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ASAP is a technology designed for learning agile whole-body skills in humanoid robots, achieving skill transfer through alignment of simulation with real-world physics.
alexandreacff
This model is a fine-tuned audio classification model based on asapp/sew-mid-100k on the alexandreacff/kaggle-fake-detection dataset, designed for fake audio detection.
asapp
SEW-D-mid is a speech pre-training model developed by ASAPP Research, focusing on automatic speech recognition tasks, achieving a good balance between performance and efficiency.
patrickvonplaten
This model is an automatic speech recognition model fine-tuned from asapp/sew-d-mid-400k on the LIBRISPEECH_ASR - CLEAN dataset, achieving a word error rate (WER) of 1.0536 on the evaluation set.
SEW-tiny is a compressed and efficient speech pretraining model developed by ASAPP Research, pretrained on 16kHz sampled speech audio, suitable for various downstream speech tasks.
SEW-D-tiny is an efficient speech recognition pre-trained model developed by ASAPP Research, focusing on the balance between performance and efficiency.
This model is an automatic speech recognition model fine-tuned from asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset, achieving a word error rate of 0.8061 on the evaluation set.
SEW-D-base+ is an efficient speech recognition model developed by ASAPP Research, pre-trained on 16kHz sampled speech audio, and excels on the LibriSpeech dataset.
SEW (Squeezed and Efficient Wav2vec) is a speech recognition pre-trained model developed by ASAPP Research, outperforming wav2vec 2.0 in both performance and efficiency.
An automatic speech recognition model fine-tuned on the TIMIT_ASR dataset based on asapp/sew-d-small-100k
SEW-D is a compressed and efficient speech pre-training model developed by ASAPP Research, pre-trained on 16kHz sampled speech audio, suitable for various downstream speech tasks.
SEW-D-mid-k127 is an efficient speech recognition pre-trained model developed by ASAPP Research, demonstrating significant improvements in performance and efficiency compared to wav2vec 2.0.
An automatic speech recognition model fine-tuned on the TIMIT_ASR - NA dataset based on asapp/sew-small-100k
anton-l
This model is a fine-tuned version of asapp/sew-mid-100k on the superb dataset, primarily used for keyword spotting tasks.
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