Instructions to use devkya/large-v2-multiple-bg-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use devkya/large-v2-multiple-bg-v1 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
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Download README.md from devkya/large-v2-multiple-bg-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
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https://huggingface.co/devkya/large-v2-multiple-bg-v1/resolve/main/README.md
- Command line
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hf download hf://devkya/large-v2-multiple-bg-v1/README.md
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curl -L -o README.md https://huggingface.co/devkya/large-v2-multiple-bg-v1/resolve/main/README.md
2.23 kB
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: openai/whisper-large-v2 | |
| datasets: | |
| - audiofolder | |
| model-index: | |
| - name: large-v2-multiple-bg-v1 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/xpertinc/huggingface/runs/v28yz3ha) | |
| # large-v2-multiple-bg-v1 | |
| This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the audiofolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.8192 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-06 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.01 | |
| - training_steps: 5000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:--------:|:----:|:---------------:| | |
| | 1.7975 | 17.2414 | 500 | 1.7851 | | |
| | 1.4044 | 34.4828 | 1000 | 1.3945 | | |
| | 1.0932 | 51.7241 | 1500 | 1.0965 | | |
| | 0.9793 | 68.9655 | 2000 | 0.9987 | | |
| | 0.9105 | 86.2069 | 2500 | 0.9390 | | |
| | 0.8601 | 103.4483 | 3000 | 0.8936 | | |
| | 0.8238 | 120.6897 | 3500 | 0.8600 | | |
| | 0.7935 | 137.9310 | 4000 | 0.8370 | | |
| | 0.7834 | 155.1724 | 4500 | 0.8236 | | |
| | 0.7751 | 172.4138 | 5000 | 0.8192 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.41.0.dev0 | |
| - Pytorch 2.2.2+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 |