---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
base_model: facebook/wav2vec2-large-xlsr-53
model-index:
- name: ASR-Somali
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ASR-Somali

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3660
- Wer: 0.3060

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1435        | 2.09  | 400  | 0.7624          | 0.7706 |
| 0.5829        | 4.18  | 800  | 0.3646          | 0.3935 |
| 0.3634        | 6.27  | 1200 | 0.3318          | 0.3944 |
| 0.2942        | 8.36  | 1600 | 0.3148          | 0.3403 |
| 0.2419        | 10.44 | 2000 | 0.3000          | 0.3255 |
| 0.2104        | 12.53 | 2400 | 0.2951          | 0.3312 |
| 0.1864        | 14.62 | 2800 | 0.3296          | 0.3083 |
| 0.1666        | 16.71 | 3200 | 0.3264          | 0.3153 |
| 0.148         | 18.8  | 3600 | 0.3188          | 0.3028 |
| 0.1305        | 20.89 | 4000 | 0.3448          | 0.3002 |
| 0.1206        | 22.98 | 4400 | 0.3660          | 0.3060 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 1.18.3
- Tokenizers 0.13.3