Instructions to use devkyle/base-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/base-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/base-v5")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/base-v5") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/base-v5") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: openai/whisper-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: whisper-base-v5 | |
| 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-base-v5 | |
| This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: 1.1042 | |
| - eval_wer: 52.5373 | |
| - eval_runtime: 135.9465 | |
| - eval_samples_per_second: 1.471 | |
| - eval_steps_per_second: 0.184 | |
| - epoch: 20.0 | |
| - step: 1000 | |
| ## 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: 0.0001 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 2000 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.46.2 | |
| - Pytorch 2.5.0+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |