feat(v1.1): add mdx_net_inst_hq3 + fix htdemucs_6s other-stem eval

README.md

@@ -40,12 +40,13 @@ small set of CC-BY tracks for qualitative listening.
 
 ## Leaderboard (median SDR per stem)
 
-| model_id    |   bass |   drums |   other |   vocals |
-|:------------|-------:|--------:|--------:|---------:|
-| htdemucs_ft |  10.38 |   10.11 |    6.34 |     9.19 |
-| mdx_extra_q |  11.42 |   11.49 |    7.67 |     9.04 |
-| htdemucs_6s |   9.11 |    9.54 |    0.22 |     8.66 |
-| htdemucs    |   9.78 |   10.01 |    6.42 |     8.53 |
+| model_id         |   bass |   drums |   other |   vocals |
+|:-----------------|-------:|--------:|--------:|---------:|
+| htdemucs_ft      |  10.38 |   10.11 |    6.34 |     9.19 |
+| mdx_extra_q      |  11.42 |   11.49 |    7.67 |     9.04 |
+| htdemucs_6s      |   9.11 |    9.54 |    5.74 |     8.66 |
+| htdemucs         |   9.78 |   10.01 |    6.42 |     8.53 |
+| mdx_net_inst_hq3 | —      |  —      |  —      |     5.81 |
 
