Instructions to use imdawon/asr_mind_mode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imdawon/asr_mind_mode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="imdawon/asr_mind_mode")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("imdawon/asr_mind_mode") model = AutoModelForCTC.from_pretrained("imdawon/asr_mind_mode", device_map="auto") - 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: 1.6572
- Wer: 0.6561
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 5
- training_steps: 60
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 12.2624 | 0.3376 | 20 | 2.0923 | 0.6766 |
| 12.6346 | 0.6751 | 40 | 1.6844 | 0.6612 |
| 8.2346 | 1.0 | 60 | 1.6572 | 0.6561 |
Framework versions
- Transformers 5.20.2
- Pytorch 2.14.1+cu130
- Datasets 4.8.5
- Tokenizers 0.23.3
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Model tree for imdawon/asr_mind_mode
Base model
w11wo/wav2vec2-xls-r-300m-korean