Datasets:

trysem
/

indicvoices_r-ML

	---
	dataset_info:
	- config_name: Malayalam
	features:
	- name: text
	dtype: string
	- name: lang
	dtype: string
	- name: samples
	dtype: int64
	- name: verbatim
	dtype: string
	- name: normalized
	dtype: string
	- name: speaker_id
	dtype: string
	- name: scenario
	dtype: string
	- name: task_name
	dtype: string
	- name: gender
	dtype: string
	- name: age_group
	dtype: string
	- name: job_type
	dtype: string
	- name: qualification
	dtype: string
	- name: area
	dtype: string
	- name: district
	dtype: string
	- name: state
	dtype: string
	- name: occupation
	dtype: string
	- name: audio
	dtype:
	audio:
	sampling_rate: 48000
	- name: utterance_pitch_mean
	dtype: float64
	- name: utterance_pitch_std
	dtype: float64
	- name: snr
	dtype: float64
	- name: c50
	dtype: float64
	- name: speaking_rate
	dtype: float64
	- name: cer
	dtype: string
	- name: duration
	dtype: float64
	splits:
	- name: train
	num_bytes: 57312827276.29399
	num_examples: 31106
	- name: test
	num_bytes: 621717372.9800854
	num_examples: 397
	download_size: 52034095360
	dataset_size: 57934544649.27408
	license: cc-by-4.0
	task_categories:
	- text-to-speech
	language:
	- ml
	pretty_name: indicvoices-r-ML
	---

	# IndicVoices-R-Malayalam
	This dataset is a specific subset of the [IndicVoices-R](https://huggingface.co/datasets/ai4bharat/indicvoices_r) TTS corpus, containing only the Malayalam language data.

	# Meta Data
	- Processing samples: 31106it [23:14, 22.30it/s]
	- Exact Total Duration: 287059.32 seconds
	- Equivalent to: 79.74 hours
	- Total samples processed: 31106
	______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
	## Original Super-Dataset Summary
	IndicVoices-R (IV-R) is the largest multilingual Indian text-to-speech (TTS) dataset derived from an automatic speech recognition (ASR) dataset. It contains 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages. This dataset is designed to enhance the development of robust Indian TTS models by providing diverse speaker demographics, natural conversational speech, and high-quality audio samples.

	## Key Features
	- Comprehensive Coverage: Includes all 22 scheduled Indian languages, ranging from 9 to 175 hours per language.
	- Speaker Diversity: 10,496 speakers, ensuring rich demographic and linguistic variation.
	- High-Quality Samples: Speech quality comparable to gold-standard TTS datasets (LJSpeech, LibriTTS, IndicTTS).
	- Natural Speech Recordings: 93.25% extempore speech, leading to more expressive and natural-sounding synthesis.
	- Zero-shot, Few-shot, and Many-shot Benchmarking: Includes a benchmark dataset for evaluating speaker generalization capabilities of TTS models.

	## Data Processing Pipeline
	The dataset was created by restoring and enhancing ASR-quality speech using:
	1. Demixing: HTDemucs model to separate speech from background noise.
	2. Dereverberation: VoiceFixer to reduce reverb and enhance speech clarity.
	3. Speech Enhancement: DeepFilterNet3 to remove remaining artifacts.
	4. Filtering: Samples filtered based on speech clarity, SNR, pitch variation, and speaking rate.
	5. Normalization: Volume adjusted using PyDub for consistency.

	## Dataset Format
	Each entry in the dataset includes:
	- Audio file: `.wav` format, 48 kHz sampling rate.
	- Text transcript: Available in both verbatim and normalized formats.
	- Metadata JSON: Includes speaker ID, gender, age group, duration, and language.

	## Benchmarks & Evaluation
	IndicVoices-R includes a benchmark suite to evaluate zero-shot, few-shot, and many-shot TTS model performance across different demographics and speech styles.

	### Key Evaluation Metrics:
	- NORESQA-MOS: Measures naturalness of synthesized speech.
	- SNR & C50: Assess speech clarity and reverberation levels.
	- Speaker Similarity (S-SIM): Evaluates zero-shot speaker generalization.

	## Usage & Applications
	IndicVoices-R can be used for:
	- Training multilingual TTS models with high speaker diversity.
	- Improving zero-shot speaker generalization for Indian languages.
	- Building expressive and natural-sounding synthetic voices.
	- Evaluating TTS performance with a standardized benchmark.

	## License
	CC-BY-4.0

	## Acknowledgements
	This project was supported by Digital India Bhashini, the Ministry of Electronics and Information Technology (Government of India),
	EkStep Foundation, and Nilekani Philanthropies. Special thanks to CDAC Pune for access to the PARAM-Siddhi supercomputer,
	enabling our speech enhancement pipeline and model training. We also appreciate the unwavering support from the AI4Bharat team.

	## Citation
	If you use IndicVoices-R, please cite the following paper:
	```
	@inproceedings{ai4bharat2024indicvoices_r,
	author = {Ashwin Sankar and
	Srija Anand and
	Praveen Srinivasa Varadhan and
	Sherry Thomas and
	Mehak Singal and
	Shridhar Kumar and
	Deovrat Mehendale and
	Aditi Krishana and
	Giri Raju and
	Mitesh M. Khapra},
	editor = {Amir Globersons and
	Lester Mackey and
	Danielle Belgrave and
	Angela Fan and
	Ulrich Paquet and
	Jakub M. Tomczak and
	Cheng Zhang},
	title = {IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech
	Corpus for Scaling Indian {TTS}},
	booktitle = {Advances in Neural Information Processing Systems 38: Annual Conference
	on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver,
	BC, Canada, December 10 - 15, 2024},
	year = {2024},
	url = {http://papers.nips.cc/paper\_files/paper/2024/hash/7dfcaf4512bbf2a807a783b90afb6c09-Abstract-Datasets\_and\_Benchmarks\_Track.html},
	}
	```