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-77400/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-77400/optimizer.pt
- Command line
-
hf download hf://codingninja/w2v-pa-v2/checkpoint-77400/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/codingninja/w2v-pa-v2/resolve/main/checkpoint-77400/optimizer.pt
4.91 GB
- Xet hash:
- b0f414482a47648ee38b601081dc46f6bde9e862888a1f21a4355f0c0cec963e
- Size of remote file:
- 4.91 GB
- SHA256:
- 151074e2c8338d3a2c61fe590b56a9bc96902e13c26c8ac019328218a7387f2e
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