Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Japanese
wav2vec2
common-voice
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use vumichien/wav2vec2-xls-r-1b-japanese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vumichien/wav2vec2-xls-r-1b-japanese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vumichien/wav2vec2-xls-r-1b-japanese")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("vumichien/wav2vec2-xls-r-1b-japanese") model = AutoModelForCTC.from_pretrained("vumichien/wav2vec2-xls-r-1b-japanese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 100.0, | |
| "eval_cer": 0.09914388076368318, | |
| "eval_loss": 0.21041299402713776, | |
| "eval_runtime": 208.9129, | |
| "eval_samples": 3676, | |
| "eval_samples_per_second": 17.596, | |
| "eval_steps_per_second": 2.202, | |
| "eval_wer": 0.19410805775500697 | |
| } |