---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/chichewa_34_136h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-chichewa_34_136h
  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_34_136h

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_34_136H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Wer: 0.4020
- Cer: 0.1153

## 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 2.7429        | 0.6122  | 1000  | 2.9154          | 0.9860 | 0.8820 |
| 0.1586        | 1.2241  | 2000  | 0.7989          | 0.6341 | 0.1888 |
| 0.0475        | 1.8362  | 3000  | 0.7777          | 0.5725 | 0.1637 |
| 0.0452        | 2.4481  | 4000  | 0.4482          | 0.5083 | 0.1482 |
| 0.0387        | 3.0600  | 5000  | 0.4168          | 0.4770 | 0.1396 |
| 0.0454        | 3.6722  | 6000  | 0.3792          | 0.4501 | 0.1306 |
| 0.0215        | 4.2841  | 7000  | 0.3758          | 0.4564 | 0.1324 |
| 0.0342        | 4.8962  | 8000  | 0.3737          | 0.4557 | 0.1298 |
| 0.0243        | 5.5081  | 9000  | 0.3805          | 0.4325 | 0.1252 |
| 0.0183        | 6.1200  | 10000 | 0.3490          | 0.4257 | 0.1240 |
| 0.0253        | 6.7322  | 11000 | 0.3670          | 0.4185 | 0.1199 |
| 0.0115        | 7.3440  | 12000 | 0.3664          | 0.4125 | 0.1207 |
| 0.0141        | 7.9562  | 13000 | 0.2952          | 0.4021 | 0.1153 |
| 0.0141        | 8.5681  | 14000 | 0.3231          | 0.4031 | 0.1133 |
| 0.0082        | 9.1800  | 15000 | 0.3209          | 0.4000 | 0.1141 |
| 0.0214        | 9.7922  | 16000 | 0.3115          | 0.3985 | 0.1134 |
| 0.0146        | 10.4040 | 17000 | 0.3092          | 0.3743 | 0.1089 |
| 0.0367        | 11.0159 | 18000 | 0.3207          | 0.3914 | 0.1153 |


### Framework versions

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