hat_asr_sixian_broadcast_clean_r

This dataset is an enhanced -R variant of formospeech/hat_asr_sixian_broadcast_clean.

Summary

• Subset: Hakka_Sixian
• Dialect: 客語四縣
• Train samples: 88263
• Audio: enhanced 24 kHz WAV

TRAIN

Subset	lang_group	hours	n_utts	n_chars	secs/utt	chars/sec
Hakka_Sixian	客語_四縣	231.77	88,263	8,172,269	9.45	9.79
Total	-	231.77	88,263	8,172,269	9.45	9.79

Processing

1. Start from the original formospeech/hat_asr_sixian_broadcast_clean train split (16 kHz native).
2. Run speech enhancement with nvidia/RE-USE (Multilingual Universal Speech Enhancement), using its bandwidth-extension mode (--BWE 24000) to enhance and upsample to 24 kHz in one pass.
3. Replace only the audio field with the enhanced audio; all other fields are copied unchanged from the source, one row per source row (no rows dropped or added).
4. Encode as 16-bit PCM WAV.

Why RE-USE (changed from the previous sidon-v0.1-based version)

The previous version of this dataset (see below) used sarulab-speech/sidon-v0.1 for enhancement. That model was observed to occasionally distort pronunciation or alter speaker timbre on this corpus. nvidia/RE-USE's own technical report (arXiv:2603.02641, §3.10) directly validates downstream TTS training on RE-USE-enhanced audio, reporting improved CER/WER and speaker-similarity (not degraded) versus unenhanced audio -- the closest available evidence to this dataset's actual use case, rather than generic audio-quality benchmarks alone.

Previous version (sidon-v0.1-based)

The prior revision of this dataset -- produced with sarulab-speech/sidon-v0.1 instead of RE-USE -- remains accessible via its commit hash for reproducibility (e.g. if a model was trained on that version and needs to be reproduced exactly):

from datasets import load_dataset

ds = load_dataset(
    "formospeech/hat_asr_sixian_broadcast_clean_r",
    revision="94f927377f99209c316e87dba6b0c809b2017168",
)

or with huggingface_hub:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="formospeech/hat_asr_sixian_broadcast_clean_r",
    repo_type="dataset",
    revision="94f927377f99209c316e87dba6b0c809b2017168",
)