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 pytorch_model.bin from scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augment_0.1.csv/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 495f7552b2f6075186d3081ef6565b14b03c6d8d12b8747e74e38a7a187111ec
- Size of remote file:
- 378 MB
- SHA256:
- a39c98cc08f9cd832cd8a2cd47416cab074de9833ce9b87a7f24709ad4debc05
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