Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2-bert
Generated from Trainer
Instructions to use AHedya/w2v-bert-fine-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AHedya/w2v-bert-fine-tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AHedya/w2v-bert-fine-tuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AHedya/w2v-bert-fine-tuning") model = AutoModelForCTC.from_pretrained("AHedya/w2v-bert-fine-tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AHedya/w2v-bert-fine-tuning: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/AHedya/w2v-bert-fine-tuning/resolve/main/training_args.bin
- Command line
-
hf download hf://AHedya/w2v-bert-fine-tuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AHedya/w2v-bert-fine-tuning/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- e73e26910990b248ecb44bda83c55486dbc6cac503151c0f76e11510d2485460
- Size of remote file:
- 4.98 kB
- SHA256:
- c4276b0abcfe6ee971bc3eb44c2d92e84169df713611510a5a92847d94b187d3
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