Instructions to use ramimmo/wav2vec2phoneme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ramimmo/wav2vec2phoneme with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ramimmo/wav2vec2phoneme")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("ramimmo/wav2vec2phoneme") model = AutoModelForCTC.from_pretrained("ramimmo/wav2vec2phoneme", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ramimmo/wav2vec2phoneme: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/ramimmo/wav2vec2phoneme/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ramimmo/wav2vec2phoneme/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ramimmo/wav2vec2phoneme/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- a06c78607533bd4397f59808e77d6ac252f4ffbdd4dc73d6c9ff1399e9814391
- Size of remote file:
- 378 MB
- SHA256:
- f835997b0d6b0b2795389a55c6f81135e43e2e7f4ab02819f3e11527c4cd5ba9
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