metadata
language:
- en
- ru
pipeline_tag: text-to-speech
tags:
- text-to-speech
- axolotl-audio
library_name: axolotl-audio
inference: true
AxolotlAudio AA-2
Multilingual neural text-to-speech from AxolotlAudio.
Runtime metadata ships as a compact bundle (assets/runtime.aa1). Use the bundled SDK to materialize weights.
Architecture
| Component | Description |
|---|---|
| Backbone | Pretrained Qwen3-4B (~4B params) — text planning, long-context prosody, multilingual input |
| Acoustic head | Hybrid autoregressive module with multi-codebook RVQ (~400M params) |
| Codec | Neural codec (neural_codec.bin) for waveform reconstruction |
The text stack reuses the Qwen3 tokenizer (chat template, extended audio token slots). That is intentional — we inherit Qwen3's multilingual coverage and instruction-following substrate rather than training a tokenizer from scratch.
Design influences
AA-2 integrates ideas from several open speech / TTS research lines (not a fork of any single repo):
| Project | What we took |
|---|---|
| VITS2 | End-to-end vocoder-less training philosophy |
| Bert-VITS2 | Multilingual BERT-style text side-conditioning |
| GPT-SoVITS | Two-stage text → semantic → acoustic decomposition |
| MQTTS | Multi-quantizer acoustic token modeling |
| GPT-Fast | Fast secondary AR stack for residual codebooks at inference |
| Qwen3 | LLM backbone + tokenizer substrate |
Usage
from axolotl_audio import load_bundle
model = load_bundle("AxolotlAudio/aa-2")
Delivery markup
<soft> Can you hear me clearly?
<energetic> That sounds great!
License
Proprietary AxolotlAudio weights.