🎙️ GLM‑TTS – High‑Quality Text‑to‑Speech Model

GLM‑TTS is an open‑source text‑to‑speech (TTS) synthesis system built using large language models (LLMs). It’s designed to produce expressive, high‑quality speech from text and includes features like zero‑shot voice cloning and emotion control. (Hugging Face)

Key points

GLM-TTS is an open-source text-to-speech synthesis system built using large language models, designed to produce expressive, high-quality speech from text with zero-shot voice cloning and emotion control.
Its zero-shot voice cloning can clone a speaker’s voice using only about 3-10 seconds of sample audio.
GLM-TTS uses a two-stage pipeline in which an LLM based on Llama turns textual input into speech token sequences and a Flow Matching model converts those tokens into mel-spectrograms and then waveform audio using a vocoder.
The system employs a multi-reward reinforcement learning framework, GRPO, to align the LLM’s generation strategy with natural prosody, emotion, and similarity to target voices.
It allows phoneme-level control by mixing phoneme input with text, supports streaming real-time inference, and is optimized for mixed Chinese and English text.

💡 Key Features

Zero‑shot voice cloning: Clone a speaker’s voice using only ~3–10 seconds of sample audio. (Hugging Face)
Emotion‑expressive speech: Uses reinforcement learning to improve emotional expressiveness and prosody. (Hugging Face)
High synthesis quality: Produces speech with low error rates (measured via character error rate comparisons). (Hugging Face)
Phoneme‑level control: You can mix phoneme input with text to control pronunciation more precisely. (Hugging Face)
Streaming inference: Supports real‑time generation, useful for interactive applications. (Hugging Face)
Bilingual support: Optimized for mixed Chinese and English text. (Hugging Face)

🧠 Architecture

GLM‑TTS uses a two‑stage pipeline:

An LLM (based on Llama) turns textual input into speech token sequences.
A Flow Matching model turns those tokens into mel‑spectrograms and then into waveform audio using a vocoder. (Hugging Face)

📈 Reinforcement Learning

The system employs a multi‑reward reinforcement learning framework (GRPO) to align the LLM’s generation strategy with natural prosody, emotion, and similarity to target voices. (Hugging Face)

🚀 Quick Usage

The project provides scripts and examples in its GitHub repo for installation and inference. You can clone the code, install dependencies, and run the inference scripts on your local machine. (Hugging Face)