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Datasets:
ai4bharat
/
MANGO

Tasks:
Text-to-Speech
Modalities:
Audio
Tabular
Text
Formats:
csv
Languages:
Hindi
Tamil
English
Size:
10K - 100K
Tags:
speech
evaluation
mushra
text-to-speech
human-evaluation
multilingual
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
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MANGO / README.md
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metadata
language:
  - hi
  - ta
  - en
license: cc-by-4.0
size_categories:
  - 100K<n<1M
task_categories:
  - text-to-speech
annotations_creators:
  - crowd-sourced
pretty_name: MANGO
dataset_info:
  features:
    - name: Rater_ID
      dtype: int64
    - name: FS2_Score
      dtype: int64
    - name: VITS_Score
      dtype: int64
    - name: ST2_Score
      dtype: int64
    - name: ANC_Score
      dtype: int64
    - name: REF_Score
      dtype: int64
    - name: FS2_Audio
      dtype: string
    - name: VITS_Audio
      dtype: string
    - name: ST2_Audio
      dtype: string
    - name: ANC_Audio
      dtype: string
    - name: REF_Audio
      dtype: string
  splits:
    - name: Tamil__MUSHRA_DG_NMR
      num_bytes: 421059
      num_examples: 2000
    - name: Hindi__MUSHRA_DG
      num_bytes: 460394
      num_examples: 2000
    - name: Hindi__MUSHRA_NMR
      num_bytes: 2344032
      num_examples: 10200
    - name: Hindi__MUSHRA_DG_NMR
      num_bytes: 459746
      num_examples: 2000
    - name: Tamil__MUSHRA_NMR
      num_bytes: 2034556
      num_examples: 9700
    - name: Tamil__MUSHRA_DG
      num_bytes: 420012
      num_examples: 2000
    - name: Tamil__MUSHRA
      num_bytes: 2098507
      num_examples: 10000
    - name: Hindi__MUSHRA
      num_bytes: 2601302
      num_examples: 11300
    - name: English__MUSHRA
      num_bytes: 170945
      num_examples: 900
    - name: English__MUSHRA_DG_NMR
      num_bytes: 176879
      num_examples: 930
  download_size: 13395762
  dataset_size: 13395762
configs:
  - config_name: default
    data_files:
      - split: Tamil__MUSHRA_DG_NMR
        path: csvs/tamil_mushra_dg_nmr.csv
      - split: Hindi__MUSHRA_DG
        path: csvs/hindi_mushra_dg.csv
      - split: Hindi__MUSHRA_NMR
        path: csvs/hindi_mushra_nmr.csv
      - split: Hindi__MUSHRA_DG_NMR
        path: csvs/hindi_mushra_dg_nmr.csv
      - split: Tamil__MUSHRA_NMR
        path: csvs/tamil_mushra_nmr.csv
      - split: Tamil__MUSHRA_DG
        path: csvs/tamil_mushra_dg.csv
      - split: Tamil__MUSHRA
        path: csvs/tamil_mushra.csv
      - split: Hindi__MUSHRA
        path: csvs/hindi_mushra.csv
      - split: English__MUSHRA
        path: csvs/english_mushra.csv
      - split: English__MUSHRA_DG_NMR
        path: csvs/english_mushra_dg_nmr.csv
tags:
  - speech
  - evaluation
  - mushra
  - text-to-speech
  - human-evaluation
  - multilingual

MANGO: A Corpus of Human Ratings for Speech

MANGO (MUSHRA Assessment corpus using Native listeners and Guidelines to understand human Opinions at scale) is the first large-scale dataset designed for evaluating Text-to-Speech (TTS) systems in Indian languages.

Key Features:

  • 255,150 human ratings of TTS-generated outputs and ground-truth human speech.
  • Covers two major Indian languages: Hindi & Tamil, and English.
  • Based on the MUSHRA (Multiple Stimuli with Hidden Reference and Anchor) test methodology.
  • Ratings are provided on a continuous scale from 0 to 100, with discrete quality categories:
    • 100-80: Excellent
    • 80-60: Good
    • 60-40: Fair
    • 40-20: Poor
    • 20-0: Bad
  • Includes evaluations involving:
    • MUSHRA: with explicitly mentioned high-quality references.
    • MUSHRA-NMR: without explicitly mentioned high-quality references.
    • MUSHRA-DG: with detailed guidelines across fine-grained dimensions
    • MUSHRA-DG-NMR: with detailed guidelines across fine-grained dimensions and without explicitly mentioned high-quality references.

Available Splits

The dataset includes the following splits based on the test type and language.

Split Number of Ratings
Hindi__MUSHRA 56500
Hindi__MUSHRA_DG 10000
Hindi__MUSHRA_DG_NMR 10000
Hindi__MUSHRA_NMR 51000
Tamil__MUSHRA 50000
Tamil__MUSHRA_DG 10000
Tamil__MUSHRA_DG_NMR 10000
Tamil__MUSHRA_NMR 48500
English__MUSHRA 4500
English__MUSHRA_DG_NMR 4650

Getting Started 

import os
from datasets import load_dataset, Audio
from huggingface_hub import snapshot_download


def get_audio_paths(example):
    for column in example.keys():
        if "Audio" in column and isinstance(example[column], str):
            example[column] = os.path.join(download_dir, example[column])
    return example


# Download
repo_id = "ai4bharat/MANGO"
download_dir = snapshot_download(repo_id=repo_id, repo_type="dataset")
dataset = load_dataset(download_dir, split='Hindi__MUSHRA')
dataset = dataset.map(get_audio_paths)

# Cast audio columns
for column in dataset.column_names:
    if 'Audio' in column:
        dataset = dataset.cast_column(column, Audio())

# Explore
print(dataset)
'''
Dataset({
    features: ['Rater_ID', 'FS2_Score', 'VITS_Score', 'ST2_Score', 'ANC_Score',
               'REF_Score', 'FS2_Audio', 'VITS_Audio', 'ST2_Audio', 'ANC_Audio',
               'REF_Audio'],
    num_rows: 11300
})
'''

# # Print first instance
print(dataset[0])
'''
{'Rater_ID': 389, 'FS2_Score': 16, 'VITS_Score': 76, 'ST2_Score': 28,
 'ANC_Score': 40, 'REF_Score': 100, 'FS2_Audio': {'path': ...
'''

# # Available Splits
dataset = load_dataset(download_dir, split=None)
print("Splits:", dataset.keys())
'''
Splits: dict_keys(['Tamil__MUSHRA_DG_NMR', 'Hindi__MUSHRA_DG', 
    'Hindi__MUSHRA_NMR', 'Hindi__MUSHRA_DG_NMR', 'Tamil__MUSHRA_NMR', 
    'Tamil__MUSHRA_DG', 'Tamil__MUSHRA', 'Hindi__MUSHRA', 
    'English__MUSHRA', 'English_MUSHRA_DG_NMR'])
'''

Why Use MANGO?

  • Addresses limitations of traditional MOS and CMOS tests.
  • Enables robust benchmarking for:
    • Comparative analysis across multiple TTS systems.
    • Evaluations in diverse linguistic contexts.
    • Large-scale studies with multiple raters.

We believe this dataset is a valuable resource for researchers and practitioners working on speech synthesis evaluation, and related fields.

Quick Overview of TTS Systems

  1. Dataset: All Indian TTS systems were trained on the IndicTTS dataset. For English, we use models trained on LJSpeech.
  2. Models: FastSpeech2, VITS, StyleTTS2, XTTS

Citation 

@article{ai4bharat2025rethinking,
  title={Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation},
  author={Praveen Srinivasa Varadhan and Amogh Gulati and Ashwin Sankar and Srija Anand and Anirudh Gupta and Anirudh Mukherjee and Shiva Kumar Marepally and Ankur Bhatia and Saloni Jaju and Suvrat Bhooshan and Mitesh M. Khapra},
  journal={Transactions on Machine Learning Research},
  year={2025},
  url={https://openreview.net/forum?id=oYmRiWCQ1W},
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).