Model card: hfa-english-fullweak-v2, mark v1 superseded

README.md

@@ -26,7 +26,8 @@ name and verifies the pinned SHA256 before it puts anything on disk.
 | File | Family | Size | SHA256 |
 |---|---|---:|---|
 | `hfa-mandarin-fullweak-v1.ckpt` | HubertFA | 54.2 MB | `d38f7d5839e2cd93e8dec5b77b6bd3ed28aa1318d5ed069c2cd81c899dd3c057` |
-| `hfa-english-fullweak-v1.ckpt` | HubertFA | 54.1 MB | `c1b2baa939a87b3ed832c97e18eba9fcb27214b39f50ff346c062601bd11c513` |
+| `hfa-english-fullweak-v2.ckpt` | HubertFA | 54.1 MB | `878de0899cb188c05537934a6f55bfd7aaabcbde1c411aba30d1e6f50a995712` |
+| `hfa-english-fullweak-v1.ckpt` | HubertFA (superseded by v2) | 54.1 MB | `c1b2baa939a87b3ed832c97e18eba9fcb27214b39f50ff346c062601bd11c513` |
 | `nll-hubert-ls960-v1.pt` | breath head (NLL) for the English aligner | 9.9 MB | `03587e7a476aea7f137904e6034420d0a4b255d12949e85ec16b20077ae29ce8` |
 
 ## `hfa-mandarin-fullweak-v1`
@@ -93,9 +94,69 @@ aligner = HubertFATorchAligner()             # resolves to this checkpoint
 results = aligner.align([{"id": "x", "audio_path": "a.wav", "text": "中文歌词"}])
 ```
 
-## `hfa-english-fullweak-v1`
+## `hfa-english-fullweak-v2`
+
+The English model: the same alignment head over a **frozen
+`facebook/hubert-base-ls960` front-end**, trained with the recipe of
+`hfa-english-fullweak-v1` below on data whose only change is *where the clips
+start*.
+
+- **Recipe**: `configs/hfa/train_en_fullweak_v6.yaml` (identical to v1's
+  except the run name), data `configs/hfa/binarize_en_fullweak_v6.yaml`.
+- **Training data**: 21.4 h full-label across 13 singers (as v1; Project-AIdol
+  and NUS-48E re-cut so that a silence goes whole to one neighbouring segment
+  instead of being split) + 1845 h weak-label English singing selected by
+  `singmos >= 3.5` only, so the corpus's silence-cut `sub` segments (37 %) are
+  in the pool alongside the lyric-timed `short` ones.
+- **Step 56000**, selected by CBER on held-out singers.
+- **Phoneme inventory / G2P**: as v1 (`english-arpabet-v1`, G2pEn); the
+  vocabulary file is byte-identical, so the two are drop-in for each other.
+
+### Why v2 exists
+
+v1 puts a spurious sub-millisecond `SP` at the head of about a third of clips
+that start on the voice. The cause was traced to its weak-label pool: a
+`wer` filter silently restricted it to lyric-timed segments, three quarters of
+which start mid-voice, and CTC training on those teaches the shared phone
+channels to read onset frames as "not yet a phone". Mixing in silence-cut
+segments removes it: **32 % → 5.3 %** of voice-initial GTSinger-en clips
+(`bench/tools/probe_leading_sp.py`; `docs/experiments/leading_sp_probe.md`).
 
-The same alignment head over a **frozen `facebook/hubert-base-ls960`
+### Results
+
+Same four held-out NUS-48E singers as v1 (201 clips, 0.53 h):
+
+| System | CBER ↓ | VER20 ↓ | VER50 ↓ | mIoU ↑ |
+|---|---:|---:|---:|---:|
+| **`hfa-english-fullweak-v2`** | 0.1951 | 0.1327 | 0.0634 | 0.7823 |
+| `hfa-english-fullweak-v1` | 0.1975 | 0.1318 | 0.0616 | 0.7821 |
+| Same recipe, full label only (21.4 h) | **0.1940** | **0.1300** | 0.0617 | **0.7833** |
+
+A tie with v1 on alignment quality; the caveat below about the weak label
+buying nothing measurable on clean studio audio still applies.
+
+### Batched inference **is** bit-reproducible
+
+Unlike v1, this checkpoint **passes** the toolkit's batch-invariance gate:
+`bench/tools/validate_hfa_batch.py` on the same 48 items, batch 8, measures a
+maximum boundary shift of **0.002 ms** against a 1.0 ms tolerance.
+
+### Runtime dependency and usage
+
+As v1: [`facebook/hubert-base-ls960`](https://huggingface.co/facebook/hubert-base-ls960)
+must be present, and the model is named explicitly:
+
+```python
+from modules.api import HubertFATorchAligner
+
+aligner = HubertFATorchAligner(ckpt="hfa-english-fullweak-v2", g2p="G2pEn")
+results = aligner.align([{"id": "x", "audio_path": "a.wav", "text": "english lyrics"}])
+```
+
+## `hfa-english-fullweak-v1` (superseded)
+
+Kept because the `CrawlSinger-en` corpus's `align_conf` column was written by
+it; use `hfa-english-fullweak-v2` for new work. The same alignment head over a **frozen `facebook/hubert-base-ls960`
 front-end** — same HuBERT-base recipe as the Mandarin model, so only the
 encoder path and the phoneme inventory differ.
 
@@ -165,7 +226,7 @@ A **breath (non-lexical) head**, not an aligner: a 2.45 M-parameter CVNT that
 labels each 10 ms frame `None` / `AP` from the HuBERT units the aligner has
 already computed, and whose breaths are spliced into the finished alignment as
 `AP` without touching any other boundary. It rides on
-`hfa-english-fullweak-v1` as `ap_detector="nll"` and is selected automatically
+`hfa-english-fullweak-v2` (or v1) as `ap_detector="nll"` and is selected automatically
 for any HubertFA aligner whose front-end is `hubert-base-ls960`.
 
 - **Why it exists**: a breath head is bound to the SSL front-end it was
@@ -205,7 +266,7 @@ the per-singer annotation probe:
 ```python
 from modules.api import HubertFATorchAligner
 
-aligner = HubertFATorchAligner(ckpt="hfa-english-fullweak-v1", g2p="G2pEn",
+aligner = HubertFATorchAligner(ckpt="hfa-english-fullweak-v2", g2p="G2pEn",
                                ap_detector="nll")   # resolves to this head
 ```