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Update card: full train/dev/test statistics, drop stale -Sample naming
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---
license: cc-by-nc-sa-4.0
language:
- zh
pretty_name: Minspeech (Southern Min)
extra_gated_prompt: >-
Audio is sourced from copyrighted TV drama content and is released here
strictly for non-commercial research use under CC BY-NC-SA 4.0, consistent
with the original Minspeech license (https://minspeech.github.io/). By
requesting access you agree to use this data only for non-commercial
research purposes and not to redistribute the raw audio.
extra_gated_fields:
Name: text
Affiliation: text
Intended use: text
I agree to use this dataset only for non-commercial research purposes: checkbox
configs:
- config_name: default
data_files:
- split: train
path: data/train-*.parquet
- split: dev
path: data/dev-*.parquet
- split: test
path: data/test-*.parquet
- split: unlabeled
path: data/unlabeled-*.parquet
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype: audio
- name: mandarin
dtype: string
---
# Minspeech (Southern Min)
Full `train`/`dev`/`test` transfer of the [Minspeech](https://minspeech.github.io/) Southern
Min (Taiwanese) speech corpus, staged from an internal COS copy. `unlabeled` is still just a
10-clip illustrative preview -- see "Splits" below.
## Dataset Description
- **Use case**: ASR (Automatic Speech Recognition)
- **Language**: spoken audio is Southern Min / Taiwanese (drama dialogue); the transcript
(`mandarin`) is written in Mandarin Chinese, not Taiwanese Han-lo -- **the two are
different languages**, matching the official Minspeech release as-is.
- **Source**: [Minspeech](https://minspeech.github.io/) official corpus (Southern Min TV
drama), re-cropped to match the official splits exactly. See "Splits" below for details.
- **Audio format**: 16kHz, mono, PCM WAV
- **Split**: 4 splits, see below.
## Dataset Structure
- `id`: official Minspeech utterance ID (`S<series><episode><utterance>` for `train`/`dev`/
`test`; source video ID for `unlabeled`).
- `audio`: audio clip (16kHz mono WAV), spoken in Southern Min / Taiwanese.
- `mandarin`: **Mandarin Chinese** transcript, exactly as released in the official `*_text`
files. Not a Taiwanese-script transcription of the audio. Empty for `unlabeled` (see below).
### Splits
- `train` / `dev` / `test`: **full** official Minspeech ASR splits, utterance-level clips
with transcripts, matching the official `*_wav.scp` / `*_text` files. One source episode
(`S01142`) failed to parse and was skipped -- everything else is included.
- `unlabeled`: raw, **not utterance-segmented** source video audio with no transcript
(`mandarin` is an empty string). The original Minspeech release ships only a video ID list
for this portion and expects users to run their own VAD (the authors suggest
[silero-vad](https://github.com/snakers4/silero-vad)) to cut it into utterances. **This is
still just a 10-clip preview** (20-second excerpts from the start of 10 source videos, for
illustration only) -- the full `unlabeled` split (807GB of raw video audio) hasn't been
uploaded yet.
## Statistics
| **split** | **lang_name** | **hours** | **n_utts** | **n_chars_in_utts** | **secs/utt** | **chars/sec** | **n_sents** | **n_chars_in_sents** |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| train | Taigi | 1692.21 | 2614243 | 20301907 | 2.33 | 3.33 | 0 | 0 |
| dev | Taigi | 16.7259 | 25671 | 201198 | 2.35 | 3.34 | 0 | 0 |
| test | Taigi | 18.509 | 28685 | 219051 | 2.32 | 3.29 | 0 | 0 |
| unlabeled | Taigi | 0.0556 | 10 | 0 | 20.02 | 0 | 0 | 0 |
| **Total** | **-** | **1727.4995** | **2668609** | **20722156** | **2.33** | **3.33** | **0** | **0** |
`dev`/`test`/`unlabeled` are computed exactly (every row scanned). **`train`'s `hours`/
`n_chars_in_utts` are estimated** from a spread sample of 48 of its 1186 shards (~4%) and
scaled up to the exact row count (2,614,243, from the Hub's own row-count metadata) --
scanning all 1186 shards' audio in full wasn't done here since it would mean re-downloading
~178GB just to compute stats. `secs/utt` and `chars/sec` are stable across the sampled
shards (2.3-2.4s/utt, ~3.3 chars/sec throughout), so the estimate should be accurate to
within a percent or two, but treat the `train` row as an estimate, not an exact count.