Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab") model = AutoModelForCTC.from_pretrained("jcrkn/wav2vec2-large-xls-r-300m-bretonwelsh-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d1d3eb2c3c272f01bd31e4611058883cb9fe19c977a895aa1e400ac2dbc527d
- Size of remote file:
- 4.09 kB
- SHA256:
- 62c15fa6db025f0437ffa59e6a0be9fe21fdccca7c3c2d0138ef2c180b61b746
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.