Instructions to use namkyeong/batch_test_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namkyeong/batch_test_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="namkyeong/batch_test_wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("namkyeong/batch_test_wav2vec2") model = AutoModelForCTC.from_pretrained("namkyeong/batch_test_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from namkyeong/batch_test_wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/namkyeong/batch_test_wav2vec2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://namkyeong/batch_test_wav2vec2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/namkyeong/batch_test_wav2vec2/resolve/main/pytorch_model.bin
1.27 GB
- Xet hash:
- 476eea119a77ac8ad3cb0ab02dbcc257eec42224bc43d3c338379d24448ee6a1
- Size of remote file:
- 1.27 GB
- SHA256:
- 26f04240861dcae0d3ad90dcae59131ff540d18123fe1693b85f86887e4e26f4
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