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
File size: 605 Bytes
299e150 7ec9ad6 299e150 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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
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