Document trimmed audio + permutation configs

README.md

@@ -18,6 +18,14 @@ configs:
   data_files:
   - split: train
     path: data/part-*.parquet
+- config_name: permutation
+  data_files:
+  - split: train
+    path: permutation/train-*
+- config_name: permutation_sample
+  data_files:
+  - split: train
+    path: permutation_sample/train-*
 ---
 
 # YouTube Cantonese — Emilia
@@ -28,14 +36,22 @@ through the [Emilia](https://github.com/open-mmlab/Amphion/tree/main/preprocesso
 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.
+label and a DNSMOS quality score. Audio is shipped separately as MP3s inside zip parts, in
+both an original and a silence-trimmed edition. A derived
+[`permutation` config](#voice-cloning-pairs-permutation-config) supplies **1,635,566
+same-speaker (reference, target) pairs** for voice cloning.
 
-## What's in the viewer
+## Configs
 
-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).
+| config | rows | what |
+|---|---|---|
+| `default` | 2,064,679 | one row per segment: transcript, timing, speaker, DNSMOS |
+| `permutation` | 1,635,566 | same-speaker (reference, target) utterance pairs |
+| `permutation_sample` | 1,626,542 | `permutation` capped at 3 targets per reference |
+
+The viewer shows these tables — **transcripts and metadata only**. Audio is *not* embedded in
+the parquet; it lives in the zip parts and is joined by `audio_filename`, so there is no
+inline playback. See [Loading the audio](#loading-the-audio).
 
 ## Files
 
@@ -43,10 +59,16 @@ there is no inline playback in the viewer. See [Loading the audio](#loading-the-
 |---|---|
 | `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 |
+| `output-audio-trim-a-*.zip`, `output-audio-trim-b-*.zip` | the same segments with internal silence shortened — see [Silence-trimmed audio](#silence-trimmed-audio) |
+| `permutation/train-*.parquet` | 1,635,566 (reference, target) voice-cloning pairs |
+| `permutation_sample/train-*.parquet` | the same, capped at 3 targets per reference (99.4 % overlap) |
 
 `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.
 
+Both audio sets use **identical arcnames**, so one `audio_filename` resolves in either: pick
+the untrimmed zips or the trimmed ones, and the metadata rows need no change.
+
 ## Schema
 
 | column | type | description |
@@ -127,10 +149,14 @@ snapshot_download(
     "Scicom-intl/YouTube-Cantonese-Emilia",
     repo_type="dataset",
     local_dir="ycd",
-    allow_patterns=["output-audio-a-*.zip"],   # drop this to fetch everything
+    allow_patterns=["output-audio-a-*.zip"],       # untrimmed, box a only
+    # allow_patterns=["output-audio-trim-*.zip"],  # silence-trimmed, both boxes
 )
 ```
 
+Take **one** of the two sets — `output-audio-*.zip` and `output-audio-trim-*.zip` hold the
+same arcnames, so downloading both and indexing them together makes the later one win.
+
 ### Joining audio to metadata
 
 `audio_filename` is the arcname inside whichever zip part happens to hold it, so build an
@@ -154,6 +180,68 @@ def read_segment(audio_filename):
 wav, sr = read_segment(df.audio_filename.iloc[0])
 ```
 
+## Silence-trimmed audio
+
+`output-audio-trim-*.zip` holds a second copy of every segment with its **internal silences
+shortened**. Same arcnames, same 24 kHz mono MP3 format — only the samples differ.
+
+The segments are already VAD-cut, so this is a light touch: over a 4,000-file sample the
+trimmed copy keeps **98.7 % of the original duration on average** (median 99.8 %, p10 96.8 %),
+and **25 % of files come through untouched**. The tail is where it earns its keep — the
+heaviest trim found was 6.83 s → 3.35 s. What it removes is the occasional long pause *inside*
+a segment, which is the part that hurts TTS alignment.
+
+Per file: 30 ms frames are labelled by WebRTC VAD (aggressiveness 3) on a 16 kHz
+peak-normalised copy; runs of same-labelled frames are grouped; then each silence run is
+shortened — leading silence keeps only its last 0.3 s, trailing silence only its first 0.3 s,
+and an interior silence of ≥ 0.4 s is cut to 0.2 s from each end. Speech is never touched.
+
+Use the trimmed set for TTS/voice-cloning training where dead air is wasted context; use the
+untrimmed set when you need timings that line up with `start`/`end`, or are doing ASR where
+the pauses are harmless.
+
+Produced by [`trim_silence.py`](https://github.com/Scicom-AI-Enterprise-Organization/Emilia/blob/master/trim_silence.py)
+in the pipeline repo, after [malaya-speech](https://github.com/malaysia-ai/malaya-speech).
+
+## Voice-cloning pairs (`permutation` config)
+
+Beyond the segments themselves, the dataset ships **(reference, target) pairs** — two
+utterances by the same speaker, for training or evaluating voice cloning.
+
+```python
+from datasets import load_dataset
+
+pairs = load_dataset("Scicom-intl/YouTube-Cantonese-Emilia", "permutation", split="train")
+pairs[0]
+# {'reference_audio': 'train-00027-of-01090/0000038032/0000038032_0.mp3',
+#  'reference_text':  '...',
+#  'target_audio':    'train-00027-of-01090/0000038032/0000038032_1.mp3',
+#  'target_text':     '...'}
+```
+
+`reference_audio` / `target_audio` use the **same paths as `audio_filename`** in the
+`default` config, so they resolve against either zip set with no rewriting.
+
+How a pair is made: within one clip, segments are kept only if their transcript passes the
+quality filters (drops ASR boilerplate, mostly-single-character filler, Whisper repetition
+loops, and any text with a 3-gram repeated more than three times). Surviving segments are
+then paired within each diarized speaker, and a pair is emitted only if the two segments'
+TitaNet-L speaker embeddings have cosine similarity **≥ 0.8** — a guard against diarization
+having merged two voices under one label.
+
+**1,635,566 pairs** drawn from 476,368 clips. Most clips contribute none: a (clip, speaker)
+turn holds at most 7 segments and usually 1–2, and a lone segment cannot form a pair.
+
+`permutation_sample` applies the same construction but caps each reference at 3 targets.
+Because that cap almost never binds here, it retains **1,626,542 pairs — 99.4 % of
+`permutation`**. It exists for parity with sibling datasets; for this corpus the two are
+effectively the same table, so just use `permutation`.
+
+> **Pairs are within a single clip, never across clips.** Speaker labels are clip-local
+> (see [Known limitations](#known-limitations)), so there is no way to pair the same person
+> across two different videos — and no claim that different clips with the same label are
+> the same speaker.
+
 ## How it was built
 
 Per source clip, in order: