Instructions to use jonatasgrosman/exp_w2v2t_sv-se_wavlm_s132 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_sv-se_wavlm_s132 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_sv-se_wavlm_s132")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_wavlm_s132") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_wavlm_s132", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- sv-SE
datasets:
- mozilla-foundation/common_voice_7_0
exp_w2v2t_sv-se_wavlm_s132
Fine-tuned microsoft/wavlm-large for speech recognition using the train split of Common Voice 7.0 (sv-SE). When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.