Instructions to use codingninja/w2v-pa-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codingninja/w2v-pa-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="codingninja/w2v-pa-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("codingninja/w2v-pa-v2") model = AutoModelForCTC.from_pretrained("codingninja/w2v-pa-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-90000/optimizer.pt from codingninja/w2v-pa-v2: direct link, hf CLI and curl.
- Browser
- Download file 4.91 GB
-
https://huggingface.co/codingninja/w2v-pa-v2/resolve/main/checkpoint-90000/optimizer.pt
- Command line
-
hf download hf://codingninja/w2v-pa-v2/checkpoint-90000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/codingninja/w2v-pa-v2/resolve/main/checkpoint-90000/optimizer.pt
4.91 GB
- Xet hash:
- 0cb0888c9356d3068e4ade264c210f69b658029474337f3732546819763197b5
- Size of remote file:
- 4.91 GB
- SHA256:
- 4bda6f39e6df94d0cdddc2187b9d66da3669fcb0888c1bee64c1558ff752314d
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