Whisper largeV2 dutch MLS
This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/multilingual_librispeech dutch dataset. It achieves the following results on the evaluation set:
- Loss: 0.2031
- Wer: 10.5916
Model description
The model is fine-tuned for 4000 updates/steps on multilingual librispeech Dutch train data.
- Zero-shot - 9.3 (MLS Dutch test)
- Fine-tune MLS Dutch train - 10.59 (MLS Dutch test)
Even after fine-tuning the model is doing worse than the zero-shot model.
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2515 | 0.25 | 1000 | 0.2579 | 12.9776 |
| 0.24 | 0.5 | 2000 | 0.2361 | 11.2418 |
| 0.1308 | 0.75 | 3000 | 0.2335 | 10.7503 |
| 0.1072 | 1.0 | 4000 | 0.2031 | 10.5916 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
- Downloads last month
- 16
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train sgangireddy/whisper-largev2-mls-dutch
Space using sgangireddy/whisper-largev2-mls-dutch 1
Evaluation results
- Wer on facebook/multilingual_librispeech dutchtest set self-reported10.592