Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
English
wav2vec2
phoneme-recognition
Generated from Trainer
Eval Results (legacy)
Instructions to use bookbot/wav2vec2-ljspeech-gruut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bookbot/wav2vec2-ljspeech-gruut with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bookbot/wav2vec2-ljspeech-gruut")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bookbot/wav2vec2-ljspeech-gruut") model = AutoModelForCTC.from_pretrained("bookbot/wav2vec2-ljspeech-gruut", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bookbot/wav2vec2-ljspeech-gruut: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bookbot/wav2vec2-ljspeech-gruut/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 2a7028ec143713a5664d7ab22dea2f103502a2471e4c47020a3a3fd893009909
- Size of remote file:
- 378 MB
- SHA256:
- abd7c24c34c4bacd0317f956bf05a9ada9745784c0c150b056ed2e5076267b0e
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