Instructions to use hts98/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hts98/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/model")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hts98/model") model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: model | |
| 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. --> | |
| # model | |
| This model is a fine-tuned version of [hts98/whisper-medium-1113](https://huggingface.co/hts98/whisper-medium-1113) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3198 | |
| - Wer: 100.0 | |
| ## 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: 1e-05 | |
| - train_batch_size: 12 | |
| - eval_batch_size: 12 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 30 | |
| - training_steps: 200 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-----:| | |
| | 0.1889 | 1.43 | 30 | 0.2595 | 100.0 | | |
| | 0.067 | 2.86 | 60 | 0.2960 | 100.0 | | |
| | 0.0319 | 4.29 | 90 | 0.3027 | 100.0 | | |
| | 0.0171 | 5.71 | 120 | 0.3166 | 100.0 | | |
| | 0.0067 | 7.14 | 150 | 0.3214 | 100.0 | | |
| | 0.0028 | 8.57 | 180 | 0.3198 | 100.0 | | |
| ### Framework versions | |
| - Transformers 4.29.0.dev0 | |
| - Pytorch 2.0.0+cu117 | |
| - Datasets 2.7.0 | |
| - Tokenizers 0.13.3 | |