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--- |
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language: |
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- hi |
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- ta |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- text-to-speech |
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annotations_creators: |
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- crowd-sourced |
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pretty_name: MANGO |
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dataset_info: |
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features: |
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- name: Rater_ID |
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dtype: int64 |
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- name: FS2_Score |
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dtype: int64 |
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- name: VITS_Score |
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dtype: int64 |
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- name: ST2_Score |
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dtype: int64 |
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- name: ANC_Score |
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dtype: int64 |
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- name: REF_Score |
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dtype: int64 |
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- name: FS2_Audio |
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dtype: string |
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- name: VITS_Audio |
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dtype: string |
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- name: ST2_Audio |
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dtype: string |
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- name: ANC_Audio |
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dtype: string |
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- name: REF_Audio |
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dtype: string |
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splits: |
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- name: Tamil__MUSHRA_DG_NMR |
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num_bytes: 421059 |
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num_examples: 2000 |
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- name: Hindi__MUSHRA_DG |
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num_bytes: 460394 |
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num_examples: 2000 |
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- name: Hindi__MUSHRA_NMR |
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num_bytes: 2344032 |
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num_examples: 10200 |
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- name: Hindi__MUSHRA_DG_NMR |
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num_bytes: 459746 |
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num_examples: 2000 |
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- name: Tamil__MUSHRA_NMR |
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num_bytes: 2034556 |
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num_examples: 9700 |
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- name: Tamil__MUSHRA_DG |
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num_bytes: 420012 |
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num_examples: 2000 |
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- name: Tamil__MUSHRA |
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num_bytes: 2098507 |
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num_examples: 10000 |
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- name: Hindi__MUSHRA |
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num_bytes: 2601302 |
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num_examples: 11300 |
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- name: English__MUSHRA |
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num_bytes: 170945 |
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num_examples: 900 |
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- name: English__MUSHRA_DG_NMR |
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num_bytes: 176879 |
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num_examples: 930 |
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download_size: 13395762 |
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dataset_size: 13395762 |
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configs: |
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- config_name: default |
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data_files: |
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- split: Tamil__MUSHRA_DG_NMR |
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path: csvs/tamil_mushra_dg_nmr.csv |
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- split: Hindi__MUSHRA_DG |
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path: csvs/hindi_mushra_dg.csv |
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- split: Hindi__MUSHRA_NMR |
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path: csvs/hindi_mushra_nmr.csv |
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- split: Hindi__MUSHRA_DG_NMR |
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path: csvs/hindi_mushra_dg_nmr.csv |
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- split: Tamil__MUSHRA_NMR |
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path: csvs/tamil_mushra_nmr.csv |
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- split: Tamil__MUSHRA_DG |
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path: csvs/tamil_mushra_dg.csv |
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- split: Tamil__MUSHRA |
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path: csvs/tamil_mushra.csv |
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- split: Hindi__MUSHRA |
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path: csvs/hindi_mushra.csv |
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- split: English__MUSHRA |
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path: csvs/english_mushra.csv |
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- split: English__MUSHRA_DG_NMR |
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path: csvs/english_mushra_dg_nmr.csv |
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tags: |
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- speech |
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- evaluation |
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- mushra |
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- text-to-speech |
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- human-evaluation |
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- multilingual |
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--- |
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# MANGO: A Corpus of Human Ratings for Speech |
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**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. |
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### Key Features: |
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- **255,150 human ratings** of TTS-generated outputs and ground-truth human speech. |
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- Covers two major Indian languages: **Hindi** & **Tamil**, and **English**. |
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- Based on the **MUSHRA** (Multiple Stimuli with Hidden Reference and Anchor) test methodology. |
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- Ratings are provided on a continuous scale from **0 to 100**, with discrete quality categories: |
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- **100-80**: Excellent |
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- **80-60**: Good |
