Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use jucielflexge/teste_finetunning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jucielflexge/teste_finetunning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jucielflexge/teste_finetunning")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jucielflexge/teste_finetunning") model = AutoModelForCTC.from_pretrained("jucielflexge/teste_finetunning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +7 -7
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README.md
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metrics:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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## Model description
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### Training results
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### Framework versions
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metrics:
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type: wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0001
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- Wer: 0.0
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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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| 0.0354 | 1000.0 | 1000 | 0.0001 | 0.0 |
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| 0.0375 | 2000.0 | 2000 | 0.0001 | 0.0 |
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### Framework versions
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model.safetensors
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