Instructions to use TopSlayer/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TopSlayer/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TopSlayer/model")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("TopSlayer/model") model = AutoModelForCTC.from_pretrained("TopSlayer/model") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: model
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results: []
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# model
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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: 0.0314
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- Wer: 0.2727
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 5.802 | 5.6338 | 400 | 3.6938 | 1.0 |
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| 2.5473 | 11.2676 | 800 | 1.1631 | 1.0 |
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| 1.4959 | 16.9014 | 1200 | 0.3692 | 1.0182 |
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| 0.8394 | 22.5352 | 1600 | 0.0807 | 0.3636 |
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| 0.4855 | 28.1690 | 2000 | 0.0314 | 0.2727 |
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### Framework versions
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base_model: facebook/wav2vec2-xls-r-300m
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tags:
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- generated_from_trainer
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model-index:
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- name: model
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results: []
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# model
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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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### Training results
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### Framework versions
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