GPA: One Model for Speech Recognition, Text-to-Speech, and Voice Conversion

📢 Announcements

• 🚀 2026.04.29: GPA-v1.5 is here! GPA v1.5 Delivers near-SOTA TTS and ASR performance—in a single unified model. Start here →

• 🚀 2026.04.29: GPA-v1.5 ONNX Runtime is now available! Run ASR/TTS through ONNX CLI tools, a FastAPI service, or the browser UI with the new GPA-v1.5 ONNX runtime guide and runtime asset bundle.

• 🆕 2026.04.07: GPA-TTS FP16/FP32 Decoder — Higher-quality decoder options now available! For users with extra compute headroom, FP16 and FP32 SparkDetokenizer decoders are now available alongside INT8, delivering more stable and higher-quality speech synthesis. Selectable at runtime via CLI, API, or Web UI. Details →

• 📌 2026.03.31: GPA-TTS — Standalone lightweight TTS runtime released! Extracted from GPA with INT8/INT4 quantization for edge deployment. Among the smallest open-source TTS runtimes with voice cloning support! Details →

• 📚 GPA-v1.0 docs have moved. The original GPA-0.3B-preview quick start, deployment, benchmark, and evaluation pages now live in docs/GPA-v1.0.md.

tiny.mp4

_{All in one, built for all.
A single model delivering near-SOTA performance on TTS and ASR — fully unified, fully open!}

📖 Abstract

GPA stands for General Purpose Audio.

A student’s GPA unifies performance across diverse subjects—from Calculus to Gym—into a single metric. Likewise, our GPA model integrates the three core audio tasks—TTS, ASR, and Voice Conversion—into one auto-regressive transformer.

GPA-v1.5 now delivers near-SOTA performance on ASR and TTS in a single unified model, with VC support on the roadmap.

_{Figure 1. GPA unifies speech understanding and generation in a single autoregressive audio-language model.}

🗺️ Roadmap · 🚀 GPA-v1.5 Release · 🎙️ GPA-TTS · 🧭 GPA-v1.0 Archive · 📊 GPA-v1.5 Evaluation · 🔗 Citation

🗺️ Roadmap

Category	Item	Status
Core Features	Unified LLM-based audio generation & understanding	✅
	Native GPA-v1.5 Inference Pipeline	✅
	Native GPA-v1.5 Training Pipeline	✅
	GPA-v1.5 ONNX Runtime CLI/API/UI	✅
	GPA-v1.5 Interactive Demo	⬜
	GPA-v1.5 Basic Service Deployment (vLLM/FastAPI)	⬜
	Paper (ArXiv)	✅
Model Releases	GPA-0.3B-preview	✅
	GPA-v1.5 — major mainline release	✅
	GPA-TTS — Lightweight TTS runtime (INT8/FP16/FP32 + INT4 ONNX)	✅
GPA-v1.5 Next Steps	Voice Conversion native path	⬜
	Expanded deployment recipes	⬜
Frameworks	torch	✅
	vllm	✅
	llama-cpp	✅
	sglang	✅
	mlx-lm	✅
	rknn	⬜

🚀 GPA-v1.5 Release!

GPA-v1.5 is the new mainline release of GPA: a larger, cleaner, more capable unified audio model for ASR and TTS, with native PyTorch workflows and ONNX runtime deployment now available.

	GPA-v1.5
Checkpoint	Open-sourced on Hugging Face
Native Inference	Direct PyTorch / Hugging Face execution for ASR and TTS
Native Training	Fine-tuning and continued training with Hugging Face Trainer
ONNX Runtime	CLI inference, FastAPI service, browser UI, voice registration, and runtime validation
Planned	Voice Conversion support in the native v1.5 path

📖 GPA-v1.5 README →   |   🏋️ Training Guide →   |   🎧 Inference Guide →   |   ⚙️ ONNX Runtime Guide →   |   🤗 Download from HuggingFace

🎙️ GPA-TTS: Edge-Ready Voice-Cloning TTS

We noticed that TTS is by far the most popular feature in our online demo. While GPA-v1.5 ships as a larger unified model, we extracted the TTS component into a standalone, self-contained runtime:

	GPA-TTS
Quantization	Qwen INT4 + Detokenizer INT8 / FP16 / FP32 (ONNX Runtime)
Voice Cloning	Zero-shot, from a short reference audio
Decoder Precision	Selectable at runtime — INT8 (edge), FP16 (balanced), FP32 (highest quality)
Footprint	Among the smallest open-source TTS runtimes with cloning support
Optimized for	Local CPU inference (Mac / Linux / Edge)

📖 GPA-TTS README →   |   🤗 Download from HuggingFace

🧭 GPA-v1.0 Archive

The original GPA-0.3B-preview homepage has been preserved for users who still rely on the v1.0 quick start, deployment recipes, benchmarks, and evaluation tables.

