Instructions to use devkyle/Akan-tiny-2000ms-1.5k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/Akan-tiny-2000ms-1.5k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/Akan-tiny-2000ms-1.5k")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/Akan-tiny-2000ms-1.5k") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/Akan-tiny-2000ms-1.5k", 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: 0.2466 | |
| - Wer: 18.4363 | |
| ## 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 | | |
| |:-------------:|:-------:|:----:|:---------------:|:-------:| | |
| | 0.8631 | 2.9070 | 250 | 0.8110 | 57.7923 | | |
| | 0.4257 | 5.8140 | 500 | 0.7295 | 50.5545 | | |
| | 0.1859 | 8.7209 | 750 | 0.7882 | 50.0381 | | |
| | 0.0717 | 11.6279 | 1000 | 0.8608 | 49.2847 | | |
| | 0.0758 | 14.5349 | 1250 | 0.2162 | 17.7826 | | |
| | 0.0242 | 17.4419 | 1500 | 0.2390 | 19.1234 | | |
| | 0.0105 | 20.3488 | 1750 | 0.2467 | 19.7519 | | |
| | 0.0054 | 23.2558 | 2000 | 0.2466 | 18.4363 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |