Praha-Labs/Astra-TTS-Arch

Astra-TTS Architecture

Architecture design documents for Astra-TTS — a lightweight, high-quality text-to-speech system based on ZipVoice/Zipformer.

Documents

File	Description
model_a_slim.md	Model A — ZipVoice naively shrunk to ~55M params. Serves as baseline.
model_b_enhanced.md	Model B — ~55M params with architectural improvements (GQA, DepthSep Conv, Grouped Param Sharing, Dilated ConvNeXt, RoPE, etc.) + inference optimizations (EPSS, Midpoint ODE, SmoothCache).
benchmark_prd.md	Benchmark PRD — Full evaluation protocol comparing Original ZipVoice (123M) vs Model A (55M) vs Model B (55M) on LibriTTS.

Goal

Determine whether smart architectural changes at ~55M params can match or exceed a naive shrink, while enabling 6-8× faster inference through combined architecture + inference-time optimizations.

Architecture Summary

	Original ZipVoice	Model A (Slim)	Model B (Enhanced)
Params	123M	~55M	~55M
Approach	Full size	Naive shrink	Smart redesign
Key changes	—	Smaller dims/fewer layers	GQA, DepthSep FFN, Grouped Sharing, Dilated ConvNeXt, RoPE, ConvNeXt text refinement, no NLA
Inference	Euler 16 NFE	Euler 16 NFE	Midpoint 4-step + EPSS + SmoothCache
Expected speed	1×	~1.5×	~6-8×

References

• ZipVoice: arXiv:2506.13053
• Zipformer: arXiv:2310.11230
• Supertonic 3: Supertone/supertonic-3
• F5-TTS: arXiv:2410.06885

License

Apache-2.0

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

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• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Praha-Labs/Astra-TTS-Arch"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.