	GPA-v1.0
Model	GPA-0.3B-preview
Docs	Original quick start, checkpoint download, inference, training, deployment, performance, and evaluation
Best for	Reproducing the initial release or maintaining existing v1.0 integrations
Downloads	Hugging Face and ModelScope links from the original release

📖 GPA-v1.0 README →   |   🤗 Hugging Face   |   🤖 ModelScope

📊 GPA-v1.5 Evaluation Metric Results

TTS Evaluation Table

Model	Open-Source	Model Size	test-zh CER (%) ↓	test-zh Sim (%) ↑	test-en WER (%) ↓	test-en Sim (%) ↑
Multi-Stage or NAR Methods
Human	-	-	1.26	75.5	2.14	73.4
Seed-TTS	❌	-	1.12	79.6	2.25	76.2
MiniMax-Speech	❌	-	0.83	78.3	1.65	69.2
F5-TTS	✅	0.3B	1.52	74.1	2.00	64.7
CosyVoice2	✅	0.5B	1.45	75.7	2.57	65.9
FireRedTTS2	✅	1.5B	1.14	73.2	1.95	66.5
Index-TTS2	✅	1.5B	1.03	76.5	2.23	70.6
VibeVoice-1.5B	✅	1.5B	1.16	74.4	3.04	68.9
VibeVoice-Realtime	✅	0.5B	-	-	2.05	63.3
HiggsAudio-v2	✅	3B	1.50	74.0	2.44	67.7
VoxCPM	✅	0.5B	0.93	77.2	1.85	72.9
GLM-TTS	✅	1.5B	1.03	76.1	-	-
GLM-TTS RL	✅	1.5B	0.89	76.4	-	-
Fun-CosyVoice3-0.5B-2512	✅	0.5B	1.21	78.0	2.24	71.8
Fun-CosyVoice3-0.5B-2512_RL	✅	0.5B	0.81	77.4	1.68	69.5
One-Stage AR Methods
Spark TTS	✅	0.5B	1.20	66.0	1.98	57.3
GPA-v1.5	✅	0.6B	1.03	70.2	1.43	63.5

ASR Evaluation Table

Note: ASR results on LibriSpeech, AISHELL-1, test_Meeting, and test_Net. WER (%) is reported for LibriSpeech; CER (%) is reported for AISHELL-1. The additional test_Meeting and test_Net columns follow the corresponding benchmark metric used in our evaluation.

Model	Model Size	LibriSpeech test-clean	LibriSpeech test-other	AISHELL-1	test_Meeting	test_Net
Whisper-S	0.24B	3.43	7.63	-	-	-
GPA-v1.5	0.6B	2.78	5.02	2.83	7.40	6.49
Fun-ASR-nano	0.8B	1.76	4.33	1.80	6.60	6.01
FireRed-ASR	1.1B	1.84	4.52	0.54	4.95	4.94
GLM-ASR-nano	1.5B	2.00	4.19	1.81	6.73	-
GLM-ASR-nano*	1.5B	2.17	4.43	2.17	8.21	6.33
Whisper-L	1.55B	1.86	3.43	4.72	18.39	11.89
Kimi-Audio	-	1.32	2.63	0.71	6.24	6.45
Step-Audio2	-	1.17	2.42	0.63	4.75	4.67
Seed-ASR	-	1.58	2.84	0.68	5.69	4.66
Seed-ASR*	-	2.80	5.69	1.63	7.07	4.84
Fun-ASR	7.7B	1.51	3.03	1.22	6.17	5.46

🔗 Citation

If you find GPA useful for your research or projects, please cite us:

@misc{cai2026unifyingspeechrecognitionsynthesis,
      title={Unifying Speech Recognition, Synthesis and Conversion with Autoregressive Transformers},
      author={Runyuan Cai and Yu Lin and Yiming Wang and Chunlin Fu and Xiaodong Zeng},
      year={2026},
      eprint={2601.10770},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2601.10770},
}