Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme") model = AutoModelForCTC.from_pretrained("Roxysun/cs2fi-wav2vec2-large-xls-r-300m-cs-colab-phoneme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3cd89d23b371ff2617b62a2dac3af490224dd09b2378c4463fd499de55a59ac
- Size of remote file:
- 1.26 GB
- SHA256:
- bac2d795ea85062b26f4a0c9d397b5f43ffc85ac222b2fff2d91ce31d81af759
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.