Instructions to use SinghManish/audio-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinghManish/audio-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SinghManish/audio-classification-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("SinghManish/audio-classification-model") model = AutoModel.from_pretrained("SinghManish/audio-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SinghManish/audio-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 997 Bytes
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https://huggingface.co/SinghManish/audio-classification-model/resolve/main/README.md
- Command line
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hf download hf://SinghManish/audio-classification-model/README.md
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curl -L -o README.md https://huggingface.co/SinghManish/audio-classification-model/resolve/main/README.md
997 Bytes
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: audio-classification-model
results: []
audio-classification-model
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.31.0.dev0
- TensorFlow 2.12.0
- Datasets 2.13.0
- Tokenizers 0.13.3