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

TL;DR GPA incorporates three speech tasks into one single model and this repo includes codes of training, fine-tuning and effecient deployment of GPA.

🆕 GPA-TTS Updates

GPA-TTS is a standalone, ultra-efficient TTS runtime distilled from GPA, designed for edge deployment.

• INT8/INT4 quantized: among the smallest open-source TTS runtimes • Runtime-selectable decoder: INT8 / FP16 / FP32 (quality vs. efficiency trade-off)
• Zero-shot voice cloning from short reference audio
• Fully local: no external LLM required
• Production-ready REST API with voice management

Built for developers deploying voice applications on resource-constrained devices.

📖 Abstract

GPA stands for General Purpose Audio.

In academia, a student’s GPA (Grade Point Average) serves as a unified metric that reflects performance across diverse subjects—ranging from Calculus and Philosophy to Gym class.

Similarly, our GPA model unifies the three major pillars of audio tasks—Text-to-Speech (TTS), Automatic Speech Recognition (ASR), and Voice Conversion (VC)—into a single auto-regreesive transformer.

• Our open-source content includes support for multiple frameworks and provides production-ready code suitable for cloud deployment.
• we include concise inference examples and training pipelines for research purpose.
• The released 0.3B model is also perfect for edge devices and edge deployment is to be released.

🔍 Model Overview

Figure 1: Architecture of the proposed GPA framework. The model utilizes a shared Large Language Model (LLM) backbone to unify three core audio tasks: Understanding (ASR), Generation (TTS), and Editing (Voice Conversion). Depending on the task, the model processes different combinations of inputs (Source Audio, Target Text, or Reference Audio) via Semantic and Acoustic modules to generate the corresponding text or audio output.

⚡ Model Performance

The following results are obtained by benchmarking services instantiated via the official deployment scripts, reflecting end-to-end performance in realistic serving scenarios rather than offline inference.

Among currently available open-source systems, our model is one of the few that natively supports both concurrent and streaming inference, while achieving performance comparable to the first tier of existing approaches.

💡Note

• TTFC: Time To First Chunk (TTS)
• TTFT: Time To First Token (ASR)
• RTF: Real-Time Factor (audio duration / synthesis time)

TTS Streaming Benchmark (Latency & Throughput)

Table 2. TTS Streaming RTF and Audio Duration

ASR Streaming Benchmark

Table 3. ASR Streaming Latency vs Concurrency

📊 Evaluation Metric Results

TTS Evaluation Table

ASR Evaluation Table

Note: ASR results on Librispeech and Aishell-1. WER (%) is reported for Librispeech, and CER (%) is reported for Aishell-1.

🙏 Acknowledgements

We borrowed a lot of code from the following excellent projects:

• Spark-TTS
• GLM-4-Voice
• Emilia
• FlashTTS
• Qwen

🔗 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}, 
}