Instructions to use jonatasgrosman/exp_w2v2t_ru_wavlm_s363 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_ru_wavlm_s363 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_ru_wavlm_s363")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_ru_wavlm_s363") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_ru_wavlm_s363", device_map="auto") - Notebooks
- Google Colab
- Kaggle
exp_w2v2t_ru_wavlm_s363
Fine-tuned microsoft/wavlm-large for speech recognition using the train split of Common Voice 7.0 (ru). When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
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