Instructions to use Bajiyo/whisper-medium-studio-records_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bajiyo/whisper-medium-studio-records_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Bajiyo/whisper-medium-studio-records_test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Bajiyo/whisper-medium-studio-records_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("Bajiyo/whisper-medium-studio-records_test") - Notebooks
- Google Colab
- Kaggle
whisper-medium-studio-records_test
This model is a fine-tuned version of openai/whisper-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0333
- Wer: 15.6507
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: 1000
- training_steps: 6000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0568 | 0.4110 | 1000 | 0.0894 | 43.3939 |
| 0.0362 | 0.8220 | 2000 | 0.0589 | 29.9079 |
| 0.0149 | 1.2330 | 3000 | 0.0463 | 22.6922 |
| 0.0117 | 1.6441 | 4000 | 0.0375 | 19.2088 |
| 0.0039 | 2.0551 | 5000 | 0.0355 | 16.1483 |
| 0.0032 | 2.4661 | 6000 | 0.0333 | 15.6507 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Bajiyo/whisper-medium-studio-records_test
Base model
openai/whisper-medium