to a running instance of stable-diffusion-webui. This is what gets called when you say, "Computer...Draw a picture of a horse."
Various test files, including:
mimic3_client.py: a client to query and test mimic3-server voice output servers.
test_cuda.py: torch, pytorch, cuda, and onnxruntime are no longer required for dictation. But you can still test them here, since mimic3 uses onnxruntime for text to speech.
Stable-Diffusion. Stable-Diffusion normally requires upwards of 16 GiB of VRAM. But we were able to get it running with a mere 2 GiB using the --medvram or --lowvram option with Stable Diffusion Web UI.
I want it to type slowly. We would love to have it type text slowly, but typing has become unbearably-slow on sites like Twitter and Facebook. The theory is they are using JavaScript to restrict input from bots. But it is annoying for fast typists too. If occasional slow typing doesn't bother you, change the code to use pyautogui.typewrite(t, typing_interval) for everything, and set a typing_interval to whatever speed you want.
Whisper Dictation is not optimized for making captions or transcripts of pre-recorded material. Use Whisper.cpp or whisper-jax for that. They too have a server with a web interface that makes transcripts for voice recordings and videos.
If you want real-time AI captions translating everyone's conversations in the room into English. If you want to watch videos with accents that are difficult to understand. Or if you just don't want to miss what the job interviewer asked you during that zoom call... WHAT???, check out my other project, Caption Anything. And generate captions as you record live "what you hear" from the audio monitor device (any sounds that are playing through the computer).
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This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.
obs-localvocal
LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.