WenetSpeech-Yue: A Large-Scale Cantonese Speech Corpus with Multi-dimensional Annotation

📑 Paper    |    🐙 GitHub    |    🤗 HuggingFace
🖥️ HuggingFace Space    |    🎤 Demo Page    |    💬 Contact Us

Abstract

The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS are regarded as the most established and fundamental tasks. However, for Cantonese (Yue Chinese), spoken by approximately 84.9 million native speakers worldwide, limited annotated resources have hindered progress and resulted in suboptimal ASR and TTS performance. To address this challenge, we propose WenetSpeech-Pipe, an integrated pipeline for building large-scale speech corpus with multi-dimensional annotation tailored for speech understanding and generation. It comprises six modules: Audio Collection, Speaker Attributes Annotation, Speech Quality Annotation, Automatic Speech Recognition, Text Postprocessing and Recognizer Output Voting, enabling rich and high-quality annotations. Based on this pipeline, we release WenetSpeech-Yue, the first large-scale Cantonese speech corpus with multi-dimensional annotation for ASR and TTS, covering 21,800 hours across 10 domains with annotations including ASR transcription, text confidence, speaker identity, age, gender, speech quality scores, among other annotations. We also release WSYue-eval, a comprehensive Cantonese benchmark with two components: WSYue-ASR-eval, a manually annotated set for evaluating ASR on short and long utterances, code-switching, and diverse acoustic conditions, and WSYue-TTS-eval, with base and coverage subsets for standard and generalization testing. Experimental results show that models trained on WenetSpeech-Yue achieve competitive results against state-of-the-art (SOTA) Cantonese ASR and TTS systems, including commercial and LLM-based models, highlighting the value of our dataset and pipeline. The dataset, benchmark, and the ASR and TTS models built upon WenetSpeech-Yue will be open-sourced. Demos can be found in the supplementary material.

Promotional video

WenetSpeech-Pipe

WenetSpeech-Pipe is an automated pipeline specifically designed for building large-scale Cantonese datasets with multi-dimensional annotation. It consists of six components: (A) Audio Collection, (B) Speaker Attributes Annotation, (C) Speech Quality Annotation, (D) Automatic Speech Recognition, (E) Text Post-Processing, and (F) Recognizer Output Voting. The figure below provides an overview of the WenetSpeech-Pipe.

WenetSpeech-Yue

Dataset Overview

• Contains 21,800 hours of large-scale Cantonese speech corpus with rich annotations, the largest open-source resource for Cantonese speech research.
• Stores metadata in a single JSON file, including audio path, duration, text confidence, speaker identity, SNR, DNSMOS, age, gender, and character-level timestamps. Additional metadata tags may be added in the future.
• Covers ten domains: Storytelling, Entertainment, Drama, Culture, Vlog, Commentary, Education, Podcast, News, and Others.

Data Samples

ASR Leaderboard

TTS Demo

TTS System Performance Comparison

Evaluation Overview

• The table below presents both objective and subjective evaluation results of different TTS systems on the WSYue-TTS-eval benchmark. Objective metrics include Mixed Error Rate (MER) and speaker similarity (SIM) on both the Base and Coverage test sets. Subjective metrics include UTMOSv2, Intelligibility MOS (I-MOS), Speaker Similarity MOS (S-MOS), and Audio Naturalness MOS (A-MOS).
• Llasa-1B-Yue is our model trained on large-scale Cantonese data and achieves the best performance on most metrics.