Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
wav2vec2
audio
en-atc
Generated from Trainer
Eval Results (legacy)
Instructions to use Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim") model = AutoModelForCTC.from_pretrained("Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 148a43162e5c5a6420b9bee01a6fdbd92abff3ecbb1b6943fe695fb5ec9e6c07
- Size of remote file:
- 3.77 kB
- SHA256:
- 99f032c9f343eb35642bde14fec67ed92f7444d52f91c2f298631159f1ec75a5
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