---
title: 'WenetSpeech-Chuan: Sichuanese Dialect Corpus'
url: https://www.emergentmind.com/topics/wenetspeech-chuan
type: topic
---

# WenetSpeech-Chuan: Sichuanese Dialect Corpus

WenetSpeech-Chuan is a 10,000-hour, richly annotated open-source corpus for Sichuanese dialect speech, accompanied by a complete data processing framework, “Chuan-Pipeline,” and manually verified evaluation benchmarks for automatic speech recognition (ASR) and text-to-speech (TTS). It is positioned as the largest open-source corpus for Sichuanese dialects and is intended to address the chronic lack of large-scale dialect resources for speech technology, especially for a dialect group spoken by roughly 120 million people. The release includes the corpus itself, WenetSpeech-Chuan-Eval benchmarks, trained models, and processing recipes on a public project page [2509.18004].

## 1. Corpus scope and composition

WenetSpeech-Chuan is organized by transcription confidence rather than by a single homogeneous training split. The corpus contains **10,013 hours** of raw audio, partitioned into **Strong Label** and **Weak Label** subsets according to ASR confidence. Strong-label data is intended for fully supervised training, whereas weak-label data is preserved for semi-supervised or self-supervised use [2509.18004].

| Set | Confidence range | Duration (h) |
|---|---:|---:|
| Strong Label | [0.9, 1.0] | 3,714 |
| Weak Label | [0.6, 0.9) | 6,299 |
| Total | / | 10,013 |

The corpus targets **Sichuan-Chongqing dialects** within the broader Sichuanese group. The design premise is that these dialects differ from Standard Mandarin in the **tonal system**, **vocabulary**, and **grammar**, so the resource is framed as a dialect corpus rather than as a simple accented-Mandarin extension [2509.18004]. This distinguishes it from much smaller resources such as MagicData-Conversation and MagicData-Daily-Use, and from corpora such as KeSpeech that include Southwestern accents but are characterized here as accented Mandarin rather than a true dialectal resource.

The data is drawn from highly heterogeneous web sources. The domain distribution is dominated by **short videos (52.83%)**, **entertainment (20.08%)**, and **live streams (18.35%)**, with smaller but important coverage of documentaries, audiobooks, interviews, news, reading, and drama. Long recordings are segmented by VAD into **5–25 second clips**, which preserves natural spontaneity while making the data tractable for large-scale ASR and TTS training [2509.18004].

Audio quality is not “studio-clean” by construction. Instead, the corpus intentionally preserves realistic acoustic variability from user-generated and broadcast-like media. Quality control is enforced through **Word-level Virtual MOS (WVMOS)** based on SNR and duration features, and the resulting quality distribution peaks around **WVMOS 3.0–3.5**, indicating mostly moderate-to-high quality rather than narrow, curated laboratory conditions [2509.18004].

## 2. Annotation schema and metadata

A central feature of WenetSpeech-Chuan is that it is **richly annotated** beyond transcription alone. Each utterance carries multiple labels spanning speaker identity, demographic or paralinguistic attributes, emotion, and domain. This makes the corpus suitable not only for ASR and TTS, but also for controlled analysis of dialect robustness across speaker types and speaking conditions [2509.18004].

Speaker identity is obtained by first isolating single-speaker segments with **pyannote** diarization and then extracting speaker embeddings with **CAM++**. Clustering assigns a consistent speaker ID to utterances attributed to the same individual. The paper does not report a total speaker count, but speaker tracking is a first-class annotation layer in the release.

Paralinguistic labels include **gender**, **age**, and **emotion**. Gender is assigned by a pre-trained classifier with a reported **98.7% accuracy**. Age is discretized into five stages: **children**, **teenager**, **young**, **middle-aged**, and **old**. Emotion is inferred by combining **Emotion2vec** and **SenseVoice** predictions through majority voting over seven classes: **happy**, **angry**, **sad**, **neutral**, **fearful**, **surprised**, and **disgusted** [2509.18004].

Domain labels are also retained. The same nine-domain ontology used in corpus construction is carried into evaluation design, which allows systematic contrast between clean, read speech and noisy, spontaneous media speech. A practical consequence is that evaluation can be stratified into “Easy” and “Hard” subsets without collapsing all dialectal performance into a single average.

