auralis_portal2_announcer_ru_xttsv2_gpt

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.

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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.

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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")

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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")

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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.

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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

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Model Features 🛸

1. Speed & Efficiency:

   • Smart batching for rapid processing of long texts.
   • Memory-optimized for consumer GPUs.

2. Easy Integration:

   • Python API with support for synchronous and asynchronous workflows.
   • Streaming mode for continuous playback during generation.

3. Audio Quality Enhancements:

   • Background noise reduction.
   • Voice clarity and volume normalization.
   • Customizable audio preprocessing.

4. Multilingual Support:

   • Automatic language detection.
   • High-quality speech in 15+ languages.

5. Customization:

   • Voice cloning using short reference clips.
   • Adjustable parameters for tone, pacing, and language.

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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.

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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}
}