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
| python run_speech_recognition_ctc.py \ | |
| --dataset_name="mozilla-foundation/common_voice_7_0" \ | |
| --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ | |
| --dataset_config_name="ur" \ | |
| --output_dir="./" \ | |
| --overwrite_output_dir \ | |
| --max_steps="1000" \ | |
| --per_device_train_batch_size="16" \ | |
| --learning_rate="3e-4" \ | |
| --save_total_limit="10" \ | |
| --evaluation_strategy="steps" \ | |
| --text_column_name="sentence" \ | |
| --length_column_name="input_length" \ | |
| --chars_to_ignore ,\?\.\!\-\;\:\"\โ\%\โ\โ\๏ฟฝ\ู\ู\ูู,ู\ | |
| --save_steps="100" \ | |
| --layerdrop="0.0" \ | |
| --freeze_feature_encoder \ | |
| --gradient_checkpointing \ | |
| --fp16 \ | |
| --group_by_length \ | |
| --push_to_hub \ | |
| --use_auth_token \ | |
| --do_train --do_eval |