Instructions to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new") model = AutoModelForAudioXVector.from_pretrained("kming/wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-new") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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datasets:
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- edinburghcstr/ami
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model-index:
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- name: wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-
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This model is a fine-tuned version of [anton-l/wav2vec2-base-superb-sv](https://huggingface.co/anton-l/wav2vec2-base-superb-sv) on the ami dataset.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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datasets:
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- edinburghcstr/ami
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model-index:
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- name: wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized
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results: []
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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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should probably proofread and complete it, then remove this comment. -->
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# wav2vec2-base-superb-sv-finetuned-ami-ten-percent-train-normalized
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This model is a fine-tuned version of [anton-l/wav2vec2-base-superb-sv](https://huggingface.co/anton-l/wav2vec2-base-superb-sv) on the ami dataset.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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