Automatic Speech Recognition
Transformers
PyTorch
Lithuanian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use Tomas1234/common_voice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tomas1234/common_voice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tomas1234/common_voice")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Tomas1234/common_voice") model = AutoModelForSpeechSeq2Seq.from_pretrained("Tomas1234/common_voice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tomas1234/common_voice: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/Tomas1234/common_voice/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tomas1234/common_voice/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tomas1234/common_voice/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 4114d1ad7474d082aeffe13f4431cc5b31517dbe58242ab513d0c9617dd19bb1
- Size of remote file:
- 967 MB
- SHA256:
- 6693e8689ad1f4a734085974731804ea06b4e8013eadcc89d4f065b1e8a0d7ca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.