Instructions to use explorer7/wav2vec2-ks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use explorer7/wav2vec2-ks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="explorer7/wav2vec2-ks")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("explorer7/wav2vec2-ks") model = AutoModelForAudioClassification.from_pretrained("explorer7/wav2vec2-ks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from explorer7/wav2vec2-ks: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/explorer7/wav2vec2-ks/resolve/main/README.md
- Command line
-
hf download hf://explorer7/wav2vec2-ks/README.md
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curl -L -o README.md https://huggingface.co/explorer7/wav2vec2-ks/resolve/main/README.md
1.27 kB
metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- superb/ks
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
wav2vec2-base-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base on the SUPERB Keyword Spotting 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 3000
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cpu
- Datasets 4.5.0
- Tokenizers 0.22.2