Instructions to use devkyle/Akan-3-3000ms with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-3-3000ms with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-3-3000ms")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-3-3000ms") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-3-3000ms", 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: 1.0747 | |
| - Wer: 43.6101 | |
| ## 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: 3000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.4857 | 10.0 | 250 | 0.7120 | 57.1555 | | |
| | 0.0806 | 20.0 | 500 | 0.8478 | 49.9411 | | |
| | 0.0347 | 30.0 | 750 | 0.9223 | 48.1743 | | |
| | 0.0168 | 40.0 | 1000 | 1.0079 | 55.1826 | | |
| | 0.0085 | 50.0 | 1250 | 1.0402 | 47.3498 | | |
| | 0.0051 | 60.0 | 1500 | 1.0890 | 46.7314 | | |
| | 0.0029 | 70.0 | 1750 | 1.0639 | 44.9352 | | |
| | 0.002 | 80.0 | 2000 | 1.0707 | 44.6702 | | |
| | 0.0005 | 90.0 | 2250 | 1.0705 | 43.7574 | | |
| | 0.0005 | 100.0 | 2500 | 1.0721 | 44.4052 | | |
| | 0.0002 | 110.0 | 2750 | 1.0730 | 43.3451 | | |
| | 0.0003 | 120.0 | 3000 | 1.0747 | 43.6101 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |