---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence_orig
    dtype: string
  - name: sentence_norm
    dtype: string
  splits:
  - name: train
    num_bytes: 822408859.068
    num_examples: 3846
  download_size: 755846406
  dataset_size: 822408859.068
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
language:
- mn
task_categories:
- text-to-speech
tags:
- mongolian
- speech
- dataset
- text-to-speech
- audio
- tts
- biblical
pretty_name: mbspeech_mn
---

# MBSpeech MN: Mongolian Biblical Speech Dataset

MBSpeech MN is a Mongolian text-to-speech (TTS) dataset derived from biblical texts. It consists of aligned audio recordings and corresponding sentences in Mongolian. The dataset is suitable for training TTS models and other speech processing applications.

## Dataset Summary

  Language: Mongolian (mn)

  Task: Text-to-Speech (TTS)

  License: MIT

  Size:

      Download size: ~721 MB

      Dataset size: ~822 MB

      Examples: 3,846

## Dataset Structure

### Features
Name	Type	Description
audio	Audio	Audio data sampled at 16 kHz
sentence	string	Transcription in Mongolian

### Splits
Split	Examples	Size
train	3,846	~822 MB



## Usage

To convert the dataset into an LJSpeech–style format for TTS model training:

```python
import os
import csv
import soundfile as sf
from datasets import load_dataset

# Dataset and output configuration
DATASET_NAME = "btsee/mbspeech_mn"
OUTPUT_DIR = "dataset"
WAV_DIR = os.path.join(OUTPUT_DIR, "wavs")
os.makedirs(WAV_DIR, exist_ok=True)

# Load dataset
ds = load_dataset(DATASET_NAME, split="train")

# Export audio files and metadata
with open(os.path.join(OUTPUT_DIR, "metadata.csv"), "w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f, delimiter="|")
    for idx, item in enumerate(ds):
        array = item["audio"]["array"]
        sr = item["audio"]["sampling_rate"]
        text = item["sentence"]
        fname = f"{idx:05d}"
        path = os.path.join(WAV_DIR, f"{fname}.wav")
        sf.write(path, array, sr, subtype="PCM_16")
        writer.writerow([fname, text])
```