Instructions to use espnet/UniverSLU-17-Task-Specifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/UniverSLU-17-Task-Specifier with ESPnet:
import soundfile from espnet2.bin.asr_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/UniverSLU-17-Task-Specifier" ) speech, rate = soundfile.read("speech.wav") text, *_ = model(speech)[0] - Notebooks
- Google Colab
- Kaggle
File size: 256 Bytes
254419c | 1 2 3 4 5 | files:
asr_model_file: exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/valid.acc.ave_10best.pth
yaml_files:
asr_train_config: exp/asr_train_asr_whisper_full_correct_specaug2_copy_raw_en_whisper_multilingual/config.yaml
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