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--- |
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language: "en" |
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thumbnail: |
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tags: |
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- Spoken language understanding |
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license: "CC0" |
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datasets: |
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- Timers and Such |
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metrics: |
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- Accuracy |
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--- |
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# End-to-end SLU model for Timers and Such |
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Attention-based RNN sequence-to-sequence model for [Timers and Such](https://zenodo.org/record/4623772) trained on the `train-real` subset. This model checkpoint achieves 86.7% accuracy on `test-real`. |
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The model uses an ASR model trained on LibriSpeech (`speechbrain/asr-crdnn-rnnlm-librispeech`) to extract features from the input audio, then maps these features to an intent and slot labels using a beam search. |
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The dataset has four intents: `SetTimer`, `SetAlarm`, `SimpleMath`, and `UnitConversion`. Try testing the model by saying something like "set a timer for 5 minutes" or "what's 32 degrees Celsius in Fahrenheit?" |
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You can try the model on the `math.wav` file included here as follows: |
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``` |
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from speechbrain.pretrained import EndToEndSLU |
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slu = EndToEndSLU.from_hparams("speechbrain/slu-timers-and-such-direct-librispeech-asr") |
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slu.decode_file("math.wav") |
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``` |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\\\\\\\\\\\\\\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### Referencing Timers and Such |
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(TODO add paper once released) |
