---
library_name: transformers
license: apache-2.0
base_model: Samuael/geez-asr
tags:
- generated_from_trainer
datasets:
- alffa_amharic
metrics:
- wer
model-index:
- name: ethiopic-asr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: alffa_amharic
      type: alffa_amharic
      config: clean
      split: None
      args: clean
    metrics:
    - name: Wer
      type: wer
      value: 0.14692601597777005
---

<!-- 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. -->

# ethiopic-asr

This model is a fine-tuned version of [Samuael/geez-asr](https://huggingface.co/Samuael/geez-asr) on the alffa_amharic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1301
- Wer: 0.1469
- Phoneme Cer: 0.0296
- Cer: 0.0416

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Phoneme Cer | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:-----------:|:------:|
| No log        | 0.0442 | 200  | 3.2216          | 1.0    | 1.0         | 1.0    |
| No log        | 0.0883 | 400  | 3.1164          | 1.0    | 1.0         | 1.0    |
| 4.1769        | 0.1325 | 600  | 0.9628          | 0.5476 | 0.1141      | 0.1609 |
| 4.1769        | 0.1767 | 800  | 0.3181          | 0.2150 | 0.0430      | 0.0607 |
| 0.8455        | 0.2208 | 1000 | 0.2195          | 0.1759 | 0.0353      | 0.0503 |
| 0.8455        | 0.2650 | 1200 | 0.1913          | 0.1846 | 0.0365      | 0.0520 |
| 0.8455        | 0.3092 | 1400 | 0.1699          | 0.1591 | 0.0322      | 0.0454 |
| 0.2929        | 0.3534 | 1600 | 0.1603          | 0.1572 | 0.0316      | 0.0442 |
| 0.2929        | 0.3975 | 1800 | 0.1503          | 0.1567 | 0.0315      | 0.0442 |
| 0.2392        | 0.4417 | 2000 | 0.1476          | 0.1587 | 0.0318      | 0.0446 |
| 0.2392        | 0.4859 | 2200 | 0.1449          | 0.1565 | 0.0312      | 0.0438 |
| 0.2392        | 0.5300 | 2400 | 0.1409          | 0.1537 | 0.0308      | 0.0427 |
| 0.2166        | 0.5742 | 2600 | 0.1395          | 0.1551 | 0.0308      | 0.0428 |
| 0.2166        | 0.6184 | 2800 | 0.1345          | 0.1469 | 0.0290      | 0.0410 |
| 0.2068        | 0.6625 | 3000 | 0.1331          | 0.1509 | 0.0297      | 0.0419 |
| 0.2068        | 0.7067 | 3200 | 0.1346          | 0.1518 | 0.0301      | 0.0421 |
| 0.2068        | 0.7509 | 3400 | 0.1335          | 0.1507 | 0.0303      | 0.0426 |
| 0.2037        | 0.7951 | 3600 | 0.1312          | 0.1471 | 0.0297      | 0.0415 |
| 0.2037        | 0.8392 | 3800 | 0.1303          | 0.1438 | 0.0289      | 0.0406 |
| 0.1985        | 0.8834 | 4000 | 0.1300          | 0.1457 | 0.0292      | 0.0410 |
| 0.1985        | 0.9276 | 4200 | 0.1303          | 0.1471 | 0.0295      | 0.0414 |
| 0.1985        | 0.9717 | 4400 | 0.1301          | 0.1469 | 0.0296      | 0.0416 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0