Instructions to use GroupSix/whisper-small-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroupSix/whisper-small-sv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GroupSix/whisper-small-sv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("GroupSix/whisper-small-sv") model = AutoModelForSpeechSeq2Seq.from_pretrained("GroupSix/whisper-small-sv") - Notebooks
- Google Colab
- Kaggle
whisper-small-sv
This model is a fine-tuned version of openai/whisper-small.en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6010
- Wer: 49.3074
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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5121 | 1.27 | 1000 | 0.6973 | 58.2803 |
| 0.2409 | 2.54 | 2000 | 0.5989 | 49.5818 |
| 0.1565 | 3.81 | 3000 | 0.5806 | 48.0991 |
| 0.0729 | 5.08 | 4000 | 0.6010 | 49.3074 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for GroupSix/whisper-small-sv
Base model
openai/whisper-small.en