Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Russian
wav2vec2
audio
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
robust-speech-event
speech
Eval Results (legacy)
Instructions to use RASMUS/wav2vec2-xlsr-1b-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RASMUS/wav2vec2-xlsr-1b-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RASMUS/wav2vec2-xlsr-1b-ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("RASMUS/wav2vec2-xlsr-1b-ru") model = AutoModelForCTC.from_pretrained("RASMUS/wav2vec2-xlsr-1b-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RASMUS/wav2vec2-xlsr-1b-ru: direct link, hf CLI and curl.
- Browser
- Download file 3.85 GB
-
https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RASMUS/wav2vec2-xlsr-1b-ru/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru/resolve/main/pytorch_model.bin
3.85 GB
- Xet hash:
- 67fa736f643ccbbe2a5d30df77cc73c8a24475767c821668998cf1be22647f9f
- Size of remote file:
- 3.85 GB
- SHA256:
- 9255ff51980293f4ae2201f74e81c1d4e047daff420771f1d787c1aedd3c800d
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