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
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: audio-classification-model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # audio-classification-model | |
| This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/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 | |