Transcriptions are automatically generated and then punctuation is added through a multimodal process. Punctuation is not treated as a purely textual post-processing problem. Instead, **Kaldi force alignment** provides word timestamps and pause durations, while a **BiLSTM punctuation model** predicts punctuation types. The system treats pauses of **at least 0.25 s** as candidate short pauses corresponding to commas and pauses of **at least 0.5 s** as candidate long pauses corresponding to periods, question marks, or exclamation marks. These thresholds are iteratively refined with human feedback so that punctuation tracks real prosodic boundaries rather than only text-based syntax [2509.18004].

## 3. Chuan-Pipeline

Chuan-Pipeline is the processing framework used to transform raw web audio into the released corpus. It combines source selection, segmentation, diarization, quality assessment, LLM-assisted transcription correction, and multimodal punctuation prediction into a single dialect-oriented workflow [2509.18004].

The first stage is **preprocessing and labeling**. Metadata is mined from online video and streaming platforms, and candidate content is filtered to identify material likely to contain Sichuanese. The paper states that candidate streams then undergo **manual verification** to confirm dialect presence. After this, VAD segments long recordings into **5–25 second** clips, and pyannote diarization is used to isolate single-speaker segments. Speaker embeddings from CAM++ support clustering and speaker-ID assignment.

The second stage is **quality assessment**. Chuan-Pipeline computes SNR- and duration-based features and combines them into **WVMOS**. Segments with low WVMOS are discarded. This stage is important because the source domains include short-video and live-stream conditions where background noise, compression artifacts, and music contamination can be substantial.

The third stage is **LLM-GER**, described as **Generative Error Correction based ROVER**. Each clip is decoded by three ASR systems: **FireRed-ASR**, **SenseVoice-small**, and **TeleASR**. Their outputs are then fed to **Qwen3**, which is prompted to exploit its Sichuanese dialectal knowledge while preserving semantics and token length. The resulting corrected transcript serves as a fourth hypothesis, and the system computes a confidence score from the four transcriptions to decide whether a sample belongs in the strong-label or weak-label subset. On held-out data, the paper reports that LLM-GER yields **about 15% transcription accuracy improvement** over individual systems [2509.18004].

The fourth stage is **multimodal punctuation prediction**. Audio-side force alignment yields word timestamps and pause intervals, and a text-side BiLSTM predicts punctuation class. The combined system produces punctuated transcripts that are better aligned with speaking rhythm and thus more useful for downstream TTS than plain unpunctuated ASR text.

A plausible implication is that Chuan-Pipeline is designed not merely to maximize transcript correctness, but to preserve a training signal that is simultaneously useful for recognition, synthesis, and speaker-conditioned modeling. That orientation distinguishes it from pipelines optimized only for raw ASR throughput.

## 4. Evaluation benchmarks

WenetSpeech-Chuan-Eval consists of manually verified ASR and TTS benchmarks designed to expose both ordinary and difficult dialectal conditions. The ASR benchmark, **WSC-Eval-ASR**, totals **9.7 hours** and is divided into an **Easy** subset of **8.55 hours** and a **Hard** subset of **1.15 hours** [2509.18004].

| Benchmark subset | Main domain(s) | Duration (h) |
|---|---|---:|
| WSC-Eval-ASR Easy | audiobooks, reading | 8.55 |
| WSC-Eval-ASR Hard | short videos, entertainment, drama | 1.15 |
| WSC-Eval-ASR Total | / | 9.7 |

The Easy portion emphasizes relatively clean, read speech, while the Hard portion emphasizes spontaneous or noisy media speech. All ASR evaluation samples retain **age**, **gender**, and **emotion** labels, enabling fine-grained analysis beyond a single corpus-level CER.

The TTS benchmark, **WSC-Eval-TTS**, is divided into **easy** and **hard** conditions as well. WSC-Eval-TTS-easy contains sentences with Sichuanese dialectal words across diverse domains. WSC-Eval-TTS-hard includes **long sentences** and **LLM-generated sentences** such as **tongue twisters**, **folk sayings**, and **emotional speech**. For prompt conditioning and speaker-similarity evaluation, the benchmark uses **10 speakers**—**5 male** and **5 female**—from MagicData and internal recordings, with **200 sentences per speaker** [2509.18004].

ASR is evaluated primarily with **CER**, while TTS is evaluated with both objective and subjective metrics. Objective TTS metrics are **CER**—computed by recognizing synthesized audio with FireRedASR—and **SIM**, a speaker-similarity score computed with **WavLM-Large**. Subjective metrics are **IMOS** for intelligibility, **SMOS** for speaker similarity, and **AMOS** for accent naturalness. AMOS is judged by **10 native Sichuanese raters** and **10 non-expert listeners** over **30 samples** [2509.18004].

