holylovenia
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README.md
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@@ -19,7 +19,7 @@ Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav
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The model is trained on the training sets of [CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D), [CSED](https://github.com/AkishinoShiame/Chinese-Speech-Emotion-Datasets), [ElderReact](https://github.com/Mayer123/ElderReact), [ESD](https://github.com/HLTSingapore/Emotional-Speech-Data), [IEMOCAP](https://sail.usc.edu/iemocap/iemocap_release.htm), and [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess).
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When using this model, make sure that your speech input is sampled at 16kHz.
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The
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[https://github.com/HLTCHKUST/elderly_ser/tree/main](https://github.com/HLTCHKUST/elderly_ser/tree/main)
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## Evaluation Results
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The model is trained on the training sets of [CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D), [CSED](https://github.com/AkishinoShiame/Chinese-Speech-Emotion-Datasets), [ElderReact](https://github.com/Mayer123/ElderReact), [ESD](https://github.com/HLTSingapore/Emotional-Speech-Data), [IEMOCAP](https://sail.usc.edu/iemocap/iemocap_release.htm), and [TESS](https://www.kaggle.com/datasets/ejlok1/toronto-emotional-speech-set-tess).
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When using this model, make sure that your speech input is sampled at 16kHz.
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The scripts used for training and evaluation can be found here:
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[https://github.com/HLTCHKUST/elderly_ser/tree/main](https://github.com/HLTCHKUST/elderly_ser/tree/main)
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## Evaluation Results
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