---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-sw-1hr-v1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_8_0
      type: common_voice_8_0
      config: sw
      split: test
      args: sw
    metrics:
    - name: Wer
      type: wer
      value: 0.5901667526216263
---

<!-- 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. -->

# wav2vec2-large-xls-r-300m-sw-1hr-v1

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_8_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8004
- Wer: 0.5902
- Cer: 0.1498

## 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: 0.0001
- 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: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 10.7706       | 4.6512  | 100  | 5.0262          | 1.0    | 1.0    |
| 3.7038        | 9.3023  | 200  | 3.2132          | 1.0    | 1.0    |
| 2.9571        | 13.9535 | 300  | 2.8597          | 1.0    | 1.0    |
| 2.7859        | 18.6047 | 400  | 2.6007          | 1.0    | 0.7810 |
| 1.2103        | 23.2558 | 500  | 0.8662          | 0.6976 | 0.1859 |
| 0.3075        | 27.9070 | 600  | 0.7534          | 0.6533 | 0.1695 |
| 0.1911        | 32.5581 | 700  | 0.7585          | 0.6282 | 0.1607 |
| 0.1482        | 37.2093 | 800  | 0.8062          | 0.6340 | 0.1667 |
| 0.1241        | 41.8605 | 900  | 0.7999          | 0.6190 | 0.1605 |
| 0.1085        | 46.5116 | 1000 | 0.8105          | 0.6001 | 0.1524 |
| 0.0935        | 51.1628 | 1100 | 0.7972          | 0.5914 | 0.1502 |
| 0.0833        | 55.8140 | 1200 | 0.7978          | 0.5931 | 0.1505 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1