---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-igbo_naijavoices_500h
  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. -->

# w2v-bert-2.0-igbo_naijavoices_500h

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2218
- Wer: 0.2455
- Cer: 0.1105

## 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: 3e-05
- train_batch_size: 160
- eval_batch_size: 160
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 320
- total_eval_batch_size: 320
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.923         | 0.6901  | 1000  | 0.6313          | 0.5401 | 0.2069 |
| 0.4648        | 1.3803  | 2000  | 0.4222          | 0.3868 | 0.1549 |
| 0.3635        | 2.0704  | 3000  | 0.3582          | 0.3499 | 0.1434 |
| 0.3388        | 2.7605  | 4000  | 0.3368          | 0.3322 | 0.1383 |
| 0.2967        | 3.4507  | 5000  | 0.3141          | 0.3191 | 0.1366 |
| 0.2738        | 4.1408  | 6000  | 0.3041          | 0.3151 | 0.1328 |
| 0.3146        | 4.8309  | 7000  | 0.2972          | 0.3091 | 0.1297 |
| 0.2612        | 5.5210  | 8000  | 0.2856          | 0.2998 | 0.1312 |
| 0.282         | 6.2112  | 9000  | 0.2873          | 0.3001 | 0.1300 |
| 0.2989        | 6.9013  | 10000 | 0.2864          | 0.2959 | 0.1309 |
| 0.2633        | 7.5914  | 11000 | 0.2660          | 0.2883 | 0.1242 |
| 0.2471        | 8.2816  | 12000 | 0.2674          | 0.2905 | 0.1263 |
| 0.2746        | 8.9717  | 13000 | 0.2671          | 0.2822 | 0.1224 |
| 0.2754        | 9.6618  | 14000 | 0.2617          | 0.2833 | 0.1234 |
| 0.2881        | 10.3520 | 15000 | 0.2596          | 0.2833 | 0.1229 |
| 0.2717        | 11.0421 | 16000 | 0.2524          | 0.2760 | 0.1221 |
| 0.2204        | 11.7322 | 17000 | 0.2513          | 0.2720 | 0.1197 |
| 0.2429        | 12.4224 | 18000 | 0.2530          | 0.2738 | 0.1203 |
| 0.2429        | 13.1125 | 19000 | 0.2511          | 0.2745 | 0.1194 |
| 0.2449        | 13.8026 | 20000 | 0.2555          | 0.2748 | 0.1209 |
| 0.2053        | 14.4928 | 21000 | 0.2464          | 0.2719 | 0.1181 |
| 0.222         | 15.1829 | 22000 | 0.2428          | 0.2659 | 0.1195 |
| 0.1874        | 15.8730 | 23000 | 0.2418          | 0.2609 | 0.1156 |
| 0.1924        | 16.5631 | 24000 | 0.2363          | 0.2675 | 0.1176 |
| 0.1855        | 17.2533 | 25000 | 0.2336          | 0.2629 | 0.1151 |
| 0.2172        | 17.9434 | 26000 | 0.2302          | 0.2633 | 0.1153 |
| 0.2074        | 18.6335 | 27000 | 0.2345          | 0.2588 | 0.1161 |
| 0.1589        | 19.3237 | 28000 | 0.2204          | 0.2474 | 0.1137 |
| 0.1382        | 20.0138 | 29000 | 0.2279          | 0.2531 | 0.1127 |
| 0.1525        | 20.7039 | 30000 | 0.2284          | 0.2550 | 0.1128 |
| 0.149         | 21.3941 | 31000 | 0.2221          | 0.2509 | 0.1122 |
| 0.1193        | 22.0842 | 32000 | 0.2229          | 0.2482 | 0.1113 |
| 0.1245        | 22.7743 | 33000 | 0.2218          | 0.2455 | 0.1105 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2