Automatic Speech Recognition
Transformers
PyTorch
Abkhaz
wav2vec2
common_voice
Generated from Trainer
Instructions to use pablouribe/xls-r-ab-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pablouribe/xls-r-ab-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="pablouribe/xls-r-ab-test")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("pablouribe/xls-r-ab-test") model = AutoModelForCTC.from_pretrained("pablouribe/xls-r-ab-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run.sh from pablouribe/xls-r-ab-test: direct link, hf CLI and curl.
- Browser
- Download file 726 Bytes
-
https://huggingface.co/pablouribe/xls-r-ab-test/resolve/main/run.sh
- Command line
-
hf download hf://pablouribe/xls-r-ab-test/run.sh
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curl -L -o run.sh https://huggingface.co/pablouribe/xls-r-ab-test/resolve/main/run.sh
726 Bytes
| python run_speech_recognition_ctc.py \ | |
| --dataset_name="common_voice" \ | |
| --model_name_or_path="hf-test/xls-r-dummy" \ | |
| --dataset_config_name="ab" \ | |
| --output_dir="./" \ | |
| --overwrite_output_dir \ | |
| --num_train_epochs="2" \ | |
| --per_device_train_batch_size="16" \ | |
| --gradient_accumulation_steps="2" \ | |
| --learning_rate="3e-4" \ | |
| --warmup_steps="500" \ | |
| --evaluation_strategy="steps" \ | |
| --text_column_name="sentence" \ | |
| --length_column_name="input_length" \ | |
| --save_steps="400" \ | |
| --eval_steps="100" \ | |
| --layerdrop="0.0" \ | |
| --save_total_limit="3" \ | |
| --freeze_feature_encoder \ | |
| --gradient_checkpointing \ | |
| --chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \ | |
| --fp16 \ | |
| --group_by_length \ | |
| --push_to_hub \ | |
| --do_train --do_eval |