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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
1
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  1. README.md +30 -12
  2. audios/English/ANC/LJ001-0110.wav +3 -0
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README.md CHANGED
@@ -2,11 +2,14 @@
2
  language:
3
  - hi
4
  - ta
 
5
  license: cc-by-4.0
6
  size_categories:
7
  - 100K<n<1M
8
  task_categories:
9
  - text-to-speech
 
 
10
  pretty_name: MANGO
11
  dataset_info:
12
  features:
@@ -57,8 +60,14 @@ dataset_info:
57
  - name: Hindi__MUSHRA
58
  num_bytes: 2601302
59
  num_examples: 11300
60
- download_size: 1045866
61
- dataset_size: 13390740
 
 
 
 
 
 
62
  configs:
63
  - config_name: default
64
  data_files:
@@ -78,10 +87,17 @@ configs:
78
  path: csvs/tamil_mushra.csv
79
  - split: Hindi__MUSHRA
80
  path: csvs/hindi_mushra.csv
 
 
 
 
81
  tags:
82
  - speech
83
  - evaluation
84
  - mushra
 
 
 
85
  ---
86
 
87
  # MANGO: A Corpus of Human Ratings for Speech
@@ -90,7 +106,7 @@ tags:
90
 
91
  ### Key Features:
92
  - **246,000 human ratings** of TTS-generated outputs and ground-truth human speech.
93
- - Covers two major Indian languages: **Hindi** & **Tamil**
94
  - Based on the **MUSHRA** (Multiple Stimuli with Hidden Reference and Anchor) test methodology.
95
  - Ratings are provided on a continuous scale from **0 to 100**, with discrete quality categories:
96
  - **100-80**: Excellent
@@ -118,6 +134,8 @@ The dataset includes the following splits based on the test type and language.
118
  | **Tamil__MUSHRA_DG** | 10000 |
119
  | **Tamil__MUSHRA_DG_NMR** | 10000 |
120
  | **Tamil__MUSHRA_NMR** | 48500 |
 
 
121
 
122
 
123
  ### Getting Started
@@ -170,7 +188,8 @@ print("Splits:", dataset.keys())
170
  '''
171
  Splits: dict_keys(['Tamil__MUSHRA_DG_NMR', 'Hindi__MUSHRA_DG',
172
  'Hindi__MUSHRA_NMR', 'Hindi__MUSHRA_DG_NMR', 'Tamil__MUSHRA_NMR',
173
- 'Tamil__MUSHRA_DG', 'Tamil__MUSHRA', 'Hindi__MUSHRA'])
 
174
  '''
175
 
176
  ```
@@ -187,20 +206,19 @@ We believe this dataset is a valuable resource for researchers and practitioners
187
 
188
  ### Quick Overview of TTS Systems
189
 
190
- 1. **Dataset:** All systems were trained on the [IndicTTS](https://www.iitm.ac.in/donlab/indictts/database) dataset.
191
  2. **Models:** FastSpeech2, VITS, StyleTTS2, XTTS
192
 
193
  ### Citation
194
  ```
195
- @article{ai4bharat2024mango,
196
  title={Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation},
197
- 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},
198
- year={2024},
199
- eprint={2411.12719},
200
- archivePrefix={arXiv},
201
- primaryClass={cs.CL},
202
- url={https://arxiv.org/abs/2411.12719},
203
  }
 
204
  ```
205
 
206
  ### License
 
2
  language:
3
  - hi
4
  - ta
5
+ - en
6
  license: cc-by-4.0
7
  size_categories:
8
  - 100K<n<1M
9
  task_categories:
10
  - text-to-speech
11
+ annotations_creators:
12
+ - crowd-sourced
13
  pretty_name: MANGO
14
  dataset_info:
15
  features:
 
60
  - name: Hindi__MUSHRA
61
  num_bytes: 2601302
62
  num_examples: 11300
63
+ - name: English__MUSHRA
64
+ num_bytes: 170945
65
+ num_examples: 900
66
+ - name: English__MUSHRA_DG_NMR
67
+ num_bytes: 176879
68
+ num_examples: 930
69
+ download_size: 13395762
70
+ dataset_size: 13395762
71
  configs:
72
  - config_name: default
73
  data_files:
 
87
  path: csvs/tamil_mushra.csv
88
  - split: Hindi__MUSHRA
89
  path: csvs/hindi_mushra.csv
90
+ - split: English__MUSHRA
91
+ path: csvs/english_mushra.csv
92
+ - split: English__MUSHRA_DG_NMR
93
+ path: csvs/english_mushra_dg_nmr.csv
94
  tags:
95
  - speech
96
  - evaluation
97
  - mushra
98
+ - text-to-speech
99
+ - human-evaluation
100
+ - multilingual
101
  ---
102
 
103
  # MANGO: A Corpus of Human Ratings for Speech
 
106
 
107
  ### Key Features:
108
  - **246,000 human ratings** of TTS-generated outputs and ground-truth human speech.
109
+ - Covers two major Indian languages: **Hindi** & **Tamil**, and **English**.
110
  - Based on the **MUSHRA** (Multiple Stimuli with Hidden Reference and Anchor) test methodology.
111
  - Ratings are provided on a continuous scale from **0 to 100**, with discrete quality categories:
112
  - **100-80**: Excellent
 
134
  | **Tamil__MUSHRA_DG** | 10000 |
135
  | **Tamil__MUSHRA_DG_NMR** | 10000 |
136
  | **Tamil__MUSHRA_NMR** | 48500 |
137
+ | **English__MUSHRA** | 4500 |
138
+ | **English__MUSHRA_DG_NMR** | 4650 |
139
 
140
 
141
  ### Getting Started
 
188
  '''
189
  Splits: dict_keys(['Tamil__MUSHRA_DG_NMR', 'Hindi__MUSHRA_DG',
190
  'Hindi__MUSHRA_NMR', 'Hindi__MUSHRA_DG_NMR', 'Tamil__MUSHRA_NMR',
191
+ 'Tamil__MUSHRA_DG', 'Tamil__MUSHRA', 'Hindi__MUSHRA',
192
+ 'English__MUSHRA', 'English_MUSHRA_DG_NMR'])
193
  '''
194
 
195
  ```
 
206
 
207
  ### Quick Overview of TTS Systems
208
 
209
+ 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/).
210
  2. **Models:** FastSpeech2, VITS, StyleTTS2, XTTS
211
 
212
  ### Citation
213
  ```
214
+ @article{varadhan2025rethinking,
215
  title={Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation},
216
+ author={Varadhan, Praveen Srinivasa and Gulati, Amogh and Sankar, Ashwin and Anand, Srija and Gupta, Anirudh and Mukherjee, Anirudh and Marepally, Shiva Kumar and Bhatia, Ankur and Jaju, Saloni and Bhooshan, Suvrat and Khapra, Mitesh M.},
217
+ journal={Transactions on Machine Learning Research},
218
+ year={2025},
219
+ url={https://openreview.net/forum?id=oYmRiWCQ1W},
 
 
220
  }
221
+
222
  ```
223
 
224
  ### License
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