Instructions to use birgermoell/wav2vec2-speechdat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use birgermoell/wav2vec2-speechdat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="birgermoell/wav2vec2-speechdat")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("birgermoell/wav2vec2-speechdat") model = AutoModelForCTC.from_pretrained("birgermoell/wav2vec2-speechdat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1293f7f0efc6b547ce435951bceb168ad0c4755c93877ad623d56eb2b6b5300f
- Size of remote file:
- 1.26 GB
- SHA256:
- d2c84bf70d7ef1d4b65d6f33f076080e84fb984dba2ba48c2b992ffa5453a312
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