sonata-asr 0.0.4

SONATA: SOund and Narrative Advanced Transcription Assistant

SONATA

SOund and Narrative Advanced Transcription Assistant

SONATA is an advanced Automatic Speech Recognition (ASR) system that captures the symphony of human expression by recognizing and transcribing both verbal content and emotive sounds.

Features

• High-accuracy speech-to-text transcription
• Recognition of emotive sounds and non-verbal cues
• Support for tags like <laugh>, <sigh>, <yawn>, <surprise>, <inhale>, <groan>, <cough>, <sneeze>, <sniffle>
• Open-source and extensible architecture

Installation

Install the package from PyPI:

pip install sonata-asr

Or install from source:

git clone https://github.com/hwk06023/SONATA.git
cd SONATA
pip install -e .

Usage Examples

Basic Transcription

from sonata.core.transcriber import IntegratedTranscriber

# Initialize the transcriber
transcriber = IntegratedTranscriber(asr_model="large-v3", device="cpu")

# Transcribe an audio file
result = transcriber.process_audio("path/to/audio.wav", language="en")

# Save the result to a file
transcriber.save_result(result, "output.json")

# Get the plain text transcript
plain_text = result["integrated_transcript"]["plain_text"]
print(plain_text)

Extracting Timestamps

from sonata.core.transcriber import IntegratedTranscriber

# Initialize the transcriber
transcriber = IntegratedTranscriber()

# Process audio with timestamps
result = transcriber.process_audio("path/to/audio.wav")

# Extract words with their timestamps
for item in result["integrated_transcript"]["rich_text"]:
    if item["type"] == "word":
        word = item["content"]
        start_time = item["start"]
        end_time = item["end"]
        print(f"{word}: {start_time:.2f}s - {end_time:.2f}s")

Processing with GPU Acceleration

from sonata.core.transcriber import IntegratedTranscriber

# Initialize with CUDA device
transcriber = IntegratedTranscriber(
    asr_model="large-v3",
    device="cuda",
    compute_type="float16"  # Use float16 for faster GPU processing
)

# Process audio
result = transcriber.process_audio("path/to/audio.wav")

Command Line Interface

SONATA provides a command-line interface for quick transcription:

# Basic usage
sonata-asr path/to/audio.wav

# Save output to specific file
sonata-asr path/to/audio.wav --output result.json

# Use GPU acceleration
sonata-asr path/to/audio.wav --device cuda

# Process audio with preprocessing (format conversion and silence trimming)
sonata-asr path/to/audio.wav --preprocess

# Split and process long audio files
sonata-asr path/to/audio.wav --split --split-length 30 --split-overlap 5

Inference Tools

The test directory contains additional inference tools for more advanced usage:

Basic Inference Script

# Process a single file
python test/infer.py path/to/audio.wav

# Specify output file and use GPU
python test/infer.py path/to/audio.wav -o output.json -d cuda

Advanced Processing

The advanced inference script supports batch processing and additional features:

# Process a directory of audio files in parallel
python test/advanced_infer.py path/to/audio_directory/ --batch --max-workers 4

# Preprocess audio before transcription
python test/advanced_infer.py path/to/audio.wav --preprocess

The preprocessing option performs two important operations:

1. Converts audio to WAV format for maximum compatibility
2. Trims silence from the beginning and end, improving accuracy and reducing processing time

See the inference tools documentation for more details.

Future Roadmap

SONATA is under active development. Here are some planned features and improvements:

Enhanced Multilingual Support

• Expand language coverage beyond current supported languages
• Improve transcription quality for non-English languages
• Add language auto-detection capabilities

ASR Model Diversity

• Support for additional ASR models beyond WhisperX
• Integration with local models for offline/private use
• Finetuned models for specific domains (medical, legal, etc.)

Advanced Emotive Detection

• Expand the range of detectable emotive events
• Improve accuracy of emotive event detection
• Add custom emotive event training capabilities

Performance Improvements

• Optimize processing for large audio files
• Enhance parallel processing capabilities
• Reduce memory footprint for resource-constrained environments

User Interface

• Add web-based UI for transcription monitoring
• Develop visualization tools for speech analytics
• Create interactive transcript editor

We welcome contributions in any of these areas!

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details. This license ensures that derivative works must also be open source and use the same license.

Acknowledgements

This project leverages the following key open source components:

• WhisperX - Fast speech recognition with word-level timestamps
• Laughter-Detection - Automatic detection of laughter in audio files (MIT License)

We are grateful to the developers and contributors of these libraries for their valuable work.