Instructions to use jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s201 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s201 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s201")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s201") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_ja_r-wav2vec2_s201", device_map="auto") - Notebooks
- Google Colab
- Kaggle
exp_w2v2t_ja_r-wav2vec2_s201
Fine-tuned facebook/wav2vec2-large-robust for speech recognition using the train split of Common Voice 7.0 (ja). 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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