---
license: apache-2.0
pretty_name: Chaashini
task_categories:
- text-to-speech
- automatic-speech-recognition
- audio-classification
language:
- hi
- en
- ne
- te
- mr
- ta
- as
- kn
- gu
- bn
- ml
- pa
- or
- brx
- mni
- mai
- kok
- sat
- sa
- doi
- sd
- bgc
- bho
- tcy
- lus
- hne
- raj
- mag
- grt
- ur
- kfy
- gbm
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
tags:
- speech
- indic
- indian-languages
- tts
- multilingual
- clean-speech
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*/*.parquet
- config_name: hi
  data_files:
  - split: train
    path: data/hi/*.parquet
- config_name: en
  data_files:
  - split: train
    path: data/en/*.parquet
- config_name: ne
  data_files:
  - split: train
    path: data/ne/*.parquet
- config_name: te
  data_files:
  - split: train
    path: data/te/*.parquet
- config_name: mr
  data_files:
  - split: train
    path: data/mr/*.parquet
- config_name: ta
  data_files:
  - split: train
    path: data/ta/*.parquet
- config_name: as
  data_files:
  - split: train
    path: data/as/*.parquet
- config_name: kn
  data_files:
  - split: train
    path: data/kn/*.parquet
- config_name: gu
  data_files:
  - split: train
    path: data/gu/*.parquet
- config_name: bn
  data_files:
  - split: train
    path: data/bn/*.parquet
- config_name: ml
  data_files:
  - split: train
    path: data/ml/*.parquet
- config_name: pa
  data_files:
  - split: train
    path: data/pa/*.parquet
- config_name: or
  data_files:
  - split: train
    path: data/or/*.parquet
- config_name: brx
  data_files:
  - split: train
    path: data/brx/*.parquet
- config_name: mni
  data_files:
  - split: train
    path: data/mni/*.parquet
- config_name: mai
  data_files:
  - split: train
    path: data/mai/*.parquet
- config_name: kok
  data_files:
  - split: train
    path: data/kok/*.parquet
- config_name: sat
  data_files:
  - split: train
    path: data/sat/*.parquet
- config_name: sa
  data_files:
  - split: train
    path: data/sa/*.parquet
- config_name: doi
  data_files:
  - split: train
    path: data/doi/*.parquet
- config_name: sd
  data_files:
  - split: train
    path: data/sd/*.parquet
- config_name: bgc
  data_files:
  - split: train
    path: data/bgc/*.parquet
- config_name: bho
  data_files:
  - split: train
    path: data/bho/*.parquet
- config_name: tcy
  data_files:
  - split: train
    path: data/tcy/*.parquet
- config_name: lus
  data_files:
  - split: train
    path: data/lus/*.parquet
- config_name: hne
  data_files:
  - split: train
    path: data/hne/*.parquet
- config_name: raj
  data_files:
  - split: train
    path: data/raj/*.parquet
- config_name: mag
  data_files:
  - split: train
    path: data/mag/*.parquet
- config_name: grt
  data_files:
  - split: train
    path: data/grt/*.parquet
- config_name: ur
  data_files:
  - split: train
    path: data/ur/*.parquet
- config_name: kfy
  data_files:
  - split: train
    path: data/kfy/*.parquet
- config_name: gbm
  data_files:
  - split: train
    path: data/gbm/*.parquet
---

# Chaashini (चाशनी)

**Chaashini** — Hindi/Urdu for *sugar syrup* — is a continuously growing corpus of **clean, single-speaker,
studio-grade Indian-language speech** built for training speech models (text-to-speech, speech
recognition, speech language models). Every clip in the corpus has passed a strict multi-stage
quality gate; the aim is purity over volume.

* **Total:** 877,646 clips · **1900.84 hours** · 32 languages
* **Format:** mono 24 kHz FLAC (`audio` column) with a verbatim transcript and rich per-clip metadata
* **Clip length:** 0.5 s – 30 s, cut at natural pauses
* **Last refreshed:** 2026-09-15 18:44 IST

The corpus grows automatically: new shards are appended every ~2 hours of newly accepted audio.

