Instructions to use JRHuy/whisper-small-vivos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JRHuy/whisper-small-vivos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JRHuy/whisper-small-vivos")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("JRHuy/whisper-small-vivos") model = AutoModelForSpeechSeq2Seq.from_pretrained("JRHuy/whisper-small-vivos") - Notebooks
- Google Colab
- Kaggle
whisper-small-vivos
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2580
- Wer: 17.6379
- Cer: 7.0276
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.1276 | 1.37 | 1000 | 0.2137 | 20.6164 | 8.6876 |
| 0.0696 | 2.74 | 2000 | 0.2173 | 19.0883 | 7.5097 |
| 0.0215 | 4.12 | 3000 | 0.2420 | 17.9876 | 7.0794 |
| 0.0199 | 5.49 | 4000 | 0.2580 | 17.6379 | 7.0276 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3
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