---
dataset_info:
  config_name: Hakka_NanSixian
  features:
  - name: id
    dtype: string
  - name: duration
    dtype: float64
  - name: hanzi
    dtype: string
  - name: hanzi_cln
    dtype: string
  - name: pinyin
    dtype: string
  - name: pinyin_cln
    dtype: string
  - name: ipa
    dtype: string
  - name: ipa_cln
    dtype: string
  - name: mandarin
    dtype: string
  - name: lang_group
    dtype: string
  - name: lang_group_en
    dtype: string
  - name: speaker
    dtype: string
  - name: mismatched_trs
    dtype: bool
  - name: audio
    dtype: audio
  splits:
  - name: train
    num_examples: 36070
configs:
- config_name: Hakka_NanSixian
  data_files:
  - split: train
    path: Hakka_NanSixian/train-*
task_categories:
- text-to-speech
language:
- zh
- hak
size_categories:
- 10K<n<1M
---

# hat_asr_nansixian_reading_clean_r

This dataset is an enhanced `-R` variant of [formospeech/hat_asr_nansixian_reading_clean](https://huggingface.co/datasets/formospeech/hat_asr_nansixian_reading_clean).

## Summary

- Subset: `Hakka_NanSixian`
- Dialect: `客語南四縣`
- Train samples: `36070`
- Audio: enhanced `24 kHz` WAV

## TRAIN

| Subset | lang_group | hours | n_utts | n_chars | secs/utt | chars/sec |
|---|---|---|---|---|---|---|
| Hakka_NanSixian | 客語_南四縣 | 71.49 | 36,070 | 2,046,782 | 7.14 | 7.95 |
| **Total** | **-** | **71.49** | **36,070** | **2,046,782** | **7.14** | **7.95** |

## Processing

1. Start from the original `formospeech/hat_asr_nansixian_reading_clean` train split (16 kHz native).
2. Run speech enhancement with [nvidia/RE-USE](https://huggingface.co/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](https://huggingface.co/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):

```python
from datasets import load_dataset

ds = load_dataset(
    "formospeech/hat_asr_nansixian_reading_clean_r",
    revision="962a18e183f4b0e7ea7d8167ac9d7dd4281c270a",
)
```

or with `huggingface_hub`:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="formospeech/hat_asr_nansixian_reading_clean_r",
    repo_type="dataset",
    revision="962a18e183f4b0e7ea7d8167ac9d7dd4281c270a",
)
```