Wav2vec2 Ks
This model is a fine-tuned version of facebook/wav2vec2-base on the SUPERB Keyword Spotting dataset.
Model source
- Kind
- Other
- File size
- 361 MB
- Base model
- facebook/wav2vec2-base
- Version
- vRevision 99ab759
- Updated
- Sep 19, 2026
- Downloads
- 9
- Source
- Hugging Face
Source description
wav2vec2-base-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base on the SUPERB Keyword Spotting dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 3000
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cpu
- Datasets 4.5.0
- Tokenizers 0.22.2
Sources
1 source
Sources
1 sourceVerified Sep 19
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - superb/ks model-index: - name: wav2vec2-base-finetuned-ks results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the SUPERB Keyword Spotting dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 3000 ### Training results ### Framework versions - Transformers 4.57.6 - Pytorch 2.9.0+cpu - Datasets 4.5.0 - Tokenizers 0.22.2
Source context: 9 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo explorer7/wav2vec2-ks
