README.md

metadata

language:
  - as
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - ne
  - or
  - pa
  - ta
  - te
license: cc-by-4.0
task_categories:
  - text-to-speech
  - automatic-speech-recognition
size_categories:
  - 100K<n<1M
tags:
  - indic
  - multilingual
  - tts
  - speech

Processed TTS Multilingual Data

Validated and quality-checked multilingual speech datasets for TTS training, covering 12+ Indian languages.

Datasets Included

Subset	Samples	Hours	Description
indic_voices_r	239,684	548.8h	Indic Voices_R — IVR recordings
rasa	201,509	361.2h	RASA — read speech (wiki, conv, book, news)
indictts_iitm	155,236	253.6h	Indic TTS (IIT Madras) — studio TTS recordings at 48kHz
Total	596,429	1,163.6h

Languages

Assamese (as), Bengali (bn), English (en), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Nepali (ne), Odia (or), Punjabi (pa), Tamil (ta), Telugu (te)

Structure

├── indic_voices_r/
│   ├── metadata.csv
│   └── audio/{lang}/*.wav
├── rasa/
│   ├── metadata.csv
│   └── audio/{lang}/*.wav
└── indictts_iitm/
    ├── metadata.csv
    └── audio/{lang}/*.wav

Schema (metadata.csv)

Each subset has a metadata.csv with these columns:

Field	Description
file_name	Relative path to audio file (e.g., audio/bn/file.wav)
text	Transcript text
lang	ISO 639-1 language code
speaker_id	Speaker identifier
duration	Audio duration in seconds
source	Original data source
emotion	Emotion label
domain	Text domain (wiki, conv, book, news, etc.)
snr_db	Signal-to-noise ratio in dB
silence_ratio	Fraction of silent frames
clipping_ratio	Fraction of clipped samples

Quality Checks Applied

All data has been validated through a 4-check pipeline:

1. SNR + Silence + Duration — reject low SNR (<10dB), excess silence (>35%), out-of-range duration (<1.5s or >30s), clipping (>1%)
2. Speaking Rate — reject abnormal speaking rates (<2 or >25 chars/sec)
3. Text Normalization — Unicode NFC normalization applied
4. Audio Corruption — reject empty, all-zeros, NaN/Inf, DC offset >0.1

Usage

from datasets import load_dataset

# Load a specific subset
ds = load_dataset(
    "PalakEngineerMaster/Processed_TTS_Multilingual_Data",
    data_dir="rasa",
    split="train",
)

# Access a sample
sample = ds[0]
print(sample["text"])
# audio is at sample["file_name"]

Audio Format

• Format: WAV
• Sample rate: 16kHz (Indic Voices_R, RASA) / 48kHz (Indic TTS IIT M)
• Channels: mono