Instructions to use SaiprasadP/wav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaiprasadP/wav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="SaiprasadP/wav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("SaiprasadP/wav2vec") model = AutoModelForAudioClassification.from_pretrained("SaiprasadP/wav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SaiprasadP/wav2vec: direct link, hf CLI and curl.
- Browser
- Download file 999 Bytes
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https://huggingface.co/SaiprasadP/wav2vec/resolve/main/README.md
- Command line
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hf download hf://SaiprasadP/wav2vec/README.md
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curl -L -o README.md https://huggingface.co/SaiprasadP/wav2vec/resolve/main/README.md
999 Bytes
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| base_model: facebook/wav2vec2-base-960h | |
| model-index: | |
| - name: wav2vec | |
| 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. --> | |
| # wav2vec | |
| 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 4.42.0.dev0 | |
| - TensorFlow 2.15.0 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |