Whisper Nagamese Adapter
This model is a fine-tuned version of openai/whisper-small on the None dataset.
Model source
- Kind
- LoRA
- File size
- 13.5 MB
- Base model
- openai/whisper-small
- Version
- vFP32
- Updated
- Nov 1, 2026
- Publisher
- @kibozoi
- Downloads
- 11
- Source
- Hugging Face
Source description
whisper-nagamese-adapter
This model is a fine-tuned version of openai/whisper-small on the None 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.19.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
Sources
1 source
Sources
1 sourceVerified Nov 1
Download files
Repository files
File inventory only; completeness is unverified.
Model artifacts
1 artifact
Model artifacts
1 artifactadapter_model.safetensors
safetensors · FP32 (tensor header) · 13.5 MB · SHA-256 8307b35dfc66…00b3 · Hugging Face
Download fileSource excerpts
3 excerpts
Source excerpts
3 excerpts--- library_name: peft license: apache-2.0 base_model: openai/whisper-small tags: - base_model:adapter:openai/whisper-small - lora - transformers model-index: - name: whisper-nagamese-adapter 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. --> # whisper-nagamese-adapter This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None 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: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - 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 - lr_scheduler_warmup_steps: 50 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.19.1 - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 5.0.0 - Tokenizers 0.22.2
Source context: 11 downloads · 0 likes · Library peft · Repo kibozoi/whisper-nagamese-adapter
Source context: 287 downloads · 0 likes · Library peft · Repo kibozoi/whisper-nagamese-adapter
