Instructions to use jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download transcriptions_cv7_test.json from jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658: direct link, hf CLI and curl.
- Browser
- Download file 15 MB
-
https://huggingface.co/jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658/resolve/main/transcriptions_cv7_test.json
- Command line
-
hf download hf://jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658/transcriptions_cv7_test.json
-
curl -L -o transcriptions_cv7_test.json https://huggingface.co/jonatasgrosman/exp_w2v2t_zh-cn_unispeech-ml_s658/resolve/main/transcriptions_cv7_test.json
15 MB
- Xet hash:
- a4e3c6872cef6c591b28113eed1a1b41694d485b4f8a71de5283e5b1b59dbb2d
- Size of remote file:
- 15 MB
- SHA256:
- c4102bd6fd277baee065041c353a7cc4bedc176fb422ceb741e77337dd3dad1d
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