---
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

```python
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](https://www.iitm.ac.in/donlab/indictts/database) dataset. For English, we use models trained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
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)](https://creativecommons.org/licenses/by/4.0/).