Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Urdu
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
sv
robust-speech-event
model_for_talk
hf-asr-leaderboard
Instructions to use Maniac/wav2vec2-xls-r-urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maniac/wav2vec2-xls-r-urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Maniac/wav2vec2-xls-r-urdu")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Maniac/wav2vec2-xls-r-urdu") model = AutoModelForCTC.from_pretrained("Maniac/wav2vec2-xls-r-urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 41.67, | |
| "eval_loss": 1.5613987445831299, | |
| "eval_runtime": 5.1356, | |
| "eval_samples": 142, | |
| "eval_samples_per_second": 27.65, | |
| "eval_steps_per_second": 3.505, | |
| "eval_wer": 0.6765475152571927, | |
| "train_loss": 1.0135024032592774, | |
| "train_runtime": 1622.9478, | |
| "train_samples": 378, | |
| "train_samples_per_second": 9.859, | |
| "train_steps_per_second": 0.616 | |
| } |