---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/chichewa_34h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-chichewa_34h
  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-chichewa_34h

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/CHICHEWA_34H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3389
- Wer: 0.4045
- Cer: 0.1148

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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
- training_steps: 100000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 0.4609        | 5.6197  | 1000 | 0.7327          | 0.6746 | 0.1953 |
| 0.1207        | 11.2366 | 2000 | 0.4130          | 0.4797 | 0.1341 |
| 0.1104        | 16.8563 | 3000 | 0.3404          | 0.4165 | 0.1182 |
| 0.0417        | 22.4732 | 4000 | 0.3389          | 0.4046 | 0.1149 |
| 0.0849        | 28.0901 | 5000 | 0.3593          | 0.3860 | 0.1110 |
| 0.0169        | 33.7099 | 6000 | 0.4053          | 0.3799 | 0.1086 |
| 0.0625        | 39.3268 | 7000 | 0.4394          | 0.3820 | 0.1103 |
| 0.0226        | 44.9465 | 8000 | 0.4477          | 0.3922 | 0.1099 |
| 0.0236        | 50.5634 | 9000 | 0.4660          | 0.3855 | 0.1101 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1