Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
best-rq-2
audioset
Instructions to use ltuncay/BEST-RQ-2.2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltuncay/BEST-RQ-2.2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ltuncay/BEST-RQ-2.2-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ltuncay/BEST-RQ-2.2-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Link comparison-table entries to published Hugging Face models
Browse files- README.md +6 -6
- export_manifest.json +1 -1
README.md
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@@ -27,16 +27,16 @@ BEST-RQ (Conformer), BEST-RQ (ViT),
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and all BEST-RQ-2 variants reported below are trained on the **same AudioSet
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split for 200,000 steps**. The pretrained baselines are shown for comparison.
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| Model | Speech | Music | Environment | Global Mean | Mean of Means |
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| --- | ---: | ---: | ---: | ---: | ---: | --- |
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| data2vec | 50.62 | 23.24 | 15.41 | 37.83 | 29.76 | Pretrained baseline |
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| wav2vec 2.0 | 41.79 | 34.94 | 29.52 | 37.84 | 35.42 | Pretrained baseline |
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| Whisper | 49.19 | 38.67 | 28.61 | 42.75 | 38.82 | Pretrained baseline |
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| BEST-RQ (Conformer) | 40.43 | 35.58 | 30.81 | 37.43 | 35.60 | Separate codebase
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| BEST-RQ (ViT) | 32.87 | 41.62 | 34.50 | 34.88 | 36.33 | [
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| BEST-RQ-2 (Interspeech 2026) | 38.49 | 54.40 | 46.39 | 43.21 | 46.43 | [
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| BEST-RQ-2.1 | 52.60 | 62.23 | 53.38 | 54.59 | 56.07 | [
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| BEST-RQ-2.2 | **53.78** | **63.90** | **55.71** | **56.11** | **57.80** | [
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Global Mean averages all benchmark task scores. Mean of Means gives equal
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weight to the Speech, Music, and Environment category means.
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and all BEST-RQ-2 variants reported below are trained on the **same AudioSet
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split for 200,000 steps**. The pretrained baselines are shown for comparison.
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+
| Model | Speech | Music | Environment | Global Mean | Mean of Means | Hugging Face model |
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| --- | ---: | ---: | ---: | ---: | ---: | --- |
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| data2vec | 50.62 | 23.24 | 15.41 | 37.83 | 29.76 | Pretrained baseline |
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| wav2vec 2.0 | 41.79 | 34.94 | 29.52 | 37.84 | 35.42 | Pretrained baseline |
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| Whisper | 49.19 | 38.67 | 28.61 | 42.75 | 38.82 | Pretrained baseline |
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| BEST-RQ (Conformer) | 40.43 | 35.58 | 30.81 | 37.43 | 35.60 | Separate codebase |
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| BEST-RQ (ViT) | 32.87 | 41.62 | 34.50 | 34.88 | 36.33 | [BEST-RQ-ViT](https://huggingface.co/ltuncay/BEST-RQ-ViT) |
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| 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) |
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| 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) |
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| 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) |
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Global Mean averages all benchmark task scores. Mean of Means gives equal
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weight to the Speech, Music, and Environment category means.
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export_manifest.json
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},
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"files": {
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"CODE_LICENSE": "8fe9e8b749cd4abedabcb3100df445db899e72192394d14cc2dbf24a40811af6",
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-
"README.md": "
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"adapters.py": "ca0f26826763c0e48b7508da732242bb83adfeb4814e389ed3fde93ad648c728",
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"config.json": "93452b1dfb678bafd2737b29cc80586e1ccd8368ad0183ed3b88ffd8f12ed6f2",
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"configuration_audio.py": "59f3a0b8db0df5af85e677ac33a4431595e9b2eff6d34c1e6a1778dd75a4568d",
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},
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"files": {
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"CODE_LICENSE": "8fe9e8b749cd4abedabcb3100df445db899e72192394d14cc2dbf24a40811af6",
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+
"README.md": "8ba0124b2cd2cf061cb4c8487909fcf46567e4d22b1746b53eb632f27488f5a2",
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"adapters.py": "ca0f26826763c0e48b7508da732242bb83adfeb4814e389ed3fde93ad648c728",
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"config.json": "93452b1dfb678bafd2737b29cc80586e1ccd8368ad0183ed3b88ffd8f12ed6f2",
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"configuration_audio.py": "59f3a0b8db0df5af85e677ac33a4431595e9b2eff6d34c1e6a1778dd75a4568d",
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