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 ⭐

python

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.

python

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:

bibtex

@misc{auralis2024,
  author = {AstraMind AI},
  title = {Auralis: High-Performance Text-to-Speech Engine},
  year = {2024},
  url = {https://huggingface.co/AstraMindAI/auralis}
}

AlekSt7/auralis_portal2_announcer_ru_xttsv2_gpt

Auralis 🌌

Model Details 🛠️