Add the project X-ARES comparison table

README.md

@@ -20,6 +20,29 @@ This repository contains the trained encoder, its preprocessing configuration,
 and the custom Transformers implementation. No installation of the research
 repository is needed.
 
+## X-ARES results
+
+Scores on [X-ARES](https://arxiv.org/abs/2505.16369) (0–100, higher is better).
+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.
+
+| Model | Speech | Music | Environment | Global Mean | Mean of Means | Training recipe |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| data2vec | 50.62 | 23.24 | 15.41 | 37.83 | 29.76 | Pretrained baseline |
+| wav2vec 2.0 | 41.79 | 34.94 | 29.52 | 37.84 | 35.42 | Pretrained baseline |
+| Whisper | 49.19 | 38.67 | 28.61 | 42.75 | 38.82 | Pretrained baseline |
+| 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/audioset/default.yaml](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/configs/experiment/best_rq/audioset/default.yaml) |
+| BEST-RQ-2 (Interspeech 2026) | 38.49 | 54.40 | 46.39 | 43.21 | 46.43 | [best_rq_2/default.yaml](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/configs/experiment/best_rq_2/default.yaml) |
+| BEST-RQ-2.1 | 52.60 | 62.23 | 53.38 | 54.59 | 56.07 | [best_rq_2_1/masking/80.yaml](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/configs/experiment/best_rq_2_1/masking/80.yaml) |
+| BEST-RQ-2.2 | **53.78** | **63.90** | **55.71** | **56.11** | **57.80** | [best_rq_2_2/masking/80.yaml](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/configs/experiment/best_rq_2_2/masking/80.yaml) |
+
+Global Mean averages all benchmark task scores. Mean of Means gives equal
+weight to the Speech, Music, and Environment category means.
+
+These are the research results reported in the [project README](https://github.com/LudovicTuncay/audio-embeddings/blob/bb88bf790b1dcf8251c6b38e7a4766534adf33d3/README.md), not a new benchmark run of the Transformers exports.
+
 ## Model and training
 
 | Property | Value |

export_manifest.json

@@ -27,7 +27,7 @@
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