README.md

metadata

dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: text
      dtype: string
    - name: lang
      dtype: string
    - name: emotion
      dtype: string
    - name: duration
      dtype: float64
    - name: utt_id
      dtype: string
    - name: speaker_id
      dtype: string
    - name: source
      dtype: string
    - name: snr_db
      dtype: float64
    - name: silence_ratio
      dtype: float64
    - name: clipping_ratio
      dtype: float64
    - name: domain
      dtype: string
configs:
  - config_name: assamese
    data_files:
      - split: train
        path: assamese/train-*.parquet
      - split: validation
        path: assamese/validation-*.parquet
  - config_name: bengali
    data_files:
      - split: train
        path: bengali/train-*.parquet
      - split: validation
        path: bengali/validation-*.parquet
  - config_name: english
    data_files:
      - split: train
        path: english/train-*.parquet
      - split: validation
        path: english/validation-*.parquet
  - config_name: gujarati
    data_files:
      - split: train
        path: gujarati/train-*.parquet
      - split: validation
        path: gujarati/validation-*.parquet
  - config_name: hindi
    data_files:
      - split: train
        path: hindi/train-*.parquet
      - split: validation
        path: hindi/validation-*.parquet
  - config_name: kannada
    data_files:
      - split: train
        path: kannada/train-*.parquet
      - split: validation
        path: kannada/validation-*.parquet
  - config_name: malayalam
    data_files:
      - split: train
        path: malayalam/train-*.parquet
      - split: validation
        path: malayalam/validation-*.parquet
  - config_name: marathi
    data_files:
      - split: train
        path: marathi/train-*.parquet
      - split: validation
        path: marathi/validation-*.parquet
  - config_name: nepali
    data_files:
      - split: train
        path: nepali/train-*.parquet
      - split: validation
        path: nepali/validation-*.parquet
  - config_name: odia
    data_files:
      - split: train
        path: odia/train-*.parquet
      - split: validation
        path: odia/validation-*.parquet
  - config_name: punjabi
    data_files:
      - split: train
        path: punjabi/train-*.parquet
      - split: validation
        path: punjabi/validation-*.parquet
  - config_name: tamil
    data_files:
      - split: train
        path: tamil/train-*.parquet
      - split: validation
        path: tamil/validation-*.parquet
  - config_name: telugu
    data_files:
      - split: train
        path: telugu/train-*.parquet
      - split: validation
        path: telugu/validation-*.parquet

Indic Multilingual TTS Dataset

Large-scale multilingual text-to-speech dataset covering 13 Indian languages with 453,823 utterances. Audio is embedded in Parquet files and playable directly in the dataset viewer.

Languages

Language	Code	Config	Train	Validation
Assamese	as	assamese	52,251	500
Bengali	bn	bengali	44,637	500
English	en	english	12,209	500
Gujarati	gu	gujarati	16,157	500
Hindi	hi	hindi	24,211	500
Kannada	kn	kannada	32,263	500
Malayalam	ml	malayalam	44,381	499
Marathi	mr	marathi	28,161	499
Nepali	ne	nepali	41,320	500
Odia	or	odia	26,468	500
Punjabi	pa	punjabi	28,598	500
Tamil	ta	tamil	53,075	499
Telugu	te	telugu	43,595	500
Total			447,326	6,497

Usage

from datasets import load_dataset

# Load a specific language
ds = load_dataset("PalakEngineerMaster/Validated_data_TTS", "hindi")

# Access audio
sample = ds["train"][0]
print(sample["audio"])   # {"array": ndarray, "sampling_rate": 16000}
print(sample["text"])    # transcription

Columns

• audio: Audio waveform (WAV, 16kHz mono) — playable in viewer
• text: Transcription
• lang: Language code
• emotion: Emotion label
• duration: Duration in seconds
• utt_id / speaker_id: Utterance and speaker identifiers
• source: Data source (rasa, indicvoices_r, indictts)
• snr_db: Signal-to-noise ratio (dB)
• silence_ratio / clipping_ratio: Audio quality metrics
• domain: Domain category

Sources

• IndicVoices-R
• RASA
• IndicTTS