--- 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](https://huggingface.co/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 ```bibtex @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} } ```