Instructions to use daveni/whisper-tiny-commonvoice_v11-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daveni/whisper-tiny-commonvoice_v11-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="daveni/whisper-tiny-commonvoice_v11-de")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("daveni/whisper-tiny-commonvoice_v11-de") model = AutoModelForSpeechSeq2Seq.from_pretrained("daveni/whisper-tiny-commonvoice_v11-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from daveni/whisper-tiny-commonvoice_v11-de: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/daveni/whisper-tiny-commonvoice_v11-de/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://daveni/whisper-tiny-commonvoice_v11-de/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/daveni/whisper-tiny-commonvoice_v11-de/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- e7320e88f99cc080800196cd6bfd8e4b2ed0048afd4c301568ff01d0f514c688
- Size of remote file:
- 151 MB
- SHA256:
- 02d96a54a1ce03e43dda2c2f79c6431dfe44a416cbefac323b2543bf0f139487
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.