---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/chichewa_34_102h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-chichewa_34_102h
  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_102h

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_102H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2991
- Wer: 0.3874
- Cer: 0.1111

## 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 2.2628        | 0.7877  | 1000  | 2.6113          | 0.9981 | 0.7664 |
| 0.1158        | 1.5750  | 2000  | 0.7048          | 0.6111 | 0.1786 |
| 0.0535        | 2.3623  | 3000  | 0.5161          | 0.5307 | 0.1527 |
| 0.0471        | 3.1497  | 4000  | 0.4501          | 0.4873 | 0.1434 |
| 0.0452        | 3.9374  | 5000  | 0.4284          | 0.4806 | 0.1410 |
| 0.0277        | 4.7247  | 6000  | 0.3880          | 0.4649 | 0.1387 |
| 0.0441        | 5.5120  | 7000  | 0.4015          | 0.4461 | 0.1294 |
| 0.0177        | 6.2993  | 8000  | 0.3798          | 0.4290 | 0.1209 |
| 0.0198        | 7.0866  | 9000  | 0.3330          | 0.4027 | 0.1171 |
| 0.0141        | 7.8744  | 10000 | 0.3333          | 0.4307 | 0.1213 |
| 0.0237        | 8.6617  | 11000 | 0.3653          | 0.4294 | 0.1259 |
| 0.014         | 9.4490  | 12000 | 0.3118          | 0.4048 | 0.1162 |
| 0.0079        | 10.2363 | 13000 | 0.2991          | 0.3874 | 0.1109 |
| 0.0106        | 11.0236 | 14000 | 0.3455          | 0.4008 | 0.1193 |
| 0.0089        | 11.8113 | 15000 | 0.3658          | 0.4091 | 0.1249 |
| 0.0068        | 12.5987 | 16000 | 0.3054          | 0.3918 | 0.1124 |
| 0.007         | 13.3860 | 17000 | 0.3255          | 0.3785 | 0.1114 |
| 0.0108        | 14.1733 | 18000 | 0.3393          | 0.4045 | 0.1152 |


### Framework versions

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