Instructions to use Sjdan/finetuning12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/finetuning12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/finetuning12")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/finetuning12") model = AutoModelForCTC.from_pretrained("Sjdan/finetuning12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: finetuning12
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results: []
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
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It achieves the following results on the evaluation set:
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- eval_runtime: 17.8202
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- eval_samples_per_second: 182.602
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- eval_steps_per_second: 22.839
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- epoch: 1.84
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- step: 3000
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 800
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- num_epochs:
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### Framework versions
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license: apache-2.0
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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: finetuning12
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results: []
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: nan
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- Wer: 1.0
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 800
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 0.0 | 0.31 | 500 | nan | 1.0 |
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| 0.0 | 0.61 | 1000 | nan | 1.0 |
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| 0.0 | 0.92 | 1500 | nan | 1.0 |
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| 0.0 | 1.23 | 2000 | nan | 1.0 |
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| 0.0 | 1.54 | 2500 | nan | 1.0 |
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| 0.0 | 1.84 | 3000 | nan | 1.0 |
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### Framework versions
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