 Higher is better. SDR is computed with the `museval` reference implementation
 (BSS Eval v4) on 1-second windows, exactly the protocol used by SiSEC and the
@@ -92,36 +93,36 @@ df = ds["results"].to_pandas()
 df.groupby(["model_id", "stem"])["sdr_median"].median().unstack()
 ```
 
-### `audio_samples` (planned for v1.1)
+### `audio_samples` (planned for v1.2)
 
 A future config will ship a small set of CC-BY-licensed clips with reference
 and predicted stems, so you can A/B them in the dataset viewer. Sourcing
 genuinely commercial-friendly 4-stem multitracks in 2026 is non-trivial;
-see the [v1.1 sourcing notes](https://github.com/yourusername/musicai/blob/main/scripts/hf-benchmark/configs/tracks.yaml)
+see the [v1.2 sourcing notes](https://github.com/yourusername/musicai/blob/main/scripts/hf-benchmark/configs/tracks.yaml)
 for the plan.
 
 ---
 
 ## Models
 
-### v1 (this release)
+### v1.1 (this release)
 
-All four models below produced complete results on the full 50-track MUSDB18-HQ
-test split.
+All five models below produced complete results on the full 50-track
+MUSDB18-HQ test split.
 
 | id | family | notes |
 |---|---|---|
 | `htdemucs` | Hybrid Transformer Demucs | Facebook AI's default 4-stem model |
 | `htdemucs_ft` | Hybrid Transformer Demucs | Fine-tuned variant, best vocal/instrumental separation in the Demucs family |
-| `htdemucs_6s` | Hybrid Transformer Demucs | 6-stem model (adds piano + guitar). See [Known Limitations](#known-limitations) for `other`-stem evaluation caveat. |
-| `mdx_extra_q` | Demucs MDX | MDX challenge winner, quantised, 4-model ensemble — best `bass` and `drums` of the v1 lineup |
+| `htdemucs_6s` | Hybrid Transformer Demucs | 6-stem model (adds piano + guitar). Its `other`-row SDR is now computed against `piano + guitar + other` summed back together — see the [Changelog](#changelog) for details. |
+| `mdx_extra_q` | Demucs MDX | MDX challenge winner, quantised, 4-model ensemble — best `bass` and `drums` of the lineup |
+| `mdx_net_inst_hq3` | MDX-Net | Vocal isolator running through `audio-separator` with the CoreML ONNX provider on Apple Silicon. Only the `vocals` row is reported (it does not produce drums/bass/other). |
 
-### Planned for v1.1 (~next 24-72 hours)
+### Planned for v1.2
 
 | id | family | why deferred |
 |---|---|---|
-| `mdx_net_inst_hq3` | MDX-Net | Runner had a `use_coreml=True` keyword that's not in the version of `audio-separator` we pinned. Fix is one-line; will land in v1.1. |
-| `bs_roformer` | Band-Split Roformer | Currently SOTA but slow on Apple MPS due to operator fallbacks (~11 hr for full test set). Will add once we run on CUDA. |
+| `bs_roformer` | Band-Split Roformer | Current SOTA but slow on Apple MPS due to operator fallbacks (~11 hr for the full test set). Will add once we run on CUDA. |
 | `mel_band_roformer` | Mel-Band Roformer | Same reason. |
 | `spleeter_4stems` | Spleeter | Legacy baseline; TensorFlow install is brittle on Apple Silicon. |
 
@@ -130,30 +131,26 @@ and command lines.
 
 ---
 
-## Known limitations
+## Changelog
 
-We're shipping v1 with two caveats called out explicitly so nobody is
-surprised by the numbers.
+### v1.1 (current)
 
-### 1. `htdemucs_6s` undersells on the `other` stem
+- Added `mdx_net_inst_hq3` (vocals-only MDX-Net via `audio-separator` with the
+  CoreML ONNX execution provider).
+- Fixed `htdemucs_6s` `other`-stem evaluation. The 6-stem model splits piano
+  and guitar out of `other`; the v1 eval pass compared its residual `other`
+  output directly to MUSDB's `other` and got a misleading 0.22 dB. v1.1 sums
+  the model's `piano + guitar + other` predictions back together before SDR,
+  so `htdemucs_6s` is now directly comparable to its 4-stem siblings.
+- New `prediction_components` column on every row records the WAV files that
+  were summed to form each stem (e.g. `vocals` or `other+piano+guitar`), so
+  the aggregation is fully auditable.
 
-The 6-stem model splits piano and guitar **out** of the `other` stem.
-MUSDB18-HQ's reference `other` stem includes piano + guitar mixed in. So the
-6-stem model's residual `other` output is, by design, nearly empty — and the
-SDR comparison against MUSDB's `other` looks much worse than it actually is
-(0.22 dB in v1 vs ~6 dB for the 4-stem siblings).
+### v1
 
-The fair comparison is to **sum the 6-stem model's `piano + guitar + other`
-outputs and compare that aggregate to MUSDB's `other`**. We'll do exactly
-that in v1.1's eval pass and republish. Until then, treat `htdemucs_6s`'s
-`other` row as not-meaningful and look at its `vocals`, `drums`, `bass` rows
-for a fair head-to-head.
-
-### 2. `mdx_net_inst_hq3` is missing from v1
-
-A bug in our runner's `audio-separator` integration killed all 50 separation
-runs for this model. The leaderboard you see here only contains the four
-Demucs-family models. v1.1 will include this model.
+- Initial release: 4 Demucs-family models × 50 MUSDB18-HQ tracks = 800 rows.
+- Known issues (both fixed in v1.1): missing `mdx_net_inst_hq3`,
+  `htdemucs_6s` `other`-stem evaluation artefact.
 
 ---
 
@@ -240,15 +237,16 @@ python -m src.push_to_hub --create
 ```
 
 Reference wall times measured on an Apple M4 Pro (24 GB unified memory),
-PyTorch 2.11 with the MPS backend, for the v1 lineup:
+PyTorch 2.11 with the MPS backend, for the v1.1 lineup. Demucs models use
+MPS; MDX-Net runs through ONNX Runtime's CoreML execution provider.
 
 | Stage | Wall time |
 |---|---|
 | Download MUSDB18-HQ from Zenodo | 32 min |
-| Separate (4 models × 50 tracks) | 2 h 3 min |
-| Eval (museval BSS Eval v4) | 2 h 10 min |
+| Separate (5 models × 50 tracks) | ~2 h 30 min |
+| Eval (museval BSS Eval v4) | ~2 h 20 min |
 | Build dataset | < 1 s |
-| **Total** | **~4 h 45 min** |
+| **Total** | **~5 h 20 min** |
 
 ---
 

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