Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Abkhaz
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_7_0
robust-speech-event
Instructions to use cahya/xls-r-ab-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/xls-r-ab-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/xls-r-ab-test")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cahya/xls-r-ab-test") model = AutoModelForCTC.from_pretrained("cahya/xls-r-ab-test", 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="hf-test/xls-r-dummy" \ | |
| --dataset_config_name="ab" \ | |
| --output_dir="./" \ | |
| --overwrite_output_dir \ | |
| --max_steps="100" \ | |
| --per_device_train_batch_size="2" \ | |
| --learning_rate="3e-4" \ | |
| --save_total_limit="1" \ | |
| --evaluation_strategy="steps" \ | |
| --text_column_name="sentence" \ | |
| --length_column_name="input_length" \ | |
| --save_steps="5" \ | |
| --layerdrop="0.0" \ | |
| --freeze_feature_encoder \ | |
| --gradient_checkpointing \ | |
| --fp16=false \ | |
| --group_by_length \ | |
| --push_to_hub \ | |
| --use_auth_token \ | |
| --do_train --do_eval | |