---
license: other
language:
- hi
- en
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- audio
- speech
- tts
- indian-languages
- hindi
- indian-english
- emotion
pretty_name: Indian English + Hindi TTS Dataset (emotion-tagged)
size_categories:
- n<1K
---

# Indian English + Hindi TTS Dataset (emotion-tagged)

A curated, single-speaker-per-clip speech dataset for **Text-to-Speech** research,
covering **Indian English** and **Hindi**. Every clip is sourced from YouTube,
transcribed with **Sarvam Saaras v3**, and **emotion-tagged via acoustic cues + a Sarvam LLM**.

- **Total:** 82 clips, **55.6 minutes**
- **Hindi:** 28.8 min &nbsp;|&nbsp; **Indian English:** 26.8 min
- **Audio:** mono, 24 kHz, 16-bit WAV
- **Single speaker per clip**, clean (no background music / overlapping speakers)

## Schema
| field | description |
|---|---|
| `audio` | the waveform (24 kHz mono) |
| `transcript` | source-language transcript (Sarvam Saaras v3, `mode=transcribe`) |
| `emotion` | style/emotion label (acoustic cues + Sarvam LLM; 8-label taxonomy) |
| `language` | `hi-IN` or `en-IN` |
| `speaker` | source speaker |
| `speaker_gender` | speaker gender |
| `style` | content style |
| `duration` | seconds |
| `snr_db` | estimated SNR (QC) |
| `source_url` | original YouTube URL |
| `license` | per-clip source license |

## Emotion distribution
| emotion | hi | en | total |
|---|---|---|---|
| serious | 20 | 14 | 34 |
| neutral | 4 | 12 | 16 |
| calm | 9 | 4 | 13 |
| happy | 4 | 3 | 7 |
| excited | 1 | 4 | 5 |
| angry | 2 | 1 | 3 |
| sad | 2 | 0 | 2 |
| surprised | 0 | 2 | 2 |

## Speakers / sources
- **Pronabesh Das**
- **Raj Shamani podcast (diarized single speaker)**
- **Sandeep Maheshwari**

## How it was built
1. **Source vetting** — candidate YouTube channels were probed and rejected when they
   turned out to be AI two-host "audiobooks", laughter-heavy standup, or skits with
   background music. Only genuine, clean, single-human narration was kept.
2. **Download & segment** — `yt-dlp` + `ffmpeg silencedetect` cut audio at natural
   pauses into <=18s sub-clips.
3. **QC** — per-clip SNR / clipping / silence metrics auto-flag bad clips.
4. **Diarization** — Sarvam Batch API extracted a single speaker's turns from a
   2-speaker podcast.
5. **Transcription** — Saaras v3 (`mode=transcribe`, source language), kept clips only,
   cached (never billed twice).
6. **Merge** — consecutive clean sub-clips merged into ~30-50s clips by re-cutting one
   continuous span from the source; transcripts concatenated (no extra ASR).
7. **Emotion** — auto-tagged via acoustic energy/dynamics + a Sarvam LLM (8-label
   taxonomy). A custom **offline audition tool** (in the repo) supports human
   keep/reject + emotion refinement.
8. **Packaging** — attributed and published here.

See the project repository for the full pipeline, scripts, and an iteration log.

## Intended use & limitations
- Intended for **research / educational** TTS and ASR experimentation.
- Audio is sourced from YouTube; rights remain with the original creators
  (per-clip `license` + `source_url` are included for attribution).
- Emotion labels are **auto-generated** (acoustic cues + LLM) and may be imperfect;
  treat as weak supervision, refine with the audition tool if needed.