audio2chat 0.1.0

Generate chat data from multi-speaker audio files

Audio2Chat

Audio2Chat converts multi-speaker audio files into chat format using AssemblyAI for speaker diarization and optionally Whisper for enhanced transcription.

Features

• Speaker diarization and transcription using AssemblyAI
• Optional enhanced transcription using Whisper large-v3-turbo
• YouTube video download support
• Word-level timestamp support (can be used for speech-to-text and text-to-speech tasks)
• Structured chat format output

Installation

# Install from PyPI
pip install audio2chat

# Or install from source
git clone https://github.com/yourusername/audio2chat.git
cd audio2chat
pip install -e .

Requirements

• Python >=3.8
• FFmpeg (for YouTube downloads)
• CUDA-capable GPU (recommended for Whisper)

Install FFmpeg:

# Ubuntu/Debian
sudo apt update && sudo apt install ffmpeg

# MacOS
brew install ffmpeg

# Windows (using Chocolatey)
choco install ffmpeg

You need to have an Assembly AI account and an API key to use audio2chat. Once you setup an account, you can find the API key on your dashboard.

Usage

Command Line

Basic usage:

# Process local audio file
audio2chat input.wav --api-key YOUR_ASSEMBLYAI_KEY --output output_dir

# Process YouTube video
audio2chat "https://youtube.com/watch?v=xxxxx" --api-key YOUR_ASSEMBLYAI_KEY --output output_dir

All options:

audio2chat --help

required arguments:
  input                   Input audio file path or YouTube URL
  --api-key API_KEY      AssemblyAI API key

output settings:
  --output OUTPUT        Output directory for audio and chat data (default: output)
  --download-format {mp3,wav}
                        Audio format for YouTube downloads (default: wav)

transcription settings:
  --language LANGUAGE    Language code for transcription (default: en)
  --num-speakers NUM     Expected number of speakers (default: auto-detect)
  --use-whisper         Use Whisper for enhanced transcription (default: False)

chat generation settings:
  --min-segment-confidence CONF
                        Minimum confidence score to include segment (default: 0.5)
  --merge-threshold THRESH
                        Time threshold to merge adjacent utterances (default: 1.0)
  --min-duration DUR    Minimum duration for a chat segment (default: 0.5)
  --include-metadata    Include additional metadata in output (default: True)
  --include-word-timestamps
                        Include word-level timing information (default: False)

vocabulary settings:
  --word-boost [WORDS ...]
                        List of words to boost recognition for

other:
  --verbose, -v         Enable verbose logging

Python API

from audio2chat.pipeline import AudioChatPipeline
from audio2chat.youtube_downloader import download_audio

# For YouTube videos
audio_path = download_audio(
    "https://youtube.com/watch?v=xxxxx",
    output_dir="downloads",
    audio_format="wav"
)

# Initialize pipeline
pipeline = AudioChatPipeline(
    api_key="YOUR_ASSEMBLYAI_KEY",
    language="en",
    num_speakers=2,  # or None for auto-detect
    use_whisper=True,  # enable Whisper for better transcription
    include_word_timestamps=True
)

# Process file
chat_data = pipeline.process_file(audio_path, "output/chat.json")

Output Format

{
    "messages": [
        {
            "speaker": "A",
            "text": "Hello there!",
            "start": 0,
            "end": 1500,
            "words": [
                {
                    "text": "Hello",
                    "start": 0,
                    "end": 750,
                    "confidence": 0.98
                },
                {
                    "text": "there",
                    "start": 750,
                    "end": 1500,
                    "confidence": 0.95
                }
            ]
        }
    ],
    "metadata": {
        "num_speakers": 2,
        "speakers": ["A", "B"],
        "transcription": "whisper+assemblyai"
    }
}

Development

Run tests:

# Set up environment
export ASSEMBLYAI_API_KEY=your_key_here

# Add test audio file
cp your_test_audio.wav tests/test_data/input.wav

# Run tests
pytest tests/test_pipeline.py tests/test_chat_builder.py  # without Whisper
pytest tests/  # all tests including Whisper

License

This project is licensed under the MIT license.

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