Instructions to use harshit345/wav2vec2-large-lv60-timit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshit345/wav2vec2-large-lv60-timit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="harshit345/wav2vec2-large-lv60-timit")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("harshit345/wav2vec2-large-lv60-timit") model = AutoModelForCTC.from_pretrained("harshit345/wav2vec2-large-lv60-timit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harshit345/wav2vec2-large-lv60-timit: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harshit345/wav2vec2-large-lv60-timit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 22d4099d1ada99c75d65a06001591219b672062bc14e925d19915a2cb0d1e532
- Size of remote file:
- 1.26 GB
- SHA256:
- 24d33462a32e457df0bc7dbafddeec392f21c0c15eb61181f7fb38c7360ea1dc
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