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
metadata
license: cc-by-nc-4.0
base_model: facebook/mms-lid-4017
tags:
- generated_from_trainer
model-index:
- name: model-classifier-vctk
results: []
model-classifier-vctk
This model is a fine-tuned version of 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