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 training_args.bin from ramimmo/wav2vec2phoneme: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/ramimmo/wav2vec2phoneme/resolve/main/training_args.bin
- Command line
-
hf download hf://ramimmo/wav2vec2phoneme/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/ramimmo/wav2vec2phoneme/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- 104bf4e978afc88f3945a62790e2e2e9ab4c960999027c01559446ce8be01e4e
- Size of remote file:
- 2.99 kB
- SHA256:
- 8d4b494aa7b9ab694dd172fab8a436198e8993d2b38d965102fe8ff81fc5c476
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