Add dataset card + viewer config

README.md

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+---
+pretty_name: YouTube Cantonese (Emilia)
+language:
+- yue
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+size_categories:
+- 1M<n<10M
+tags:
+- cantonese
+- speech
+- emilia
+- youtube
+- speech-synthesis
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/part-*.parquet
+---
+
+# YouTube Cantonese — Emilia
+
+**2,064,679 speaker-homogeneous Cantonese speech segments — 5,312.6 hours** — produced by
+running [`alvanlii/cantonese-youtube`](https://huggingface.co/datasets/alvanlii/cantonese-youtube)
+through the [Emilia](https://github.com/open-mmlab/Amphion/tree/main/preprocessors/Emilia)
+speech-data pipeline (source separation → diarization → VAD segmentation → ASR → MOS filtering).
+
+Each row is one clean, single-speaker segment of 3–30 s with a transcript, a speaker turn
+label and a DNSMOS quality score. Audio is shipped separately as MP3s inside zip parts.
+
+## What's in the viewer
+
+The dataset viewer shows the **metadata + transcripts** table (`data/part-a.parquet`,
+`data/part-b.parquet`, concatenated into one `train` split). Audio is *not* embedded in the
+parquet — it lives in the `output-audio-*.zip` parts and is joined by `audio_filename`, so
+there is no inline playback in the viewer. See [Loading the audio](#loading-the-audio).
+
+## Files
+
+| path | what |
+|---|---|
+| `data/part-a.parquet`, `data/part-b.parquet` | segment metadata + transcripts (280 MB total, 2,064,679 rows) |
+| `output-audio-a-*.zip`, `output-audio-b-*.zip` | segment MP3s, 65 parts, 152.6 GB total |
+
+`a` and `b` are the two machines that ran the pipeline. They processed disjoint sets of
+clips — the two parquets share **no** `id`, so concatenating them introduces no duplicates.
+
+## Schema
+
+| column | type | description |
+|---|---|---|
+| `id` | string | source clip id, zero-padded 10 digits (unique across the source dataset) |
+| `shard` | string | source parquet shard, e.g. `train-00027-of-01090` |
+| `segment_index` | int64 | 0-based index of this segment within its clip |
+| `audio_filename` | string | path inside the audio zips: `<shard>/<id>/<id>_<segment_index>.mp3` |
+| `text` | string | Whisper `large-v3` transcript, decoded as `yue` |
+| `start` | double | segment start, seconds, relative to the source clip |
+| `end` | double | segment end, seconds |
+| `speaker` | string | pyannote speaker label, **local to the clip** (`SPEAKER_00`, `SPEAKER_01`, …) |
+| `language` | string | always `yue` (forced, see [How it was built](#how-it-was-built)) |
+| `dnsmos` | double | DNSMOS OVRL score of the segment |
+
+Example row:
+
+```json
+{
+  "id": "0000038032",
+  "shard": "train-00027-of-01090",
+  "segment_index": 0,
+  "audio_filename": "train-00027-of-01090/0000038032/0000038032_0.mp3",
+  "text": " 我地呢,就嚟咗第七区食一个Cribs嘅甜品。因为呢,我地等间諗住去一个百货公司,都系喺呢一区嚟嘅。…",
+  "start": 0.2344375,
+  "end": 23.3624375,
+  "speaker": "SPEAKER_01",
+  "language": "yue",
+  "dnsmos": 2.8213813060420665
+}
+```
+
+## Statistics
+
+| | |
+|---|---|
+| segments | 2,064,679 |
+| total duration | 5,312.6 h |
+| source clips represented | 1,106,929 |
+| source shards represented | 1,090 / 1,090 |
+| distinct (clip, speaker) turns | 1,188,909 |
+| segment duration | mean 9.26 s · median 7.8 s · p90 17.0 s · range 3.0–30.0 s |
+| DNSMOS (OVRL) | mean 3.13 · range 2.80–3.68 |
+| transcript text | 139.7 M characters, mean 67.7 per segment |
+| language | `yue` — 100 % |
+
+Audio format: **24 kHz mono MP3**, loudness-normalized, and taken from the **separated vocal
+stem** (not the original mix).
