Automatic Speech Recognition
Transformers
PyTorch
Chinese
wav2vec2
audio
speech
xlsr-fine-tuning-week
Instructions to use gymeee/demo_code_switching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gymeee/demo_code_switching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gymeee/demo_code_switching")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gymeee/demo_code_switching") model = AutoModelForCTC.from_pretrained("gymeee/demo_code_switching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gymeee/demo_code_switching: direct link, hf CLI and curl.
- Browser
- Download file 1.43 GB
-
https://huggingface.co/gymeee/demo_code_switching/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://gymeee/demo_code_switching@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gymeee/demo_code_switching/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.43 GB
- Xet hash:
- ec4c63d8559358fbe9cd5d2a50b6db0df6cda1c00dcdf130d373bc111490d3c4
- Size of remote file:
- 1.43 GB
- SHA256:
- 57a19e7d2431310e0b982e296f5d2eb412a7ae68e7b73ca70f87bca4fb60900a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.