Document 2M X-ARES and HEAR results and link the BEST-RQ-2 paper

README.md

@@ -9,9 +9,12 @@ tags:
   - audio-embeddings
   - best-rq-2
   - audioset
+  - arxiv:2606.30700
 ---
 # BEST-RQ-2.2-base
 
+[![BEST-RQ-2 paper: arXiv 2606.30700](https://img.shields.io/badge/arXiv-2606.30700-b31b1b.svg)](https://arxiv.org/abs/2606.30700)
+
 BEST-RQ-2.2-base is a self-supervised audio encoder trained on AudioSet for
 **200,000 optimizer steps**. It produces **768-dimensional** clip and frame
 embeddings from mono **16 kHz** waveforms, and supports downstream fine-tuning.
@@ -23,29 +26,61 @@ repository is needed.
 ## X-ARES results
 
 Scores on [X-ARES](https://arxiv.org/abs/2505.16369) (0–100, higher is better).
-Audio-JEPA, BEST-RQ (Conformer), BEST-RQ (ViT),
-and all BEST-RQ-2 variants reported below are trained on the **same AudioSet
-split for 200,000 steps**. The pretrained baselines are shown for comparison.
+Audio-JEPA, BEST-RQ (Conformer), BEST-RQ (ViT), and the original BEST-RQ-2
+variants below use the **same AudioSet split for 200,000 steps**.
+**BEST-RQ-2.2 (2M)** uses **2,000,000 steps**, with batch size 128 per GPU
+on two GPUs (effective batch 256). The pretrained baselines are shown for comparison.
 
 | Model | Speech | Music | Environment | Global Mean | Mean of Means | Hugging Face model |
 | --- | ---: | ---: | ---: | ---: | ---: | --- |
-| data2vec | 50.62 | 23.24 | 15.41 | 37.83 | 29.76 | [facebook/data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base) |
-| wav2vec 2.0 | 41.79 | 34.94 | 29.52 | 37.84 | 35.42 | [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) |
-| Whisper | 49.19 | 38.67 | 28.61 | 42.75 | 38.82 | [openai/whisper-base](https://huggingface.co/openai/whisper-base) |
+| data2vec | 50.62 | 23.24 | 15.41 | 37.83 | 29.76 | [data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base) |
+| wav2vec 2.0 | 41.79 | 34.94 | 29.52 | 37.84 | 35.42 | [wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) |
+| Whisper | 49.19 | 38.67 | 28.61 | 42.75 | 38.82 | [whisper-base](https://huggingface.co/openai/whisper-base) |
 | Audio-JEPA | 29.64 | 44.27 | 25.61 | 31.18 | 33.17 | [Audio-JEPA-base](https://huggingface.co/ltuncay/Audio-JEPA-base) |
-| BEST-RQ (Conformer) | 40.43 | 35.58 | 30.81 | 37.43 | 35.60 | Separate codebase |
+| BEST-RQ (Conformer) | 40.43 | 35.58 | 30.81 | 37.43 | 35.60 | Separate codebase (recipe unavailable) |
 | BEST-RQ (ViT) | 32.87 | 41.62 | 34.50 | 34.88 | 36.33 | [BEST-RQ-ViT](https://huggingface.co/ltuncay/BEST-RQ-ViT) |
 | BEST-RQ-2 (Interspeech 2026) | 38.49 | 54.40 | 46.39 | 43.21 | 46.43 | [BEST-RQ-2](https://huggingface.co/ltuncay/BEST-RQ-2) |
 | BEST-RQ-2.1 | 52.60 | 62.23 | 53.38 | 54.59 | 56.07 | [BEST-RQ-2.1-base](https://huggingface.co/ltuncay/BEST-RQ-2.1-base) |
-| BEST-RQ-2.2 | **53.78** | **63.90** | **55.71** | **56.11** | **57.80** | [BEST-RQ-2.2-base](https://huggingface.co/ltuncay/BEST-RQ-2.2-base) |
+| BEST-RQ-2.2 | 53.78 | 63.90 | 55.71 | 56.11 | 57.80 | [BEST-RQ-2.2-base](https://huggingface.co/ltuncay/BEST-RQ-2.2-base) |
+| BEST-RQ-2.2 (2M) | 58.62 | 65.74 | 61.80 | 58.77 | 62.05 | [BEST-RQ-2.2-base-2M](https://huggingface.co/ltuncay/BEST-RQ-2.2-base-2M) |
+| BEST-RQ-2.2 (2M, overlap50) | **60.87** | **65.78** | **65.90** | **61.13** | **64.18** | [BEST-RQ-2.2-base-2M](https://huggingface.co/ltuncay/BEST-RQ-2.2-base-2M) |
 
