---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Amharic FLEURS
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs am_et
      type: google/fleurs
      config: am_et
      split: validation
      args: am_et
    metrics:
    - name: Wer
      type: wer
      value: 100.0
---

<!-- 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. -->

# Whisper Small Amharic FLEURS

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs am_et dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8012
- Wer: 100.0

## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9013        | 100.0  | 100  | 2.7051          | 276.0    |
| 0.0002        | 200.0  | 200  | 3.7415          | 334.6667 |
| 0.0001        | 300.0  | 300  | 3.8402          | 117.3333 |
| 0.0001        | 400.0  | 400  | 3.8931          | 340.0    |
| 0.0001        | 500.0  | 500  | 4.0671          | 397.3333 |
| 0.0001        | 600.0  | 600  | 4.2844          | 137.3333 |
| 0.0           | 700.0  | 700  | 4.4697          | 289.3333 |
| 0.0           | 800.0  | 800  | 4.6278          | 449.3333 |
| 0.0           | 900.0  | 900  | 4.7794          | 678.6667 |
| 0.0405        | 1000.0 | 1000 | 4.6769          | 261.3333 |
| 0.0002        | 1100.0 | 1100 | 5.4995          | 100.0    |
| 0.0002        | 1200.0 | 1200 | 6.0033          | 100.0    |
| 0.0002        | 1300.0 | 1300 | 6.2884          | 100.0    |
| 0.0002        | 1400.0 | 1400 | 6.4744          | 100.0    |
| 0.0002        | 1500.0 | 1500 | 6.5964          | 100.0    |
| 0.0001        | 1600.0 | 1600 | 6.6792          | 100.0    |
| 0.0001        | 1700.0 | 1700 | 6.7370          | 100.0    |
| 0.0001        | 1800.0 | 1800 | 6.7735          | 100.0    |
| 0.0001        | 1900.0 | 1900 | 6.7958          | 100.0    |
| 0.0001        | 2000.0 | 2000 | 6.8012          | 100.0    |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2