whispercpp-kit 0.1.6

A toolkit for whisper.cpp with audio processing and model management

WhisperCPP Kit 🎙️

🚀 A Python wrapper around whisper.cpp with model management and helper features.

✨ Features

• 🔄 Automatic building and setup of whisper.cpp
• 🎯 Simple, intuitive Python API
• 🔧 Built-in model management
• 🚦 Clear error messages and dependency checks
• 🎵 Automatic audio format conversion
• 🧵 Multi-threading support
• 🐳 Docker support
• 🎯 Support for custom and fine-tuned models
• ⚡ Cached builds for faster subsequent inference

📋 System Requirements

Before installing whispercpp_kit, ensure you have these system-level dependencies:

Required dependencies 🛠️

• git
• cmake
• ffmpeg
• make
• g++/gcc (C++ compiler)
• Build essentials

Installation commands 📦

Ubuntu/Debian

sudo apt update
sudo apt install git cmake ffmpeg build-essential

MacOS

brew install git cmake ffmpeg gcc make

CentOS/RHEL

sudo yum update
sudo yum groupinstall "Development Tools"
sudo yum install git cmake ffmpeg gcc-c++ make

⚠️ Windows is currently not supported. Please use WSL (Windows Subsystem for Linux) with Ubuntu.

🚀 Quick start

Installation

pip install whispercpp_kit

Basic usage

from whispercpp_kit import WhisperCPP

# Initialize with default model
whisper = WhisperCPP(model_name="tiny.en")

# First-time setup (automatically done on first transcribe)
whisper.setup()

# Transcribe audio
text = whisper.transcribe("audio.mp3")
print(text)

Advanced configuration

# Using standard models
whisper = WhisperCPP(
    model_name="tiny.en",
    num_threads=8,        # Control threads number
    verbose=True,         # Enable verbose output
    cache_dir="./cache"   # Custom cache directory
)

# Using custom or fine-tuned models
whisper = WhisperCPP(model_path="/path/to/your/fine-tuned-model.bin")

# The library caches the built whisper.cpp source code
# This means subsequent runs will be faster as compilation is skipped

🐳 Troubleshooting

Rebuilding whisper.cpp

If you encounter issues with the whisper.cpp binary, you can force a rebuild:

import shutil
from whispercpp_kit import WhisperCPP

whisper = WhisperCPP(model_name="tiny.en")
# Force rebuild of whisper.cpp
shutil.rmtree(whisper.base_path)
whisper.setup()

Common Issues

1. Binary Deprecation Warning: If you see a warning about the 'main' binary being deprecated, rebuild whisper.cpp using the steps above. The latest version uses 'whisper-cli' instead.

2. Transcription Failures: Ensure you have all required dependencies installed and sufficient permissions to execute the binary.

3. Audio Format Issues: The library automatically converts audio files using ffmpeg. Make sure ffmpeg is properly installed if you encounter audio-related errors.

🐳 Docker support

Docker Instructions

git clone https://github.com/s-emanuilov/whispercpp_kit
cd whispercpp_kit/examples/docker

# Build the image
docker build -t whispercpp_kit .

# Run with default model (base.en)
docker run -v $(pwd):/app/audio whispercpp_kit your_audio.mp3

# Using specific model
docker run -v $(pwd):/app/audio whispercpp_kit your_audio.mp3 tiny.en

See examples/docker/README.md for more details.

📝 License

MIT License - feel free to use in your projects!

🤝 Contributing

Contributions are welcome! Feel free to submit issues and pull requests.