Instructions to use devkyle/base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/base-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/base-v1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/base-v1") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/base-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: openai/whisper-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: whisper-base-akan | |
| 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-akan | |
| 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: | |
| - Loss: 1.1058 | |
| - Wer: 41.1088 | |
| ## 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: 32 | |
| - 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: 2000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 1.8723 | 10.0 | 250 | 1.0427 | 68.4371 | | |
| | 0.3084 | 20.0 | 500 | 0.7759 | 44.8196 | | |
| | 0.0708 | 30.0 | 750 | 0.9140 | 42.7532 | | |
| | 0.027 | 40.0 | 1000 | 1.0043 | 42.5058 | | |
| | 0.0109 | 50.0 | 1250 | 1.0740 | 42.1711 | | |
| | 0.0046 | 60.0 | 1500 | 1.0846 | 40.9488 | | |
| | 0.0032 | 70.0 | 1750 | 1.1017 | 41.3708 | | |
| | 0.0022 | 80.0 | 2000 | 1.1058 | 41.1088 | | |
| ### Framework versions | |
| - Transformers 4.45.1 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.0 | |