Instructions to use Akaash1/wav2vec-khmer-english-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akaash1/wav2vec-khmer-english-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akaash1/wav2vec-khmer-english-all")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akaash1/wav2vec-khmer-english-all") model = AutoModelForCTC.from_pretrained("Akaash1/wav2vec-khmer-english-all") - Notebooks
- Google Colab
- Kaggle
training
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on all of the podcast dataset (20hours) dataset. It achieves the following results on the evaluation set:
- Wer: 0.5692
- Downloads last month
- 3
Model tree for Akaash1/wav2vec-khmer-english-all
Base model
facebook/wav2vec2-xls-r-300m