--- language: - ig license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - deepdml/igbo-dict-16khz - deepdml/igbo-dict-expansion-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 metrics: - name: Wer type: wer value: 93.38037030379292 --- # Whisper Base ig This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs dataset. It achieves the following results on the evaluation set: - Loss: 1.7179 - Wer: 93.3804 ## 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: 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 | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.2396 | 0.2 | 1000 | 1.3704 | 57.7791 | | 0.0803 | 1.0814 | 2000 | 1.5414 | 71.3104 | | 0.0636 | 1.2814 | 3000 | 1.6047 | 94.5668 | | 0.0346 | 2.1628 | 4000 | 1.6904 | 83.7003 | | 0.035 | 3.0442 | 5000 | 1.7179 | 93.3804 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ## Citation ```bibtex @misc{deepdml/whisper-base-ig-mix, title={Fine-tuned Whisper base ASR model for speech recognition in Igbo}, author={Jimenez, David}, howpublished={\url{https://huggingface.co/deepdml/whisper-base-ig-mix}}, year={2025} } ```