Automatic Speech Recognition
Transformers
PyTorch
Slovenian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1") model = AutoModelForCTC.from_pretrained("DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 100.0, | |
| "eval_loss": 0.2756262719631195, | |
| "eval_runtime": 36.5594, | |
| "eval_samples": 1193, | |
| "eval_samples_per_second": 32.632, | |
| "eval_steps_per_second": 1.039, | |
| "eval_wer": 0.22793148880105402, | |
| "train_loss": 1.2146642443028892, | |
| "train_runtime": 11060.029, | |
| "train_samples": 2606, | |
| "train_samples_per_second": 23.562, | |
| "train_steps_per_second": 0.741 | |
| } |