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- **60-40**: Fair |
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- **40-20**: Poor |
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- **20-0**: Bad |
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- Includes evaluations involving: |
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- *MUSHRA*: with explicitly mentioned high-quality references. |
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- *MUSHRA-NMR*: without explicitly mentioned high-quality references. |
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- *MUSHRA-DG*: with detailed guidelines across fine-grained dimensions |
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- *MUSHRA-DG-NMR*: with detailed guidelines across fine-grained dimensions and without explicitly mentioned high-quality references. |
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### Available Splits |
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The dataset includes the following splits based on the test type and language. |
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| **Split** | **Number of Ratings** | |
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|---------------------------|:---------------------------------------------------------------------:| |
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| **Hindi__MUSHRA** | 56500 | |
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| **Hindi__MUSHRA_DG** | 10000 | |
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| **Hindi__MUSHRA_DG_NMR** | 10000 | |
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| **Hindi__MUSHRA_NMR** | 51000 | |
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| **Tamil__MUSHRA** | 50000 | |
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| **Tamil__MUSHRA_DG** | 10000 | |
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| **Tamil__MUSHRA_DG_NMR** | 10000 | |
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| **Tamil__MUSHRA_NMR** | 48500 | |
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| **English__MUSHRA** | 4500 | |
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| **English__MUSHRA_DG_NMR** | 4650 | |
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### Getting Started |
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```python |
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import os |
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from datasets import load_dataset, Audio |
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from huggingface_hub import snapshot_download |
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def get_audio_paths(example): |
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for column in example.keys(): |
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if "Audio" in column and isinstance(example[column], str): |
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example[column] = os.path.join(download_dir, example[column]) |
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return example |
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# Download |
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repo_id = "ai4bharat/MANGO" |
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download_dir = snapshot_download(repo_id=repo_id, repo_type="dataset") |
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dataset = load_dataset(download_dir, split='Hindi__MUSHRA') |
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dataset = dataset.map(get_audio_paths) |
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# Cast audio columns |
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for column in dataset.column_names: |
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if 'Audio' in column: |
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dataset = dataset.cast_column(column, Audio()) |
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# Explore |
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print(dataset) |
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''' |
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Dataset({ |
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features: ['Rater_ID', 'FS2_Score', 'VITS_Score', 'ST2_Score', 'ANC_Score', |
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'REF_Score', 'FS2_Audio', 'VITS_Audio', 'ST2_Audio', 'ANC_Audio', |
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'REF_Audio'], |
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num_rows: 11300 |
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}) |
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''' |
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# # Print first instance |
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print(dataset[0]) |
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''' |
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{'Rater_ID': 389, 'FS2_Score': 16, 'VITS_Score': 76, 'ST2_Score': 28, |
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'ANC_Score': 40, 'REF_Score': 100, 'FS2_Audio': {'path': ... |
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''' |
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# # Available Splits |
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dataset = load_dataset(download_dir, split=None) |
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print("Splits:", dataset.keys()) |
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''' |
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Splits: dict_keys(['Tamil__MUSHRA_DG_NMR', 'Hindi__MUSHRA_DG', |
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'Hindi__MUSHRA_NMR', 'Hindi__MUSHRA_DG_NMR', 'Tamil__MUSHRA_NMR', |
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'Tamil__MUSHRA_DG', 'Tamil__MUSHRA', 'Hindi__MUSHRA', |
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'English__MUSHRA', 'English_MUSHRA_DG_NMR']) |
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''' |
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``` |
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### Why Use MANGO? |
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- Addresses limitations of traditional **MOS** and **CMOS** tests. |
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- Enables robust benchmarking for: |
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- Comparative analysis across multiple TTS systems. |
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- Evaluations in diverse linguistic contexts. |
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- Large-scale studies with multiple raters. |
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We believe this dataset is a valuable resource for researchers and practitioners working on speech synthesis evaluation, and related fields. |
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### Quick Overview of TTS Systems |
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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/). |
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2. **Models:** FastSpeech2, VITS, StyleTTS2, XTTS |
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### Citation |
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``` |
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@article{ai4bharat2025rethinking, |
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title={Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation}, |
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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}, |
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journal={Transactions on Machine Learning Research}, |
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year={2025}, |
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url={https://openreview.net/forum?id=oYmRiWCQ1W}, |
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} |
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``` |
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### License |
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This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). |
