speech-assistant

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speech-assistant

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Desktop application for Linux and Windows that utilizes distil-whisper models from HuggingFace, to enable real-time offline speech-to-text dictation.

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# speech-assistant

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### Table of Contents
- [Welcome](#Welcome)
- [How to Use](#How-to-Use)
  - [Steps](#steps)
- [Configurations](#Configurations)
- [Notes and Considerations](#notes-and-considerations)
- [Contributions](#future-contributions)
- [Acknowledgements](#acknowledgements)

# Welcome
**Welcome to Speech-Assistant!**

This is a project to implement an offline Personal PC assistant that combines AI-powered speech-to-text dictation and conversational voice interactions with Large Language Models.

This is a desktop application working on both Linux and Windows that provides GUI interfaces for both communicating with the assistant, and controlling speech detection. It also includes the functionality of a real-time, offline speech-to-text dictation (or translation) program. It uses the distil-whisper models from HuggingFace which offers an accurate transcription of speech that is fully structured, complete with proper punctuation and syntax. Distil-whisper is based on OpenAI's Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% word error rate** on speech it has never seen before. The research is based on this [repo](https://github.com/huggingface/distil-whisper).

The speech-to-text assistant writes down spoken words directly to the keyboard cursor. To use it, hold down a hotkey combination of Windows key (Super) and Shift to begin, and let go to end the recording. Your speech will be transcribed (or translated) in real time and the transcription will be typed in for you at the keyboard cursor. The Whisper models are most effective transcribing a full sequence of speech rather than word-by-word, however, do check out [nerd-dictation](https://github.com/ideasman42/nerd-dictation) for the implementation of speech-to-text for the vosk models, also [WhisperLive](https://github.com/collabora/WhisperLive).

https://github.com/Mohamad-Hussein/speech-assistant/assets/115669425/57975e07-af13-4582-95df-ae8c8e049bfe

# How to Use
![Linux](https://img.shields.io/badge/Linux-F2F2F2) ![Windows](https://img.shields.io/badge/Windows-17b3d2)

You can get started on any operating system you would like. The program was tested in Pop-os (Ubuntu 22.04), Windows 10 and 11. Here is Anaconda's installation [instructions](https://docs.anaconda.com/free/anaconda/install/). If you are on Windows make sure to have access to the conda command using the Anaconda **cmd** terminal, or to source it directly. Nvidia and AMD have different packages needed to run Pytorch, please follow as appropriate to ensure smooth compatibility.

## Steps
1. **Install Ollama** from [here](https://ollama.com/download)
2. **Navigate to the speech-assistant repo** using the terminal (using the Anaconda CMD on Windows).

3. **Install dependencies.** Please use the command for your corresponding GPU type and operating system. Depending on your internet connection, this will take ~5-15 minutes (Type ```y``` and press enter when asked to download packages).
   - **Nvidia GPU:**
     ```bash
     conda env create -f env-cuda.yaml
     ```
   - **AMD GPU or any CPU Integrated Graphics on Windows:**
     ```powershell
     conda env create -f env-general-win.yaml
     ```

- **AMD GPU on Linux:**
     ```bash
     conda env create -f env-amd-linux.yaml
     ```
4. **Activate the conda environment.**
    ```bash
    conda activate speech-assistant
    ```
5. **Start running the program.**
    ```bash
    python main.py
    ```
6. **The program is now ready to use!**

7. **(Optional) Pull the assistant model of your choice** to use assistant capabilities, *llama3 is recommended*
    ```bash
    ollama pull llama3:8b
    ```
# Speech-to-text Configurations
The program will download the ```distil-whisper/distil-small.en``` model by default and cache it locally in a folder named 'model'. The model consumes ~600 MB of GPU memory, and to improve accuracy, you could choose a bigger model. You could change models in the `Options` menu. The available model choices are shown below.

| Model                                                                      | Params / M | Rel. Latency | Short-Form WER | Long-Form WER |
|----------------------------------------------------------------------------|------------|--------------|----------------|---------------|
| [whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en)         | 39       |          | [~15](https://arxiv.org/abs/2212.04356)        | [~15](https://arxiv.org/abs/2212.04356)          |
| [distil-small.en](https://huggingface.co/distil-whisper/distil-small.en)   | **166**    | 5.6          | 12.1           | 12.8          |
| [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) | 394    | **6.8**      | 11.1           | 12.4          |
| [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2)   | 756        | 5.8          | 10.1           | **11.6**      |
| [whisper-large-v2](https://huggingface.co/openai/whisper-large-v2)         | 1550       | 1.0          | **9.1**        | 11.7          |
| [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)         | 1550       |           |         |           |

*Please note that the distil models are currently English only, except for `whisper-large`, which supports transcription and translation capabilities of multiple languages.*

# Notes and Suggestions
- You can translate your speech to English in real-time using Whisper-Large by going to `options` and checking `Translate to English`
- Users with dedicated graphics cards will have a better experience running the big models.
- Make sure to locate your primary sound input device!
- There is a problem with using PowerShell, use cmd, and activate the conda environment.
- Installing with requirement.txt, package ffmpeg will be missing on model inference. This module can be downloaded with Anaconda with ```conda install ffmpeg -c pytorch.```
- For transcribing on Windows you can use its built-in dictation service with left windows + h. However, the whisper models can be useful for formatting expressive punctuation, and the implementation allows for private and quick dictation.

# Future contributions
## Features
- Adding more tools for agent
- Add sequential inference, for transcription as you talk (WhisperLive)
- Add the option to use whatever key bind of the user's choosing (GUI)
- Make Dockerfile for containers

# Acknowledgements

Distil-whisper paper:

- **Title:** Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labeling
- **Authors:** Sanchit Gandhi, Patrick von Platen, Alexander M. Rush
- **Year:** 2023
- **Link:** [ArXiv](https://arxiv.org/abs/2311.00430)

Sound effect: [soundsforyou](https://pixabay.com/users/soundsforyou-4861230/) on pixabay

User icon: [iconpacks](https://www.iconpacks.net/free-icon/user-3296.html)

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