Instructions to use heado/asr_mind_mode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/asr_mind_mode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="heado/asr_mind_mode")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("heado/asr_mind_mode") model = AutoModelForCTC.from_pretrained("heado/asr_mind_mode") - Notebooks
- Google Colab
- Kaggle
asr_mind_mode
This model is a fine-tuned version of w11wo/wav2vec2-xls-r-300m-korean on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.7565
- Wer: 4.3843
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 500
- training_steps: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.8183 | 0.1667 | 5 | 2.7565 | 4.3843 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for heado/asr_mind_mode
Base model
w11wo/wav2vec2-xls-r-300m-korean