| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: facebook/wav2vec2-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - timit_asr |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: repo_name |
| | results: |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Automatic Speech Recognition |
| | dataset: |
| | name: timit_asr |
| | type: timit_asr |
| | config: clean |
| | split: None |
| | args: clean |
| | metrics: |
| | - type: wer |
| | value: 0.22107366825167116 |
| | name: Wer |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # repo_name |
| | |
| | This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit_asr dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5351 |
| | - Wer: 0.2211 |
| |
|
| | ## 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: 0.0001 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - 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: 1000 |
| | - num_epochs: 30 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-------:|:-----:|:---------------:|:------:| |
| | | 3.5252 | 1.0040 | 500 | 1.6991 | 0.9701 | |
| | | 0.854 | 2.0080 | 1000 | 0.5187 | 0.4025 | |
| | | 0.4211 | 3.0120 | 1500 | 0.4289 | 0.3326 | |
| | | 0.2871 | 4.0161 | 2000 | 0.3947 | 0.2896 | |
| | | 0.2266 | 5.0201 | 2500 | 0.4034 | 0.2881 | |
| | | 0.1789 | 6.0241 | 3000 | 0.4833 | 0.2926 | |
| | | 0.1638 | 7.0281 | 3500 | 0.4342 | 0.2776 | |
| | | 0.15 | 8.0321 | 4000 | 0.4643 | 0.2750 | |
| | | 0.1251 | 9.0361 | 4500 | 0.4449 | 0.2642 | |
| | | 0.1064 | 10.0402 | 5000 | 0.4785 | 0.2578 | |
| | | 0.0986 | 11.0442 | 5500 | 0.4480 | 0.2627 | |
| | | 0.0883 | 12.0482 | 6000 | 0.4876 | 0.2603 | |
| | | 0.0784 | 13.0522 | 6500 | 0.5100 | 0.2519 | |
| | | 0.0721 | 14.0562 | 7000 | 0.4795 | 0.2536 | |
| | | 0.0696 | 15.0602 | 7500 | 0.4797 | 0.2456 | |
| | | 0.0598 | 16.0643 | 8000 | 0.5064 | 0.2410 | |
| | | 0.0575 | 17.0683 | 8500 | 0.5075 | 0.2362 | |
| | | 0.0508 | 18.0723 | 9000 | 0.5062 | 0.2420 | |
| | | 0.048 | 19.0763 | 9500 | 0.5078 | 0.2397 | |
| | | 0.0402 | 20.0803 | 10000 | 0.5511 | 0.2341 | |
| | | 0.0429 | 21.0843 | 10500 | 0.4835 | 0.2330 | |
| | | 0.0362 | 22.0884 | 11000 | 0.5800 | 0.2308 | |
| | | 0.0333 | 23.0924 | 11500 | 0.5250 | 0.2306 | |
| | | 0.0285 | 24.0964 | 12000 | 0.5242 | 0.2288 | |
| | | 0.0296 | 25.1004 | 12500 | 0.4995 | 0.2238 | |
| | | 0.0264 | 26.1044 | 13000 | 0.5296 | 0.2236 | |
| | | 0.0245 | 27.1084 | 13500 | 0.5530 | 0.2233 | |
| | | 0.0214 | 28.1124 | 14000 | 0.5376 | 0.2209 | |
| | | 0.0214 | 29.1165 | 14500 | 0.5351 | 0.2211 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.56.2 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 2.21.0 |
| | - Tokenizers 0.22.1 |
| |
|
