---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/naijavoices_500h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-hausa_naijavoices_500h
  results: []
---

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# w2v-bert-2.0-hausa_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 CLEAR-GLOBAL/NAIJAVOICES_500H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2268
- Wer: 0.3250
- Cer: 0.1875

## 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 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.7206        | 0.5587  | 1000  | 0.4335          | 0.4167 | 0.2119 |
| 0.2797        | 1.1173  | 2000  | 0.3227          | 0.3731 | 0.1999 |
| 0.2159        | 1.6760  | 3000  | 0.2890          | 0.3517 | 0.1951 |
| 0.2734        | 2.2346  | 4000  | 0.2733          | 0.3458 | 0.1932 |
| 0.1894        | 2.7933  | 5000  | 0.2602          | 0.3431 | 0.1917 |
| 0.2076        | 3.3520  | 6000  | 0.2577          | 0.3413 | 0.1918 |
| 0.2168        | 3.9106  | 7000  | 0.2481          | 0.3386 | 0.1911 |
| 0.1709        | 4.4693  | 8000  | 0.2492          | 0.3407 | 0.1913 |
| 0.3026        | 5.0279  | 9000  | 0.2520          | 0.3386 | 0.1914 |
| 0.2794        | 5.5866  | 10000 | 0.2597          | 0.3395 | 0.1910 |
| 0.1957        | 6.1453  | 11000 | 0.2412          | 0.3370 | 0.1903 |
| 0.1605        | 6.7039  | 12000 | 0.2393          | 0.3319 | 0.1893 |
| 0.1902        | 7.2626  | 13000 | 0.2426          | 0.3335 | 0.1896 |
| 0.1637        | 7.8212  | 14000 | 0.2348          | 0.3319 | 0.1888 |
| 0.1693        | 8.3799  | 15000 | 0.2320          | 0.3289 | 0.1885 |
| 0.1584        | 8.9385  | 16000 | 0.2341          | 0.3298 | 0.1885 |
| 0.1671        | 9.4972  | 17000 | 0.2274          | 0.3253 | 0.1876 |
| 0.244         | 10.0559 | 18000 | 0.2337          | 0.3305 | 0.1885 |
| 0.2167        | 10.6145 | 19000 | 0.2335          | 0.3264 | 0.1877 |
| 0.165         | 11.1732 | 20000 | 0.2276          | 0.3269 | 0.1875 |
| 0.1679        | 11.7318 | 21000 | 0.2303          | 0.3322 | 0.1886 |
| 0.1746        | 12.2905 | 22000 | 0.2277          | 0.3247 | 0.1867 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1