---
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-Fleurs_AMMI_AFRIVOICE_LRSC-ln-5hrs-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-5hrs-v1

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: 0.6643
- Wer: 0.2469
- Cer: 0.0788

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 1.8874        | 0.9949  | 98   | 0.6403          | 0.5429 | 0.1657 |
| 0.4899        | 2.0     | 197  | 0.4921          | 0.3300 | 0.1001 |
| 0.3892        | 2.9949  | 295  | 0.4608          | 0.3314 | 0.1019 |
| 0.3259        | 4.0     | 394  | 0.4729          | 0.3080 | 0.0942 |
| 0.2863        | 4.9949  | 492  | 0.4495          | 0.3156 | 0.0951 |
| 0.2333        | 6.0     | 591  | 0.4269          | 0.2624 | 0.0808 |
| 0.2059        | 6.9949  | 689  | 0.4365          | 0.2609 | 0.0839 |
| 0.1722        | 8.0     | 788  | 0.4346          | 0.2552 | 0.0825 |
| 0.1551        | 8.9949  | 886  | 0.4134          | 0.2468 | 0.0766 |
| 0.1318        | 10.0    | 985  | 0.4794          | 0.2631 | 0.0811 |
| 0.1189        | 10.9949 | 1083 | 0.5191          | 0.2530 | 0.0796 |
| 0.1004        | 12.0    | 1182 | 0.5311          | 0.2689 | 0.0794 |
| 0.0959        | 12.9949 | 1280 | 0.5502          | 0.2535 | 0.0778 |
| 0.0831        | 14.0    | 1379 | 0.5060          | 0.2476 | 0.0757 |
| 0.0679        | 14.9949 | 1477 | 0.5023          | 0.2517 | 0.0830 |
| 0.0617        | 16.0    | 1576 | 0.5279          | 0.2403 | 0.0757 |
| 0.0562        | 16.9949 | 1674 | 0.6012          | 0.2411 | 0.0761 |
| 0.0496        | 18.0    | 1773 | 0.6263          | 0.2423 | 0.0755 |
| 0.0442        | 18.9949 | 1871 | 0.5991          | 0.2581 | 0.0794 |
| 0.0401        | 20.0    | 1970 | 0.6323          | 0.2412 | 0.0762 |
| 0.0329        | 20.9949 | 2068 | 0.6417          | 0.2326 | 0.0735 |
| 0.0266        | 22.0    | 2167 | 0.6279          | 0.2381 | 0.0756 |
| 0.0255        | 22.9949 | 2265 | 0.5834          | 0.2470 | 0.0772 |
| 0.0214        | 24.0    | 2364 | 0.6781          | 0.2364 | 0.0735 |
| 0.0217        | 24.9949 | 2462 | 0.6253          | 0.2398 | 0.0752 |
| 0.0163        | 26.0    | 2561 | 0.6940          | 0.2427 | 0.0813 |
| 0.0363        | 26.9949 | 2659 | 0.6632          | 0.2363 | 0.0756 |
| 0.0182        | 28.0    | 2758 | 0.6094          | 0.2363 | 0.0766 |
| 0.014         | 28.9949 | 2856 | 0.6928          | 0.2438 | 0.0770 |
| 0.0157        | 30.0    | 2955 | 0.6863          | 0.2422 | 0.0768 |
| 0.0121        | 30.9949 | 3053 | 0.6643          | 0.2469 | 0.0788 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3