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data/Falam_23305.wav
Falam
8.064
data/Falam_10987.wav
Falam
9.984
data/Falam_02055.wav
Falam
9.984
data/Falam_22545.wav
Falam
7.68
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9.728
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9.152
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8.544
data/Falam_19919.wav
Falam
9.344
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7.616
data/Falam_22233.wav
Falam
9.536
data/Falam_10524.wav
Falam
9.504
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8.928
data/Falam_02723.wav
Falam
8.96
data/Falam_22594.wav
Falam
8.896
data/Falam_10283.wav
Falam
8.64
data/Falam_11364.wav
Falam
8.128
data/Falam_23473.wav
Falam
7.808
data/Falam_16772.wav
Falam
8.224
data/Falam_23801.wav
Falam
9.312
data/Falam_14848.wav
Falam
8.8
data/Falam_03163.wav
Falam
7.36
data/Falam_12271.wav
Falam
9.312
data/Falam_17695.wav
Falam
9.344
data/Falam_20914.wav
Falam
9.888
data/Falam_24065.wav
Falam
9.92
data/Falam_20566.wav
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9.248
data/Falam_17288.wav
Falam
9.92
data/Falam_00076.wav
Falam
9.76
data/Falam_05728.wav
Falam
7.584
data/Falam_01191.wav
Falam
8.544
data/Falam_21326.wav
Falam
9.088
data/Falam_13843.wav
Falam
8.224
data/Falam_13431.wav
Falam
9.088
data/Falam_24478.wav
Falam
8.448
data/Falam_04168.wav
Falam
9.088
data/Falam_22189.wav
Falam
9.568
data/Falam_01636.wav
Falam
6.912
data/Falam_21481.wav
Falam
9.536
data/Falam_07812.wav
Falam
9.408
data/Falam_13396.wav
Falam
7.968
data/Falam_23012.wav
Falam
7.776
data/Falam_11705.wav
Falam
5.664
data/Falam_06989.wav
Falam
9.12
data/Falam_03970.wav
Falam
9.888
data/Falam_03502.wav
Falam
9.12
data/Falam_02897.wav
Falam
9.856
data/Falam_10145.wav
Falam
8.672
data/Falam_22652.wav
Falam
9.344
data/Falam_02342.wav
Falam
8.352
data/Falam_01982.wav
Falam
8.16
data/Falam_24019.wav
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9.568
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9.088
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8.128
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9.056
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6.656
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7.488
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9.216
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Falam
7.2
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Falam
8.512
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Falam
9.6
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Falam
8.96
data/Falam_22317.wav
Falam
9.92
data/Falam_10139.wav
Falam
8.288
data/Falam_11995.wav
Falam
8.736
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9.92
data/Falam_09352.wav
Falam
7.904
data/Falam_14214.wav
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9.92
data/Falam_03047.wav
Falam
9.152
data/Falam_08605.wav
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8.864
data/Falam_07682.wav
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7.584
data/Falam_01064.wav
Falam
9.248
data/Falam_11938.wav
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9.504
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9.696
data/Falam_04662.wav
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9.408
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8.8
data/Falam_00183.wav
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9.728
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Falam
8.64
data/Falam_23557.wav
Falam
8.672
data/Falam_05785.wav
Falam
8.704
data/Falam_15322.wav
Falam
8.032
data/Falam_20334.wav
Falam
9.568
data/Falam_07653.wav
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9.408
data/Falam_11240.wav
Falam
8.896
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Falam
9.728
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Falam
8.704
data/Falam_12423.wav
Falam
7.744
data/Falam_15454.wav
Falam
8.864
data/Falam_18579.wav
Falam
9.568
data/Falam_17225.wav
Falam
9.6
data/Falam_07125.wav
Falam
8.608
data/Falam_12851.wav
Falam
8.416
data/Falam_15826.wav
Falam
8.832
data/Falam_12880.wav
Falam
8.48
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MizoSpeech Banner

Disclaimer / Notice: Details for these are in Peer Review and publications of the paper will be made available soon for more details.

Dataset Description

The MizoSpeech is an audio corpus designed for unsupervised pre-training and linguistic research on the Mizo language and its associated dialects. It encompasses 761,053 .wav audio recordings across 10 distinct dialects within the Kuki-Chin-Mizo branch of the Tibeto-Burman family.

This dataset is distributed in Parquet format, split into ~500 MB shards.

  • Language(s): Falam, Gangte, Hmar, Lai, Lushai, Mara, Paite, Ralte, Vaiphei, Zou

Repository Structure

The primary dataset repository consists entirely of processed data:

  • /data — Contains the entire dataset serialized into parquet format, split into approximately 500 MB shards (e.g., mizospeech-00000-of-00408.parquet). The raw audio bytes are securely embedded within these parquet chunks alongside their corresponding metadata, removing the need to manage thousands of raw .wav files locally while facilitating high-throughput streaming and loading.

Data Fields

Field Name Type Description
audio AudioFeature A dictionary containing the decoded audio waveform. Includes the internal path string, the decoded numeric array, and the sampling_rate.
path string The explicit directory path mapping to the original file structure (e.g., data/Falam_00001.wav).
language string The specific Mizo dialect spoken in the audio recording (Falam, Gangte, Hmar, Lai, Lushai, Mara, Paite, Ralte, Vaiphei, Zou).
duration float32 The total duration of the audio clip in seconds. Highly useful for filtering extremely long or short clips during training.

Usage and Pre-processing

from datasets import load_dataset

dataset = load_dataset("andrewbawitlung/MizoSpeech", split="mizospeech")

Dataset Statistics

Dialect Number of Utterances Total Audio Duration (Hours)
Lushai 425,794 1,048.34
Hmar 118,833 276.43
Lai 107,289 264.37
Paite 53,162 126.89
Falam 24,923 62.02
Vaiphei 15,806 39.66
Mara 9,111 22.73
Gangte 5,428 13.71
Ralte 492 1.22
Zou 215 0.51
Total 761,053 1,855.88

Audio Specifications

  • Format: .wav embedded in Parquet
  • Sampling Rate: 16,000 Hz
  • Channels: Mono (1)
  • Bit Depth: 16-bit
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