metadata
language:
- ig
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- google/fleurs
- deepdml/igbo-dict-expansion-16khz
- deepdml/igbo-dict-16khz
metrics:
- wer
model-index:
- name: Whisper Base ig
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ig_ng
split: test
args: ig_ng
metrics:
- name: Wer
type: wer
value: 54.948739128322245
Whisper Base ig
This model is a fine-tuned version of openai/whisper-base on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.0933
- Wer: 54.9487
- Cer: 21.3532
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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.2087 | 0.2 | 1000 | 0.8427 | 54.4143 | 20.1160 |
| 0.0734 | 1.0814 | 2000 | 0.9702 | 55.5707 | 21.6200 |
| 0.0609 | 1.2814 | 3000 | 1.0272 | 54.0256 | 20.4927 |
| 0.0336 | 2.1628 | 4000 | 1.0804 | 54.4337 | 20.4677 |
| 0.0341 | 3.0442 | 5000 | 1.0933 | 54.9487 | 21.3532 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Citation
Please cite the model using the following BibTeX entry:
@misc{deepdml/whisper-base-ig-mix-norm,
title={Fine-tuned Whisper base ASR model for speech recognition in Lingala},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-base-ig-mix-norm}},
year={2025}
}