Instructions to use Sjdan/mst_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/mst_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/mst_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/mst_1") model = AutoModelForCTC.from_pretrained("Sjdan/mst_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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# mst_1
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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.
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## Model description
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- num_epochs: 7
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.17.0
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# mst_1
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.0038
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- Wer: 0.9915
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## Model description
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- num_epochs: 7
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 11.0362 | 1.15 | 500 | 2.7219 | 1.0 |
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| 1.9536 | 2.29 | 1000 | 0.5060 | 2.0274 |
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| 0.2347 | 3.44 | 1500 | 0.0268 | 1.0462 |
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| 0.0633 | 4.59 | 2000 | 0.0078 | 1.0 |
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| 0.0359 | 5.73 | 2500 | 0.0047 | 0.9949 |
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| 0.014 | 6.88 | 3000 | 0.0038 | 0.9915 |
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### Framework versions
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- Transformers 4.17.0
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