---
library_name: transformers
base_model: vasista22/ccc-wav2vec2-base-SUPERB
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: superb-wav2vec2
  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. -->

# superb-wav2vec2

This model is a fine-tuned version of [vasista22/ccc-wav2vec2-base-SUPERB](https://huggingface.co/vasista22/ccc-wav2vec2-base-SUPERB) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Wer: 0.0233

## 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: 0.0004
- 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: 132
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.0211        | 0.4082  | 50   | 2.0267          | 0.9861 |
| 1.9496        | 0.8163  | 100  | 1.7685          | 0.9849 |
| 1.7178        | 1.2245  | 150  | 1.4738          | 0.8240 |
| 1.3801        | 1.6327  | 200  | 1.1281          | 0.8227 |
| 1.189         | 2.0408  | 250  | 0.8568          | 0.5723 |
| 0.9318        | 2.4490  | 300  | 0.6622          | 0.5615 |
| 0.7042        | 2.8571  | 350  | 0.3612          | 0.3023 |
| 0.5805        | 3.2653  | 400  | 0.4220          | 0.4606 |
| 0.4229        | 3.6735  | 450  | 0.1465          | 0.1417 |
| 0.3913        | 4.0816  | 500  | 0.1350          | 0.1688 |
| 0.2645        | 4.4898  | 550  | 0.1030          | 0.1421 |
| 0.2809        | 4.8980  | 600  | 0.0867          | 0.0977 |
| 0.2344        | 5.3061  | 650  | 0.0901          | 0.1367 |
| 0.1703        | 5.7143  | 700  | 0.0659          | 0.1246 |
| 0.1718        | 6.1224  | 750  | 0.0432          | 0.0545 |
| 0.1442        | 6.5306  | 800  | 0.0636          | 0.0824 |
| 0.1494        | 6.9388  | 850  | 0.0431          | 0.0448 |
| 0.1492        | 7.3469  | 900  | 0.0328          | 0.0478 |
| 0.1185        | 7.7551  | 950  | 0.0376          | 0.0621 |
| 0.107         | 8.1633  | 1000 | 0.0249          | 0.0241 |
| 0.1159        | 8.5714  | 1050 | 0.0350          | 0.0396 |
| 0.1015        | 8.9796  | 1100 | 0.0232          | 0.0334 |
| 0.1203        | 9.3878  | 1150 | 0.0341          | 0.0780 |
| 0.0835        | 9.7959  | 1200 | 0.0178          | 0.0458 |
| 0.1239        | 10.2041 | 1250 | 0.0231          | 0.0543 |
| 0.0859        | 10.6122 | 1300 | 0.0163          | 0.0289 |
| 0.0732        | 11.0204 | 1350 | 0.0309          | 0.0494 |
| 0.063         | 11.4286 | 1400 | 0.0168          | 0.0963 |
| 0.0693        | 11.8367 | 1450 | 0.0268          | 0.0619 |
| 0.0649        | 12.2449 | 1500 | 0.0328          | 0.0687 |
| 0.063         | 12.6531 | 1550 | 0.0173          | 0.0438 |
| 0.0574        | 13.0612 | 1600 | 0.0118          | 0.0506 |
| 0.0438        | 13.4694 | 1650 | 0.0101          | 0.0510 |
| 0.0556        | 13.8776 | 1700 | 0.0064          | 0.0291 |
| 0.0536        | 14.2857 | 1750 | 0.0098          | 0.0225 |
| 0.047         | 14.6939 | 1800 | 0.0157          | 0.0251 |
| 0.0588        | 15.1020 | 1850 | 0.0097          | 0.0291 |
| 0.0397        | 15.5102 | 1900 | 0.0113          | 0.0541 |
| 0.0375        | 15.9184 | 1950 | 0.0173          | 0.0531 |
| 0.0411        | 16.3265 | 2000 | 0.0079          | 0.0394 |
| 0.0382        | 16.7347 | 2050 | 0.0056          | 0.0340 |
| 0.0448        | 17.1429 | 2100 | 0.0064          | 0.0287 |
| 0.0359        | 17.5510 | 2150 | 0.0053          | 0.0261 |
| 0.032         | 17.9592 | 2200 | 0.0091          | 0.0400 |
| 0.0295        | 18.3673 | 2250 | 0.0018          | 0.0275 |
| 0.03          | 18.7755 | 2300 | 0.0034          | 0.0259 |
| 0.0246        | 19.1837 | 2350 | 0.0280          | 0.0368 |
| 0.0465        | 19.5918 | 2400 | 0.0099          | 0.0297 |
| 0.0264        | 20.0    | 2450 | 0.0063          | 0.0111 |
| 0.025         | 20.4082 | 2500 | 0.0015          | 0.0370 |
| 0.04          | 20.8163 | 2550 | 0.0020          | 0.0344 |
| 0.0203        | 21.2245 | 2600 | 0.0055          | 0.0356 |
| 0.0241        | 21.6327 | 2650 | 0.0024          | 0.0299 |
| 0.0465        | 22.0408 | 2700 | 0.0022          | 0.0392 |
| 0.0283        | 22.4490 | 2750 | 0.0026          | 0.0149 |
| 0.0134        | 22.8571 | 2800 | 0.0015          | 0.0177 |
| 0.0177        | 23.2653 | 2850 | 0.0041          | 0.0177 |
| 0.0288        | 23.6735 | 2900 | 0.0011          | 0.0147 |
| 0.0216        | 24.0816 | 2950 | 0.0034          | 0.0287 |
| 0.0147        | 24.4898 | 3000 | 0.0046          | 0.0155 |
| 0.0118        | 24.8980 | 3050 | 0.0021          | 0.0235 |
| 0.0113        | 25.3061 | 3100 | 0.0012          | 0.0261 |
| 0.0135        | 25.7143 | 3150 | 0.0006          | 0.0261 |
| 0.0118        | 26.1224 | 3200 | 0.0008          | 0.0287 |
| 0.0083        | 26.5306 | 3250 | 0.0004          | 0.0257 |
| 0.0148        | 26.9388 | 3300 | 0.0006          | 0.0261 |
| 0.0081        | 27.3469 | 3350 | 0.0005          | 0.0263 |
| 0.0192        | 27.7551 | 3400 | 0.0004          | 0.0237 |
| 0.0096        | 28.1633 | 3450 | 0.0004          | 0.0231 |
| 0.0083        | 28.5714 | 3500 | 0.0003          | 0.0215 |
| 0.0056        | 28.9796 | 3550 | 0.0004          | 0.0233 |
| 0.0082        | 29.3878 | 3600 | 0.0003          | 0.0233 |
| 0.0102        | 29.7959 | 3650 | 0.0003          | 0.0233 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1