 Global Mean averages all benchmark task scores. Mean of Means gives equal
 weight to the Speech, Music, and Environment category means.
 
-Audio-JEPA scores were supplied by the author for [run `jp6l70l6`](https://wandb.ai/tuncay-ludovic/audio%20embeddings/runs/jp6l70l6). The remaining scores are reported in the [project README](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/README.md). These are reported research results, not a new benchmark run of the Transformers exports.
+The two 2M rows report the same training checkpoint with different extraction
+settings: [original evaluation](https://wandb.ai/tuncay-ludovic/X-ARES/runs/cw0rrt7o)
+and [50% overlapping windows](https://wandb.ai/tuncay-ludovic/X-ARES/runs/a7a3fy9z).
+Their scores use the logged **Combined** metric: equal weight for the linear-probe
+and k-NN aggregates, excluding unavailable tasks separately for each method.
+The earlier rows retain previously reported scores. Extraction and evaluation
+settings differ across runs, so this is not a controlled training-duration ablation.
+
+Audio-JEPA scores were supplied by the author for [run `jp6l70l6`](https://wandb.ai/tuncay-ludovic/audio%20embeddings/runs/jp6l70l6). The other 200k-model and pretrained-baseline scores are reported in the [project README](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/README.md). These are reported research results, not a new benchmark run of the Transformers exports.
 
 The Audio-JEPA row refers to the newer **16 kHz, 200,000-step** run, not the original ICME model (**32 kHz, 100,000 steps**). The author reports better results for this newer checkpoint.
 
+## HEAR results
+
+Scores on [HEAR](https://arxiv.org/abs/2203.03022) (0–100, higher is better),
+using `overlap50_two_phase` extraction. All five project models cover all 19 tasks.
+BEST-RQ-2.2 (2M) was trained for 2,000,000 steps; the other rows use 200,000 steps.
+
+| Model | Speech | Music | Environment | Global Mean | Mean of Means | Coverage |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| Audio-JEPA | 36.53 | 74.20 | 51.85 | 56.42 | 54.19 | 19/19 |
+| BEST-RQ-2 | 50.74 | 79.46 | 69.21 | 67.69 | 66.47 | 19/19 |
+| BEST-RQ-2.1 | 64.74 | 86.47 | 70.83 | 75.50 | 74.02 | 19/19 |
+| BEST-RQ-2.2 | 65.76 | 85.13 | 76.19 | 76.66 | 75.69 | 19/19 |
+| BEST-RQ-2.2 (2M) | 70.18 | 85.38 | 81.82 | 79.64 | 79.13 | 19/19 |
+
+The 2M scores come from [this W&B evaluation](https://wandb.ai/tuncay-ludovic/HEAR/runs/hear-v2-aa56c55deb6099f6); its checkpoint hash
+matches the released 2M checkpoint. The earlier rows retain the reported
+2026-09-19 W&B comparison. [Score sources and task-level 2M results](https://github.com/LudovicTuncay/audio-embeddings/blob/master/docs/data/best_rq_2_2_2m_results.json).
+These are arithmetic means of primary task scores, with equal task weighting for
+Global Mean and equal domain weighting for Mean of Means; they are not the
+challenge's standardized ranking. These evaluations precede the Transformers export.
+
 ## Model and training
 
 | Property | Value |

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