| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - audiofolder |
| metrics: |
| - wer |
| model-index: |
| - name: csml_word2vec |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: audiofolder |
| type: audiofolder |
| config: default |
| split: None |
| args: default |
| metrics: |
| - name: Wer |
| type: wer |
| value: 0.12267657992565056 |
| --- |
| |
| <!-- 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. --> |
|
|
| # csml_word2vec |
| |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2183 |
| - Wer: 0.1227 |
| |
| ## 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: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - 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: 500 |
| - training_steps: 2000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | 3.2282 | 1.0 | 100 | 3.1130 | 1.0 | |
| | 2.9562 | 2.0 | 200 | 2.9543 | 1.0 | |
| | 2.9212 | 3.0 | 300 | 2.9317 | 1.0 | |
| | 2.9119 | 4.0 | 400 | 2.9110 | 1.0 | |
| | 2.9099 | 5.0 | 500 | 2.9099 | 1.0 | |
| | 2.9027 | 6.0 | 600 | 2.9106 | 1.0 | |
| | 2.4303 | 7.0 | 700 | 1.9136 | 0.9615 | |
| | 0.7427 | 8.0 | 800 | 0.5675 | 0.4321 | |
| | 0.4794 | 9.0 | 900 | 0.4161 | 0.3183 | |
| | 0.3372 | 10.0 | 1000 | 0.3228 | 0.2441 | |
| | 0.2685 | 11.0 | 1100 | 0.2762 | 0.1916 | |
| | 0.2092 | 12.0 | 1200 | 0.2522 | 0.1654 | |
| | 0.1848 | 13.0 | 1300 | 0.2361 | 0.1564 | |
| | 0.1678 | 14.0 | 1400 | 0.2246 | 0.1383 | |
| | 0.1377 | 15.0 | 1500 | 0.2183 | 0.1337 | |
| | 0.122 | 16.0 | 1600 | 0.2206 | 0.1310 | |
| | 0.1125 | 17.0 | 1700 | 0.2205 | 0.1285 | |
| | 0.0938 | 18.0 | 1800 | 0.2219 | 0.1273 | |
| | 0.0994 | 19.0 | 1900 | 0.2209 | 0.1239 | |
| | 0.1 | 20.0 | 2000 | 0.2183 | 0.1227 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.1 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
|
|
