Instructions to use rav2040/wav2vec2-base-timit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rav2040/wav2vec2-base-timit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rav2040/wav2vec2-base-timit")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rav2040/wav2vec2-base-timit") model = AutoModelForCTC.from_pretrained("rav2040/wav2vec2-base-timit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rav2040/wav2vec2-base-timit: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/rav2040/wav2vec2-base-timit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rav2040/wav2vec2-base-timit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rav2040/wav2vec2-base-timit/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 18efbc21b5d1205ffc8c0ea54b777526db9fe8fed370c7f3602c7bf06a2494e5
- Size of remote file:
- 378 MB
- SHA256:
- 325a0b45ef8acc7d5e84bfd678dbc1b33ffb78b049671a81508efc61a6dc3154
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