Instructions to use jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449 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_unispeech_s449")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download transcriptions_cv7_test.json from jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449: direct link, hf CLI and curl.
- Browser
- Download file 15.4 MB
-
https://huggingface.co/jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449/resolve/main/transcriptions_cv7_test.json
- Command line
-
hf download hf://jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449/transcriptions_cv7_test.json
-
curl -L -o transcriptions_cv7_test.json https://huggingface.co/jonatasgrosman/exp_w2v2t_sv-se_unispeech_s449/resolve/main/transcriptions_cv7_test.json
15.4 MB
- Xet hash:
- 755b737aee605d69cc5eb89ac59ee67e5f869fcbbdd5bd246a12092de922e871
- Size of remote file:
- 15.4 MB
- SHA256:
- 64e56dbd34a9e72e4582adc6495016ee6c17418e45d4808aecd479330f3a11e3
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