---
library_name: transformers
language:
- sw
base_model: EYEDOL/FROM_C3_4
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: ASR_FROM_C3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13.0
      type: mozilla-foundation/common_voice_13_0
      config: sw
      split: None
      args: 'config: sw, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 17.669860078154546
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ASR_FROM_C3

This model is a fine-tuned version of [EYEDOL/FROM_C3_4](https://huggingface.co/EYEDOL/FROM_C3_4) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2687
- Wer: 17.6699

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0239        | 0.6918 | 2000 | 0.2687          | 17.6699 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2