Feature Extraction
Transformers
Safetensors
calmos
audio
speech
speech-quality-assessment
mos-prediction
speech-foundation-models
cal-mos
interspeech-2026
custom_code
Instructions to use alefiury/CALMOS-XLS-R-300m-SingMOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alefiury/CALMOS-XLS-R-300m-SingMOS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="alefiury/CALMOS-XLS-R-300m-SingMOS", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alefiury/CALMOS-XLS-R-300m-SingMOS", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 596 Bytes
3f3c53d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"model_type": "calmos",
"architectures": [
"CalMOS"
],
"auto_map": {
"AutoConfig": "configuration_calmos.CalMOSConfig",
"AutoModel": "modeling_calmos.CalMOS"
},
"backbone_name": "facebook/wav2vec2-xls-r-300m",
"num_feature_layers": 25,
"adapter_input_dim": 1024,
"adapter_dim": 1024,
"mlp_input_dim": 1024,
"mlp_hidden_dim": 1024,
"mlp_num_layers": 1,
"mlp_output_size": 1,
"mlp_dropout": 0.1,
"target_sr": 16000,
"mos_min": 1.0,
"mos_max": 5.0,
"layer_weight_strategy": "layer_adapter",
"pooling_strategy": "mean",
"freeze_backbone": true
}
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