Instructions to use devkyle/Akan-tiny-2000ms-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-tiny-2000ms-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-tiny-2000ms-1k")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-tiny-2000ms-1k") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-tiny-2000ms-1k", 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: 0.2315 | |
| - Wer: 13.2075 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.7229 | 5.0 | 250 | 0.8173 | 56.7933 | | |
| | 0.2324 | 10.0 | 500 | 0.8472 | 50.4318 | | |
| | 0.0673 | 15.0 | 750 | 0.9745 | 49.5250 | | |
| | 0.0297 | 20.0 | 1000 | 1.0408 | 49.4675 | | |
| | 0.0245 | 25.0 | 1250 | 0.2170 | 13.1495 | | |
| | 0.0088 | 30.0 | 1500 | 0.2295 | 13.7591 | | |
| | 0.0025 | 35.0 | 1750 | 0.2324 | 13.9478 | | |
| | 0.0019 | 40.0 | 2000 | 0.2315 | 13.2075 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 3.0.0 | |
| - Tokenizers 0.19.1 | |