Dataset Card for MMCRSC

Dataset Summary

MAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use. The contents and the corresponding descriptions of the corpus include:

The corpus contains 755 hours of speech data, which is mostly mobile recorded data. 1080 speakers from different accent areas in China are invited to participate in the recording. The sentence transcription accuracy is higher than 98%. Recordings are conducted in a quiet indoor environment. The database is divided into training set, validation set, and testing set in a ratio of 51: 1: 2. Detail information such as speech data coding and speaker information is preserved in the metadata file. The domain of recording texts is diversified, including interactive Q&A, music search, SNS messages, home command and control, etc. Segmented transcripts are also provided. The corpus aims to support researchers in speech recognition, machine translation, speaker recognition, and other speech-related fields. Therefore, the corpus is totally free for academic use. The corpus is a subset of a much bigger data ( 10566.9 hours Chinese Mandarin Speech Corpus ) set which was recorded in the same environment. Please feel free to contact us via [email protected] for more details.

Supported Tasks and Leaderboards

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Languages

zh-CN

Dataset Structure

Data Instances

{
  'file': '14_3466_20170826171404.wav',
  'audio': {
    'path': '14_3466_20170826171404.wav',
    'array': array([0., 0., 0., ..., 0., 0., 0.]),
    'sampling_rate': 16000
  },
  'text': '请搜索我附近的超市',
  'speaker_id': 143466,
  'id': '14_3466_20170826171404.wav'
}

Data Fields

• file: A path to the downloaded audio file in .wav format.
• audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].
• text: the transcription of the audio file.
• id: unique id of the data sample.
• speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

Please cite the corpus as "Magic Data Technology Co., Ltd., "http://www.imagicdatatech.com/index.php/home/dataopensource/data_info/id/101", 05/2019".