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 training_args.bin from codingninja/w2v-pa-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/codingninja/w2v-pa-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://codingninja/w2v-pa-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/codingninja/w2v-pa-v2/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 9829b89ecc461c160c179f57d0015d4153c495f7c62fd82fc6796501dfd16f67
- Size of remote file:
- 5.18 kB
- SHA256:
- 4a3d104dd0de82ba496dacbb03ca16e40097bbba41d4d52ba45ae6003fa227e2
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