Instructions to use Milana/model-classifier-vctk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Milana/model-classifier-vctk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Milana/model-classifier-vctk")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Milana/model-classifier-vctk") model = AutoModelForAudioClassification.from_pretrained("Milana/model-classifier-vctk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Milana/model-classifier-vctk: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/Milana/model-classifier-vctk/resolve/main/README.md
- Command line
-
hf download hf://Milana/model-classifier-vctk/README.md
-
curl -L -o README.md https://huggingface.co/Milana/model-classifier-vctk/resolve/main/README.md
1.14 kB
| license: cc-by-nc-4.0 | |
| base_model: facebook/mms-lid-4017 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: model-classifier-vctk | |
| 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. --> | |
| # model-classifier-vctk | |
| This model is a fine-tuned version of [facebook/mms-lid-4017](https://huggingface.co/facebook/mms-lid-4017) on an unknown dataset. | |
| ## 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: 3e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - training_steps: 1000 | |
| - mixed_precision_training: Native AMP | |
| ### Framework versions | |
| - Transformers 4.41.0 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 | |