Instructions to use Semih/wav2vec2_Irish_Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Semih/wav2vec2_Irish_Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Semih/wav2vec2_Irish_Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Semih/wav2vec2_Irish_Large") model = AutoModelForCTC.from_pretrained("Semih/wav2vec2_Irish_Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Semih/wav2vec2_Irish_Large: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Semih/wav2vec2_Irish_Large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Semih/wav2vec2_Irish_Large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Semih/wav2vec2_Irish_Large/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 9d7780f821bc48a524d12daa03d79274ef701166141d060f6f7fe9ac0d1c5f08
- Size of remote file:
- 1.26 GB
- SHA256:
- 58d5619c3be4b96b20ed518b36995d31dabcc64c723cc133740bd0054d386f33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.