Instructions to use Sjdan/m14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/m14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/m14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/m14") model = AutoModelForCTC.from_pretrained("Sjdan/m14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sjdan/m14: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/Sjdan/m14/resolve/main/README.md
- Command line
-
hf download hf://Sjdan/m14/README.md
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curl -L -o README.md https://huggingface.co/Sjdan/m14/resolve/main/README.md
1.6 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: m14 | |
| 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. --> | |
| # m14 | |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2985 | |
| - Wer: 1.3916 | |
| ## 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: 8 | |
| - 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: 1000 | |
| - num_epochs: 7 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| | |
| | 9.7026 | 1.34 | 500 | 2.9975 | 1.0 | | |
| | 2.7765 | 2.67 | 1000 | 2.1014 | 2.2574 | | |
| | 1.1625 | 4.01 | 1500 | 0.6151 | 2.0191 | | |
| | 0.2872 | 5.35 | 2000 | 0.3668 | 1.4766 | | |
| | 0.1172 | 6.68 | 2500 | 0.2985 | 1.3916 | | |
| ### Framework versions | |
| - Transformers 4.17.0 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.13.2 | |