## 5. Benchmark models and reported performance

The paper evaluates both ASR and TTS systems trained or fine-tuned on WenetSpeech-Chuan. On the ASR side, the key systems include **Paraformer (220M)**, **FireRedASR-AED (1.1B)**, **SenseVoice-small (234M)**, **Whisper-small (244M)**, **Kimi-Audio (7B)**, and **Qwen2.5-omni (3B)**, together with WenetSpeech-Chuan-finetuned variants [2509.18004].

The clearest ASR result is that **Paraformer-WSC-Finetune** reduces average CER from **16.52** to **14.58**, described as an improvement of **about 11.7%** over baseline Paraformer. A further variant, **Paraformer-WSC-Finetune + internal data**, reaches **12.87** CER on WSC-Eval-ASR Total, **14.59** on MagicData Conversation, **6.28** on MagicData Daily-Use, and **13.38** average CER, which the paper characterizes as **state-of-the-art among open-source models** and comparable to commercial systems [2509.18004].

Fine-tuning also benefits omni-modal models. **Qwen2.5-omni-WSC-Finetune** improves average CER from **17.69** to **15.74**, roughly **11%** better than the base omni model. By contrast, generic multilingual or general speech models without Sichuanese specialization, such as Whisper-small and SenseVoice-small, are reported to perform much worse on the dialectal benchmarks [2509.18004].

On the TTS side, **CosyVoice2-WSC**—with its LLM component trained on the full WenetSpeech-Chuan corpus—achieves strong dialectal synthesis. On WSC-Eval-TTS-easy it reports **CER 4.28**, **SIM 72.78**, **IMOS 4.13**, **SMOS 3.94**, and **AMOS 4.05**. A further supervised fine-tuned model, **CosyVoice2-WSC-SFT**, reaches **CER 4.08**, **SIM 78.84**, **SMOS 4.16**, and **AMOS 4.20** on the easy set, and **CER 7.22**, **SIM 67.96**, **IMOS 4.01**, **SMOS 3.03**, and **AMOS 3.98** on the hard set [2509.18004].

A notable pattern is that WenetSpeech-Chuan-trained TTS systems are not merely intelligible; they are competitive on **accent naturalness**. On hard TTS evaluation, **CosyVoice2-WSC** and **CosyVoice2-WSC-SFT** achieve **AMOS 3.92** and **3.98**, respectively, slightly above the reported **3.88** for commercial **Qwen-TTS**. This suggests that the corpus is especially valuable for dialectal accent control rather than only for neutral-text reconstruction.

## 6. Research significance, interpretation, and release

WenetSpeech-Chuan addresses a structural bottleneck in dialectal speech processing: the absence of large, openly accessible corpora that capture real dialect use rather than small read-speech datasets or accented-Mandarin approximations. In that sense, its most important contribution is not only the 10,013-hour scale, but the combination of **authentic Sichuanese**, **rich paralinguistic annotation**, and **manually verified ASR/TTS benchmarks** [2509.18004].

A common simplification is to treat Sichuanese as merely a pronunciation variant of Standard Mandarin. The corpus design directly contradicts that simplification by targeting dialectal differences in **tone**, **lexicon**, and **grammar**, and by evaluating on content such as folk sayings, dialectal vocabulary, and emotionally expressive speech. This also clarifies why generic Mandarin or multilingual speech models degrade on the benchmark and why dialect-specific fine-tuning yields large gains.

The release is also framed as an **AI equity** intervention. The paper argues that underrepresentation of dialect speakers in mainstream ASR and TTS systems produces a systematic performance gap, and that an open 10,000-hour Sichuanese corpus helps reduce that bias. An important empirical point is that the reported dialect gains are said not to come at the cost of Mandarin performance, although the detailed Mandarin-side results are deferred to the project page [2509.18004].

The corpus, benchmarks, models, and recipes are released through the project repository at **https://github.com/ASLP-lab/WenetSpeech-Chuan** [2509.18004]. A plausible broader implication is that WenetSpeech-Chuan establishes a template for future dialect corpora: large-scale web mining, diarization and speaker clustering, LLM-assisted transcript correction, multimodal punctuation, rich speaker metadata, and evaluation sets deliberately split by difficulty. As such, it is likely to function not only as a Sichuanese benchmark, but also as a methodological reference point for dialectal speech-resource construction in Chinese and beyond.

Source: https://www.emergentmind.com/topics/wenetspeech-chuan