Instructions to use patrickvonplaten/wav2vec2_tiny_random_robust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickvonplaten/wav2vec2_tiny_random_robust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="patrickvonplaten/wav2vec2_tiny_random_robust")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2_tiny_random_robust") model = AutoModel.from_pretrained("patrickvonplaten/wav2vec2_tiny_random_robust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download run.sh from patrickvonplaten/wav2vec2_tiny_random_robust: direct link, hf CLI and curl.
- Browser
- Download file 631 Bytes
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https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust/resolve/main/run.sh
- Command line
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hf download hf://patrickvonplaten/wav2vec2_tiny_random_robust/run.sh
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curl -L -o run.sh https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust/resolve/main/run.sh
631 Bytes
| python ../transformers/examples/research_projects/wav2vec2/run_asr.py --output_dir=output_dir --num_train_epochs=1 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --evaluation_strategy=steps --save_steps=500 --eval_steps=100 --logging_steps=50 --learning_rate=5e-4 --warmup_steps=3000 --model_name_or_path=./wav2vec2_tiny_random_robust --dataset_name=patrickvonplaten/librispeech_asr_dummy --dataset_config_name=clean --train_split_name=validation --validation_split_name=validation --orthography=timit --preprocessing_num_workers=1 --group_by_length --freeze_feature_extractor --verbose_logging | |