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 train_results.json from bookbot/wav2vec2-ljspeech-gruut: direct link, hf CLI and curl.
- Browser
- Download file 197 Bytes
-
https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/train_results.json
- Command line
-
hf download hf://bookbot/wav2vec2-ljspeech-gruut/train_results.json
-
curl -L -o train_results.json https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/resolve/main/train_results.json
197 Bytes
| { | |
| "epoch": 30.0, | |
| "train_loss": 0.15939651108792915, | |
| "train_runtime": 19958.548, | |
| "train_samples": 11135, | |
| "train_samples_per_second": 16.737, | |
| "train_steps_per_second": 0.523 | |
| } |