Instructions to use devkyle/Akan-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-3") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: whisper-tiny-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-tiny-akan | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1096 | |
| - Wer: 45.1603 | |
| ## 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: 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.4793 | 10.0 | 250 | 0.7459 | 53.4739 | | |
| | 0.0732 | 20.0 | 500 | 0.9086 | 49.4656 | | |
| | 0.0309 | 30.0 | 750 | 1.0036 | 47.3278 | | |
| | 0.0132 | 40.0 | 1000 | 1.0760 | 46.8230 | | |
| | 0.005 | 50.0 | 1250 | 1.0944 | 45.3088 | | |
| | 0.002 | 60.0 | 1500 | 1.0899 | 44.5368 | | |
| | 0.0006 | 70.0 | 1750 | 1.1071 | 45.0416 | | |
| | 0.0005 | 80.0 | 2000 | 1.1096 | 45.1603 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |