The lack of large, well-annotated dialectal speech data is a key bottleneck for robust ASR and TTS across regional varieties. This dataset addresses that gap by assembling over 10,000 hours of Sichuanese (Chuan‑Yu) speech from diverse real-world sources and providing multi-dimensional metadata and evaluation splits to enable reproducible research.
What Sets It Apart
- Scale and coverage: ~10,013 hours spanning short videos, entertainment, live streams, documentaries, audiobooks, drama, interviews, news and more — short videos account for the largest share, boosting real-world diversity.
- Rich, standardized metadata: single JSONL metadata file with utt_id, timestamps, duration, rover_result, transcription confidence, sample_rate, DNSMOS, SNR/WVMOS, speaker id, age/gender, emotion, domain and original content link — simplifies filtering and segment extraction.
- Quality tiers and benchmarks: segments labeled as Strong (confidence > 0.90, ~3,714 h) and Weak (0.60–0.90, ~6,299 h) to support supervised and semi-supervised setups; manually verified ASR/TTS eval sets included for fair comparison.
- Pipeline and transcription methodology: Chuan‑Pipeline automates segmentation, speaker clustering, forced-alignment and multi-modal punctuation; an LLM-GER (LLM Generative Error Correction + ROVER) step merges multiple ASR outputs (including Qwen3 for dialectal normalization) to raise transcription accuracy.
Who it's for and trade-offs
Great fit if you need a large open dialectal speech corpus to train or evaluate ASR/TTS models, study robustness across noisy real-world audio, or develop dialect normalization methods. Look elsewhere if you require fully license-free redistributable audio files (original media links are provided but audio access follows source constraints), extremely clean studio recordings, or very fine-grained speaker demographics beyond the provided age/gender estimates.
