Auralis 🌌
Model Details 🛠️
Model Name: Auralis
Model Architecture: Based on Coqui XTTS-v2
License:
- license: Apache 2.0
- base_model: XTTS-v2 Components Coqui AI License
Language Support: English, Spanish, French, German, Italian, Portuguese, Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese (Simplified), Hungarian, Korean, Japanese, Hindi
Developed by: AstraMind.ai
GitHub: AstraMind AI
Primary Use Case: Text-to-Speech (TTS) generation for real-world applications, including books, dialogues, and multilingual tasks.
Model Description 🚀
Auralis transforms text into natural, high-quality speech with exceptional speed and scalability. It is powered by Coqui XTTS-v2 and optimized for both consumer-grade and high-performance GPUs. Auralis is designed to meet real-world needs like long-text processing, voice cloning, and concurrent request handling.
Key Features:
- Warp-Speed Processing: Generate speech for an entire novel (e.g., Harry Potter) in ~10 minutes.
- Hardware Friendly: Requires <10GB VRAM on a single NVIDIA RTX 3090.
- Scalable: Handles multiple requests simultaneously.
- Streaming: Seamlessly processes long texts in a streaming format.
- Custom Voices: Enables voice cloning from short reference audio.
Quick Start ⭐
from auralis import TTS, TTSRequest
# Initialize the model
tts = TTS().from_pretrained("AstraMindAI/xtts2-gpt")
# Create a TTS request
request = TTSRequest(
text="Hello Earth! This is Auralis speaking.",
speaker_files=["reference.wav"]
)
# Generate speech
output = tts.generate_speech(request)
output.save("output.wav")
Ebook Generation 📚
Auralis converting ebooks into audio formats at lightning speed. For Python script, check out ebook_audio_generator.py.
def process_book(chapter_file: str, speaker_file: str):
# Read chapter
with open(chapter_file, 'r') as f:
chapter = f.read()
# You can pass the whole book, auralis will take care of splitting
request = TTSRequest(
text=chapter,
speaker_files=[speaker_file],
audio_config=AudioPreprocessingConfig(
enhance_speech=True,
normalize=True
)
)
output = tts.generate_speech(request)
output.play()
output.save("chapter_output.wav")
# Example usage
process_book("chapter1.txt", "reference_voice.wav")
Intended Use 🌟
Auralis is designed for:
- Content Creators: Generate audiobooks, podcasts, or voiceovers.
- Developers: Integrate TTS into applications via a simple Python API.
- Accessibility: Providing audio versions of digital content for people with visual or reading difficulties.
- Multilingual Scenarios: Convert text to speech in multiple supported languages.
Performance 📊
Benchmarks on NVIDIA RTX 3090:
- Short phrases (<100 characters): ~1 second
- Medium texts (<1,000 characters): ~5-10 seconds
- Full books (~100,000 characters): ~10 minutes
Memory Usage:
- Base VRAM: ~4GB
- Peak VRAM: ~10GB
Model Features 🛸
-
Speed & Efficiency:
- Smart batching for rapid processing of long texts.
- Memory-optimized for consumer GPUs.
-
Easy Integration:
- Python API with support for synchronous and asynchronous workflows.
- Streaming mode for continuous playback during generation.
-
Audio Quality Enhancements:
- Background noise reduction.
- Voice clarity and volume normalization.
- Customizable audio preprocessing.
-
Multilingual Support:
- Automatic language detection.
- High-quality speech in 15+ languages.
-
Customization:
- Voice cloning using short reference clips.
- Adjustable parameters for tone, pacing, and language.
Limitations & Ethical Considerations ⚠️
- Voice Cloning Risks: Auralis supports voice cloning, which may raise ethical concerns about misuse. Use responsibly and ensure proper consent.
- Accent Limitations: While robust for many languages, accents and intonations may vary based on the input.
Citation 📜
If you use Auralis in your research or projects, please cite:
@misc{auralis2024,
author = {AstraMind AI},
title = {Auralis: High-Performance Text-to-Speech Engine},
year = {2024},
url = {https://huggingface.co/AstraMindAI/auralis}
}