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AI CLI Tools

A command-line interface for access to core AI services: speech-to-text, large language models, and text-to-speech. This toolkit offers advanced AI capabilities directly from their terminal.

Key Features

  • Speech-to-Text (STT): Transcribe speech from your microphone to text.
  • Large Language Model (LLM): Interact with powerful language models for various tasks.
  • Text-to-Speech (TTS): Convert text to natural-sounding speech.

Highlights

  • Remote Server Support: All AI services can be installed on remote servers for enhanced performance and flexibility.
  • Modular Design: Each tool functions independently and can be combined for complex workflows.
  • Real-time Processing: Supports real-time transcription and speech synthesis.
  • Multiple LLM Providers: Switch between different LLM providers (LMStudio, Anthropic, OpenAI, Ollama).
  • Customizable TTS: Options for different TTS models and voices, including RVC support.

Project Status

This project is in early alpha stage. Servers aren't stable yet, also this current implementation only offers a working prototype with opinionated, streamlined commands. While customization options are limited at this phase, the foundation is set for potential expansion into a comprehensive CLI Swiss Army knife for STT, LLM, and TTS applications.

Roadmap

  • Enhance customization options for each tool
  • Expand API parameter accessibility
  • Improve documentation and usage examples
  • Gather user feedback for prioritizing new features

Tool Descriptions

Speech to Text (STT)

The STT command transcribes speech from your microphone to text.

Usage:

  • Basic: stt
  • File output: stt > file.txt

Examples:

C:\Dev\Test\ai-cli-tools>stt
This is detected speech.

C:\Dev\Test\ai-cli-tools>stt > transcription.txt
[Transcription saved to file]

Large Language Model (LLM)

The LLM command sends queries to a Large Language Model (using LM Studio on our server).

Usage:

  • Inline query: llm your question here
  • Piped input: echo your question | llm
  • Speech input: stt | llm
  • File output: llm your question > answer.txt
  • System message output: echo your query or question | llm your system message
  • Provider and model selection: llm your question --provider [lmstudio|anthropic|openai|ollama] --model [model_name]

Examples:

C:\Dev\Test\ai-cli-tools>llm When was Barack Obama born?
Barack Obama, the 44th President of the United States, was born on August 4, 1961.

C:\Dev\Test\ai-cli-tools>echo List 5 reputable sources for information on climate change: | llm > sources.txt

C:\Dev\Test\ai-cli-tools>llm Tell me about AI --anthropic
[Response from Anthropic's Claude model]

C:\Dev\Test\ai-cli-tools>llm Explain quantum computing --openai --model gpt-4
[Response from OpenAI's GPT-4 model]

Text-to-Speech (TTS)

The TTS command synthesizes text into speech in real-time.

Usage:

  • Inline text: tts text to speak
  • Piped input: echo text to speak | tts
  • Full pipeline: stt | llm | tts
  • File output: tts Hello, world! > output.wav
  • RVC toggle: tts Hello, world! --rvc

Examples:

C:\Dev\Test\ai-cli-tools>tts Let me hear this
Let me hear this

C:\Dev\Test\ai-cli-tools>echo hey there | llm answer short | tts
[Speaks the short answer]

C:\Dev\Test\ai-cli-tools>stt | llm translate to english, only return the translation | tts --rvc > translation.wav
[Transcribes speech, translates it to English, applies RVC, and saves to file]

Advanced Usage

You can combine these tools in various ways to create powerful workflows:

stt | llm --anthropic | tts --rvc
[Transcribes speech, processes it with Anthropic's LLM, and speaks the result using RVC]

echo Explain relativity | llm --openai --model gpt-4 | tts > explanation.wav
[Processes text with OpenAI's GPT-4, then speaks and saves the result]

stt | llm translate to english, only return the translation | tts --xtts-voice french_voice.wav
[Transcribes speech, translates it to English, and speaks the translation with a French voice]

Prerequisites

  • Python 3.10.9
  • CUDA 12.1

Installation

  1. Clone the Repository

    git clone https://github.com/KoljaB/ai_cli_tools.git
cd ai_cli_tools

  2. Create a Virtual Environment

    For Windows:

    _create_venv.bat

  3. Activate the Virtual Environment

    For Windows:

    _start_venv.bat

  4. Install Dependencies

    Execute in that virtual environment:

    pip install -r requirements.txt

  5. Upgrade PyTorch to Use GPU

    Execute in venv:

    pip install torch==2.3.1+cu121 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121

    (Adjust the CUDA version according to your system.)

  6. Install models for TTS and RVC

    Execute in venv or main environment:

    For Windows:

    _install_models.bat

  7. Install CLI commands

    Execute this on your main python or on your clients environment, NOT in the virtual environment.

    For Windows:

    pip install -r requirements_client.txt
_install_cli_commands.bat

  8. Start the servers

    Execute this on your main python or on your clients environment, NOT in the venv.

    For Windows:

    _start_stt_server.bat
_start_llm_server.bat
_start_tts_server.bat

    or execute

    stt-server
llm-server
tts-server

Environment Setup

To use certain LLM providers, set the following environment variables on the server machine:

  • For OpenAI: OPENAI_API_KEY
  • For Anthropic: ANTHROPIC_API_KEY

Notes

  • You can use CTRL+C to immediately abort any command (stt, llm, tts).
  • The system will prompt to start a local server if it's not running when you try to use a command.
  • Default models:
    • OpenAI: gpt-4o-mini
    • Anthropic: claude-3-sonnet-20240229
    • Ollama: llama3.1