Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Dutch
whisper
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("golesheed/whisper-non-native-children-8-dutch")
model = AutoModelForSpeechSeq2Seq.from_pretrained("golesheed/whisper-non-native-children-8-dutch", device_map="auto")Quick Links
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: 0.4064
- Wer: 11.6270
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: 16
- 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 |
|---|---|---|---|---|
| 0.6676 | 0.71 | 30 | 0.4010 | 19.5433 |
| 0.2705 | 1.43 | 60 | 0.3582 | 13.6441 |
| 0.1694 | 2.14 | 90 | 0.3648 | 11.8934 |
| 0.0793 | 2.86 | 120 | 0.3757 | 13.0542 |
| 0.0416 | 3.57 | 150 | 0.3965 | 13.3587 |
| 0.0245 | 4.29 | 180 | 0.3938 | 11.9125 |
| 0.012 | 5.0 | 210 | 0.4064 | 11.6270 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
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Model tree for golesheed/whisper-non-native-children-8-dutch
Base model
openai/whisper-large-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="golesheed/whisper-non-native-children-8-dutch")