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
metadata
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: facebook/wav2vec2-base-960h
model-index:
- name: wav2vec
results: []
wav2vec
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 4.42.0.dev0
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1