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")# 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 training_args.bin from bookbot/wav2vec2-ljspeech-gruut: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/training_args.bin
- Command line
-
hf download hf://bookbot/wav2vec2-ljspeech-gruut/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- de39871d7119b7ec4e5cb01752ec300296dc96598ea3aa340ed233c59acae87b
- Size of remote file:
- 3.44 kB
- SHA256:
- 568fbb8527dc12e68bd967c150cdd930d8004c36be50967c5682ca8c225280d4
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