HyperneuronAI Text-to-Speech Model
Multilingual Open-Source Text-to-Speech for Indian Languages
Model Details
Model Description
This is an open-source Text-to-Speech model developed by HyperneuronAI. The model is designed to generate natural speech from text and currently supports Hindi, Assamese, Punjabi, and Kannada. Quipus is 2x Faster than realtime and lower latency TTFB <120ms.
⚡Quipus Performance
| Metric | Value |
|---|---|
| First Audio | ~120 ms |
| Generation Speed | 2× Realtime |
| Languages | 4 |
Sneak Peak
In model Inference performance
🗺️ Roadmap
- 🌏 17+ Indian Languages
- 😊 Emotion-aware Speech
- 🌍 Arabic & English support as part of worldwide contribution
- ⚡ Streaming Backend Optimizations
- Training and Inference Code
The model uses a Qwen3 backbone and is intended for research, experimentation, and building voice AI applications. Users are free to fine-tune the model for custom voices and additional languages.
Voice cloning capabilities are not provided with this release to encourage responsible AI usage.
- Developed by: HyperneuronAI
- Funded by: HyperneuronAI
- Shared by: HyperneuronAI
- Model type: Text-to-Speech
- Backbone: Qwen3
- Languages: Hindi, Assamese, Punjabi, Kannada
- License: MIT
Audio Samples
🎙️ Available Voices
| Language | Speakers |
|---|---|
| 🇮🇳 Hindi | Raman(Male) • Anvita(Female) |
| 🇮🇳 Punjabi | Amanjit(Male) • Supreet(Female) |
| 🇮🇳 Assamese | Dipankar(Male) • Kavita(Female) |
| 🇮🇳 Kannada | Sindhura(Female) |
Model Sources
- Repository: Realtime optimized streaming inference code is planned for a future release.
- Demo: Coming soon.
Uses
Direct Use
This model can be used for Text-to-Speech generation in voice AI applications, including:
| ✅ |
|---|
| Conversational AI |
| AI Calling |
| Voice Assistants |
| Accessibility |
| Customer Support |
| IVR |
| Edge Devices |
| Research |
Fine-Tuning
Users may fine-tune this model for:
- New speakers
- Domain-specific speech styles
- Additional Indian languages
- Custom application-specific voices
Out-of-Scope Use
This model should not be used for unethical, harmful, deceptive, or illegal purposes, including but not limited to:
- Impersonation without consent
- Fraudulent voice generation
- Misinformation or manipulation
- Harassment or abuse
- Any use that violates applicable laws or platform policies
HyperneuronAI is not responsible for misuse of this model by third parties.
How to Get Started
Inference code and examples will be added soon.
# Example usage will be added soon.
Training Details
Training Data
The model was trained and fine-tuned on multilingual speech data covering Hindi, Assamese, Punjabi, and Kannada.
More details about the dataset composition, duration, speakers, and preprocessing pipeline will be added in a future update.
Training Procedure
The model was fine-tuned for text-to-speech generation using a Qwen3-based architecture.
More details about training configuration, tokenizer setup, audio codec/token representation, and optimization strategy will be added later.
Training Hyperparameters
Training hyperparameters will be added in a future update.
Evaluation
Testing Data
The model has been tested on multilingual text prompts across Hindi, Assamese, Punjabi, and Kannada.
A detailed benchmark set will be released in a future update.
Metrics
Formal evaluation metrics such as MOS, speaker similarity, intelligibility, word error rate, and latency benchmarks are not yet published.
Results
Evaluation results will be added after broader testing and benchmarking.
Technical Specifications
Model Architecture and Objective
This is a Text-to-Speech model with a Qwen3 backbone. The model is optimized to generate speech from input text in supported Indian languages.
Further architecture details will be added in future documentation.
Compute Infrastructure
Compute details will be added in a future update.
Hardware
Hardware details will be added in a future update.
Software
Software and inference dependencies will be added with the official inference code.
Limitations
- The model currently supports Hindi, Assamese, Punjabi, and Kannada.
- Output quality may vary depending on language, text normalization, punctuation, and input style.
- The model may struggle with code-mixed text, rare words, abbreviations, numerals, and domain-specific terminology.
- Voice cloning is not included in this release.
- Realtime streaming inference code is planned but not included yet.
Ethical Considerations
This model is released to support open-source development of Indian language voice AI. Users should ensure responsible deployment, obtain consent where required, and avoid deceptive or harmful applications.
Citation
Citation details will be added in a future release.
Authors
| Name |
|---|
| Nikhil Yadav |
| Ramanjit Singh |
| Pradeep Yadav |
| HyperneuronAI Research |
🤝 Contact
For questions, collaborations, or contributions, please contact HyperneuronAI
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