Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Dutch
whisper
Generated from Trainer
Instructions to use golesheed/whisper-v2-CGN-Frisian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use golesheed/whisper-v2-CGN-Frisian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="golesheed/whisper-v2-CGN-Frisian")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("golesheed/whisper-v2-CGN-Frisian") model = AutoModelForSpeechSeq2Seq.from_pretrained("golesheed/whisper-v2-CGN-Frisian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Large V2
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2005
- Wer: 9.6819
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: 3e-05
- train_batch_size: 12
- 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: 20
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.36 | 2.5 | 15 | 0.2378 | 12.5864 |
| 0.0614 | 5.0 | 30 | 0.2005 | 9.6819 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for golesheed/whisper-v2-CGN-Frisian
Base model
openai/whisper-large-v2