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The task_categories "noisy_speech_recognition" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
Dataset Card for the Noisy LibriSpeech dataset
Dataset Summary
The noisy speech corpus is constructed by randomly sampling noise clips from the MUSAN noise dataset and adding them to LibriSpeech dataset. The Signal-to-Noise Ratio (SNR) levels are sampled from a uniform distribution in 0 dB, 5 dB, 10 dB, 15 dB, and 20 dB.
Dataset Structure
same structure with LibriSpeech dataset
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