metadata
language:
- ig
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- deepdml/igbo-dict-16khz
- deepdml/igbo-dict-expansion-16khz
metrics:
- wer
model-index:
- name: Whisper Medium ig
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ig_ng
split: test
metrics:
- name: Wer
type: wer
value: 36.62142728743484
Whisper Medium ig
This model is a fine-tuned version of openai/whisper-medium on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.5395
- Wer: 36.6214
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: 32
- 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: 500
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1362 | 0.2 | 1000 | 1.2088 | 40.5087 |
| 0.0549 | 0.4 | 2000 | 1.3555 | 39.1381 |
| 0.0268 | 0.6 | 3000 | 1.4718 | 38.2932 |
| 0.0085 | 1.163 | 4000 | 1.5330 | 36.7742 |
| 0.0166 | 1.363 | 5000 | 1.5395 | 36.6214 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Citation
@misc{deepdml/whisper-medium-ig-mix,
title={Fine-tuned Whisper medium ASR model for speech recognition in Igbo},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-medium-ig-mix}},
year={2025}
}