Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Dutch
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use pplantinga/whisper-small-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pplantinga/whisper-small-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pplantinga/whisper-small-nl")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("pplantinga/whisper-small-nl") model = AutoModelForSpeechSeq2Seq.from_pretrained("pplantinga/whisper-small-nl") - Notebooks
- Google Colab
- Kaggle
Whisper Small Dutch
This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 nl dataset. It achieves the following results on the evaluation set:
- Loss: 0.2035
- Wer: 11.5135
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: 112
- eval_batch_size: 56
- 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: 500
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Model tree for pplantinga/whisper-small-nl
Base model
openai/whisper-smallEvaluation results
- Wer on mozilla-foundation/common_voice_11_0 nltest set self-reported11.514