| --- |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: asr_model |
| 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. --> |
|
|
| # asr_model |
| |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2363 |
| - Wer: 0.5153 |
| |
| ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 30 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 0.4621 | 2.0 | 1000 | 0.4702 | 0.9741 | |
| | 0.4612 | 4.0 | 2000 | 0.4621 | 0.9741 | |
| | 0.4458 | 6.0 | 3000 | 0.4464 | 0.9714 | |
| | 0.384 | 8.0 | 4000 | 0.3853 | 0.8235 | |
| | 0.3065 | 10.0 | 5000 | 0.3166 | 0.7829 | |
| | 0.2861 | 12.0 | 6000 | 0.2809 | 0.6802 | |
| | 0.248 | 14.0 | 7000 | 0.2677 | 0.6051 | |
| | 0.2449 | 16.0 | 8000 | 0.2541 | 0.5778 | |
| | 0.2298 | 18.0 | 9000 | 0.2480 | 0.5710 | |
| | 0.2281 | 20.0 | 10000 | 0.2418 | 0.5505 | |
| | 0.216 | 22.0 | 11000 | 0.2420 | 0.5340 | |
| | 0.2083 | 24.0 | 12000 | 0.2380 | 0.5253 | |
| | 0.1957 | 26.0 | 13000 | 0.2380 | 0.5209 | |
| | 0.1985 | 28.0 | 14000 | 0.2360 | 0.5181 | |
| | 0.2078 | 30.0 | 15000 | 0.2363 | 0.5153 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
| |