+
+## Loading
+
+### Metadata
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Scicom-intl/YouTube-Cantonese-Emilia", split="train")
+print(ds[0])
+```
+
+Or straight from the parquet, which is faster if you only want to filter:
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("hf://datasets/Scicom-intl/YouTube-Cantonese-Emilia/data/part-a.parquet")
+df = df[df.dnsmos > 3.2]
+```
+
+### Loading the audio
+
+Download the zip parts (152.6 GB — use `allow_patterns` to take a subset):
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    "Scicom-intl/YouTube-Cantonese-Emilia",
+    repo_type="dataset",
+    local_dir="ycd",
+    allow_patterns=["output-audio-a-*.zip"],   # drop this to fetch everything
+)
+```
+
+### Joining audio to metadata
+
+`audio_filename` is the arcname inside whichever zip part happens to hold it, so build an
+index once and reuse it:
+
+```python
+import glob, zipfile, io
+import soundfile as sf
+
+index = {}
+handles = {}
+for path in glob.glob("ycd/output-audio-*.zip"):
+    handles[path] = zipfile.ZipFile(path)
+    for name in handles[path].namelist():
+        index[name] = path
+
+def read_segment(audio_filename):
+    zf = handles[index[audio_filename]]
+    return sf.read(io.BytesIO(zf.read(audio_filename)))
+
+wav, sr = read_segment(df.audio_filename.iloc[0])
+```
+
+## How it was built
+
+Per source clip, in order:
+
+0. **Standardization** — 24 kHz, mono, 16-bit, loudness-normalized.
+1. **Source separation** — UVR-MDX-NET (`UVR-MDX-NET-Inst_HQ_3`) vocal extraction.
+2. **Speaker diarization** — `pyannote/speaker-diarization-3.1`.
+3. **Segmentation** — Silero VAD, merged and trimmed per speaker to 3–30 s.
+4. **ASR** — WhisperX / faster-whisper `large-v3`, **forced to `yue`**. The source is known to
+   be single-language, and per-segment language detection reliably mislabels Cantonese as
+   `zh`; forcing the label keeps every segment rather than dropping it.
+5. **Quality filter** — DNSMOS OVRL ≥ 2.8, duration 3–30 s, ≥ 2 characters of text, plus a
+   per-clip IQR outlier rejection on seconds-per-character (catches badly aligned segments).
+6. **Export** — one MP3 per surviving segment.
+
+Of the 1,477,757 source clips processed, **1,106,929 (74.9 %) kept at least one segment**;
+the rest were emptied by the quality filter or were unreadable.
+
+Pipeline code: [Scicom-AI-Enterprise-Organization/Emilia](https://github.com/Scicom-AI-Enterprise-Organization/Emilia)
+— a fork of Amphion's Emilia adapted for streaming HF parquet input and multi-GPU sharding.
+
+## Known limitations
+
+- **Transcripts are machine-generated and unverified.** Base `large-v3` normalizes Cantonese
+  toward written/simplified Chinese even when the decode language is forced to `yue`. Many
+  segments do keep authentic Cantonese morphology (`嘅`, `咁`, `㗎`, `喺`, `唔`), but others read
+  closer to Mandarin. If you need reliable Cantonese orthography, re-transcribe with a
+  Cantonese-finetuned model — or use the source dataset's own `transcript_whisper` field.
+- **Whisper repetition loops survive the filter.** A small fraction of segments degenerate
+  into a repeated token (e.g. `再,再,再,…`). Filter on character-repetition ratio if this
+  matters to you.
+- **`speaker` is clip-local.** `SPEAKER_00` in two different clips is not the same person.
+  There is no global speaker identity resolution.
+- **`dnsmos` has a floor of 2.8 by construction** — it is a filter threshold, not a full
+  quality ranking, so the column's dynamic range is narrow.
+- **`start`/`end` index the standardized, vocals-separated clip**, which shares a timeline
+  with the source clip but not its audio content.
+- **Coverage is 99.96 %, not 100 %.** 1,477,757 of the source dataset's 1,478,373 clips were
+  processed; 616 clips (0.04 %) were never completed because their worker was killed
+  mid-clip. They are simply absent — no partial rows.
+
+## Provenance and licensing
+
+Derived from [`alvanlii/cantonese-youtube`](https://huggingface.co/datasets/alvanlii/cantonese-youtube)
+(gated), which is itself sourced from YouTube. The underlying recordings remain subject to
+their original terms; **no additional license is granted here**, and no license is asserted
+over the source audio. Review the upstream dataset's terms before redistributing or training
+on this data. The processing code is Apache-2.0.
+
+## Citation
+
+The pipeline:
+
+```bibtex
+@inproceedings{emilia,
+    author={He, Haorui and Shang, Zengqiang and Wang, Chaoren and Li, Xuyuan and Gu, Yicheng and Hua, Hua and Liu, Liwei and Yang, Chen and Li, Jiaqi and Shi, Peiyang and Wang, Yuancheng and Chen, Kai and Zhang, Pengyuan and Wu, Zhizheng},
+    title={Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation},
+    booktitle={Proc.~of SLT},
+    year={2024}
+}
+```