Instructions to use scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/resolve/main/training_args.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 6e6b77bed456d8f4f7e6e4dda84088261dea6d50d902c7bd79aaa1765289d0e1
- Size of remote file:
- 3.06 kB
- SHA256:
- 05f97a626a23a764f0c88dcad7b7d8bc65c7a93c09a4f8d73c05ca89e23bb234
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