Instructions to use NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal") model = AutoModelForCTC.from_pretrained("NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix modelhub id
Browse files
run.sh
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WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
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--dataset_name="NbAiLab/NPSC" \
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--model_name_or_path="KBLab/wav2vec2-large-voxrex" \
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--hub_model_id="NbAiLab/wav2vec2-large-voxrex-npsc" \
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--dataset_config_name="16K_mp3" \
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--output_dir="./" \
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--overwrite_output_dir \
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WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
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--dataset_name="NbAiLab/NPSC" \
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--model_name_or_path="KBLab/wav2vec2-large-voxrex" \
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--hub_model_id="NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal" \
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--dataset_config_name="16K_mp3" \
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--output_dir="./" \
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--overwrite_output_dir \
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