Instructions to use MBMMurad/wav2vec2_spec_aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBMMurad/wav2vec2_spec_aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MBMMurad/wav2vec2_spec_aug")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("MBMMurad/wav2vec2_spec_aug") model = AutoModelForCTC.from_pretrained("MBMMurad/wav2vec2_spec_aug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MBMMurad/wav2vec2_spec_aug: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/MBMMurad/wav2vec2_spec_aug/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MBMMurad/wav2vec2_spec_aug/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MBMMurad/wav2vec2_spec_aug/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- fbd6be7e19d7ff5ae974b918ce27182056b640fbc5db18187b5beeee92cfdefe
- Size of remote file:
- 1.26 GB
- SHA256:
- 412c1f11f07230af145bcd5cd17376cab82e12c19b46923fae7a76798f6f15a2
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