Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use mouseyy/result_data_2-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mouseyy/result_data_2-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mouseyy/result_data_2-5")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mouseyy/result_data_2-5") model = AutoModelForCTC.from_pretrained("mouseyy/result_data_2-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d8df030272193c3577862a358fbe8a4cc96b5e41b8f907a5af04a16b1d343bd
- Size of remote file:
- 5.43 kB
- SHA256:
- f0b48207be10d74b2e510c0112f177967cdf21e68e4415db4562092a291b75cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.