Instructions to use Devanshj7/whisper-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Devanshj7/whisper-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Devanshj7/whisper-dev")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Devanshj7/whisper-dev") model = AutoModelForSpeechSeq2Seq.from_pretrained("Devanshj7/whisper-dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: whisper-dev | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # whisper-dev | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9716 | |
| - Wer: 68.6047 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - training_steps: 1000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.039 | 4.0 | 200 | 0.7402 | 70.9302 | | |
| | 0.002 | 8.0 | 400 | 0.8662 | 62.7907 | | |
| | 0.0003 | 12.0 | 600 | 0.9329 | 66.2791 | | |
| | 0.0002 | 16.0 | 800 | 0.9613 | 68.6047 | | |
| | 0.0002 | 20.0 | 1000 | 0.9716 | 68.6047 | | |
| ### Framework versions | |
| - Transformers 4.39.2 | |
| - Pytorch 2.2.0+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |