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README.md
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dtype: string
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- name: text
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dtype: string
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- name: speakerID
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dtype: string
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- name: gender
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dtype: int64
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- name: audio
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dtype:
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audio:
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sampling_rate: 16000
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- name: new_province_name
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dtype: string
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- name: length
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dtype: float64
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splits:
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- name: train
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num_bytes: 4732880672
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num_examples: 15023
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- name: test
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num_bytes: 635471319
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num_examples: 2026
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- name: valid
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num_bytes: 598490941
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num_examples: 1900
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download_size: 5962186491
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dataset_size: 5966842932
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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- split: valid
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path: data/valid-*
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language:
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- vi
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license: other
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task_categories:
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- audio-classification
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- automatic-speech-recognition
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pretty_name: ViMD Truncated 10s (16kHz)
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size_categories:
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- 10K<n<100K
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---
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# ViMD Truncated 10s — 16kHz
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Preprocessed version of **ViMD** (Nguyen et al., EMNLP 2024) for Dialect Identification.
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## Preprocessing applied
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- **Resample:** 44.1kHz (original) → **16kHz, mono**
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- **Truncate:** only the **FIRST 10 SECONDS** of each audio are kept
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(files shorter than 10s are kept intact). **1 original file = 1 sample.**
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This follows the truncation strategy of Lu et al. (2020), NOT chunking.
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- **Splits:** original ViMD train/valid/test kept unchanged (speaker-exclusive).
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## New column vs. original ViMD
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- `new_province`: province name **after the administrative merger** (34 classes),
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mapped from the original `province` column (63 classes).
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## Usage
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```python
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from datasets import load_dataset
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ds = load_dataset("tannhoo06/ViMD_preprocessing")
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```
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## EDA without downloading audio
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```python
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import pandas as pd
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meta = pd.read_parquet("hf://datasets/tannhoo06/ViMD_preprocessing/metadata.parquet")
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```
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## Citation
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Please cite the original ViMD paper (Nguyen et al., EMNLP 2024).
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