Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
Instructions to use jcmc/wav2vec2-xls-r-1b-ir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcmc/wav2vec2-xls-r-1b-ir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jcmc/wav2vec2-xls-r-1b-ir")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jcmc/wav2vec2-xls-r-1b-ir") model = AutoModelForCTC.from_pretrained("jcmc/wav2vec2-xls-r-1b-ir", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| python run_speech_recognition_ctc_bnb.py \ | |
| --dataset_name="mozilla-foundation/common_voice_7_0" \ | |
| --model_name_or_path="facebook/wav2vec2-xls-r-1b" \ | |
| --dataset_config_name="ga-IE" \ | |
| --output_dir="./" \ | |
| --overwrite_output_dir \ | |
| --num_train_epochs="100" \ | |
| --per_device_train_batch_size="8" \ | |
| --per_device_eval_batch_size="8" \ | |
| --gradient_accumulation_steps="4" \ | |
| --learning_rate="5e-5" \ | |
| --warmup_steps="500" \ | |
| --length_column_name="input_length" \ | |
| --evaluation_strategy="steps" \ | |
| --text_column_name="sentence" \ | |
| --chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … – \ | |
| --save_steps="500" \ | |
| --eval_steps="500" \ | |
| --logging_steps="100" \ | |
| --layerdrop="0.0" \ | |
| --activation_dropout="0.1" \ | |
| --save_total_limit="3" \ | |
| --freeze_feature_encoder \ | |
| --feat_proj_dropout="0.0" \ | |
| --mask_time_prob="0.75" \ | |
| --mask_time_length="10" \ | |
| --mask_feature_prob="0.25" \ | |
| --mask_feature_length="64" \ | |
| --gradient_checkpointing \ | |
| --use_auth_token \ | |
| --fp16 \ | |
| --group_by_length \ | |
| --do_train --do_eval \ | |
| --push_to_hub | |