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---
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
```python
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
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