Instructions to use neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10") model = AutoModelForSpeechSeq2Seq.from_pretrained("neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10/resolve/main/training_args.bin
- Command line
-
hf download hf://neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/neoALI/VC-MJY_Woman_40s-0_preprocessed_random_per_sample-10/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 56a7cafeed2bc830cab9c594e70a3cdee599c8d6191b25ecf1d243622f2bb688
- Size of remote file:
- 5.5 kB
- SHA256:
- e5d369ee6d467497a376289ebe63aa334a5bedfe7120f1b1f7c473dc226386eb
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