Instructions to use Umong/w2v-bert-2.0-bd-regional-dialects with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Umong/w2v-bert-2.0-bd-regional-dialects with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Umong/w2v-bert-2.0-bd-regional-dialects")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Umong/w2v-bert-2.0-bd-regional-dialects") model = AutoModelForCTC.from_pretrained("Umong/w2v-bert-2.0-bd-regional-dialects", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Umong/w2v-bert-2.0-bd-regional-dialects: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Umong/w2v-bert-2.0-bd-regional-dialects/resolve/main/training_args.bin
- Command line
-
hf download hf://Umong/w2v-bert-2.0-bd-regional-dialects/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/Umong/w2v-bert-2.0-bd-regional-dialects/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 7a2d313db10adb30ef24d6ae234d29f85bca5706bd88badfc6f90fa518191e8d
- Size of remote file:
- 4.92 kB
- SHA256:
- f1f2880bfa676703d4f2930e5ed7303a49f9e264aee0b247df1c9e555ea0ac95
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