## Languages

| code | language | clips | hours | avg clip | avg quality (OVRL) |
|---|---|---:|---:|---:|---:|
| `hi` | Hindi | 646,478 | 1394.81 | 7.8 s | 3.25 |
| `en` | English | 112,027 | 240.61 | 7.7 s | 3.24 |
| `ne` | Nepali | 14,383 | 35.73 | 8.9 s | 3.27 |
| `te` | Telugu | 15,581 | 35.39 | 8.2 s | 3.24 |
| `mr` | Marathi | 16,528 | 33.94 | 7.4 s | 3.23 |
| `ta` | Tamil | 8,166 | 21.11 | 9.3 s | 3.22 |
| `as` | Assamese | 8,386 | 19.21 | 8.2 s | 3.24 |
| `kn` | Kannada | 6,724 | 18.68 | 10.0 s | 3.25 |
| `gu` | Gujarati | 8,843 | 17.97 | 7.3 s | 3.27 |
| `bn` | Bengali | 8,779 | 17.19 | 7.1 s | 3.25 |
| `ml` | Malayalam | 7,519 | 15.23 | 7.3 s | 3.22 |
| `pa` | Punjabi | 7,452 | 14.85 | 7.2 s | 3.23 |
| `or` | Odia | 5,867 | 13.83 | 8.5 s | 3.25 |
| `brx` | Bodo | 4,303 | 9.40 | 7.9 s | 3.26 |
| `mni` | Manipuri | 2,362 | 6.12 | 9.3 s | 3.24 |
| `mai` | Maithili | 775 | 1.94 | 9.0 s | 3.28 |
| `kok` | Konkani | 1,065 | 1.47 | 5.0 s | 3.25 |
| `sat` | Santali | 890 | 1.07 | 4.3 s | 3.16 |
| `sa` | Sanskrit | 927 | 1.03 | 4.0 s | 3.19 |
| `doi` | Dogri | 164 | 0.51 | 11.1 s | 3.25 |
| `sd` | Sindhi | 135 | 0.30 | 8.0 s | 3.17 |
| `bgc` | Haryanvi | 114 | 0.15 | 4.6 s | 3.20 |
| `bho` | Bhojpuri | 60 | 0.10 | 5.8 s | 3.19 |
| `tcy` | Tulu | 22 | 0.05 | 9.0 s | 3.11 |
| `lus` | Mizo | 37 | 0.05 | 5.1 s | 3.23 |
| `hne` | Chhattisgarhi | 38 | 0.05 | 4.6 s | 3.19 |
| `raj` | Rajasthani | 10 | 0.02 | 7.7 s | 3.19 |
| `mag` | Magahi | 3 | 0.01 | 14.0 s | 3.19 |
| `grt` | Garo | 3 | 0.00 | 5.6 s | 3.27 |
| `ur` | Urdu | 3 | 0.00 | 3.7 s | 3.35 |
| `kfy` | Kumaoni | 1 | 0.00 | 3.8 s | 3.06 |
| `gbm` | Garhwali | 1 | 0.00 | 2.2 s | 3.28 |

## What makes a clip "pristine"

Audio is sourced from publicly available spoken-word recordings (talks, interviews, narration,
lectures, podcasts and similar long-form speech). Each recording then passes through:

1. **Source-level screening** – recordings dominated by music, singing, or non-speech content are discarded whole.
2. **Speech activity detection** – frame-accurate speech/non-speech decisions; clips are cut only at genuine pauses.
3. **Speaker purity** – a neural diarizer labels speakers; every clip contains exactly **one** speaker and
   any region where voices overlap (plus a safety margin) is removed. Speaker labels are consistent within a source.
4. **Perceptual quality scoring** – each clip receives non-intrusive MOS-style scores for signal quality,
   background intrusiveness and overall quality, plus tagger probabilities for music / singing / noise,
   an SNR estimate, loudness, clipping and effective bandwidth. Clips outside strict thresholds are rejected;
   borderline clips are passed through a speech enhancer and **re-scored** (never accepted blindly) — such clips are flagged `enhanced=true`.
5. **Transcription** – a multilingual Indic ASR system transcribes each accepted clip; clips with implausible
   character rates (empty, hallucinated or clipped transcripts) or low recogniser confidence are rejected.
6. **Language identification** – script-aware identification on the transcript gives the language, a
   confidence, and the full **language composition** of the clip (code-mixing is common in Indian speech and is
   preserved, not filtered; `language` is the dominant language and `language_mix` holds the shares).

## Schema

| column | type | description |
|---|---|---|
| `id` | string | unique clip id |
| `audio` | Audio | mono 24 kHz FLAC |
| `text` | string | transcript (native script; borrowed English words may appear in Latin script) |
| `language` | string | dominant language (ISO 639 code) |
| `language_name` | string | human-readable language name |
| `language_confidence` | float | confidence of the language decision, 0–1 |
| `language_mix` | string (JSON) | share of each language/script in the clip, e.g. `{"hi": 0.82, "en": 0.18}` |
| `script` | string | dominant writing system of the transcript |
| `code_mixed` | bool | true if a secondary language exceeds 15 % of the tokens |
| `duration_s` | float | clip duration in seconds |
| `sample_rate` | int | 24000 |
| `speaker_id` | string | speaker label, consistent within a source recording |
| `source_id` | string | opaque, stable id of the source recording (for grouping / de-duplication) |
| `segment_index` | int | order of the clip within its source |
| `enhanced` | bool | clip was passed through speech enhancement before re-scoring |
| `dnsmos_sig` / `dnsmos_bak` / `dnsmos_ovrl` / `dnsmos_p808` | float | perceptual quality scores (1–5): signal, background, overall, P.808 MOS |
| `music_prob` / `speech_prob` / `noise_prob` | float | tagger probabilities (0–1) |
| `snr_db` | float | estimated signal-to-noise ratio |
| `rms_dbfs` / `peak_dbfs` | float | loudness and peak level |
| `clipping_ratio` | float | fraction of clipped samples |
| `bandwidth_hz` | float | effective audio bandwidth |
| `vad_speech_ratio` | float | fraction of the clip that is active speech |
| `speaker_dominance` | float | fraction of speaker-labelled frames belonging to the clip's speaker |
| `chars_per_sec` | float | transcript characters per second |
| `asr_confidence` | float | mean posterior of the recogniser's emitted tokens (0–1); low values flag hard audio |
| `genre` | string | coarse content genre of the source (talk, narration, interview, …) |
| `created_at` | string | ISO-8601 timestamp when the clip was accepted |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("kapturecx/Chaashini", "hi", split="train", streaming=True)   # one language
row = next(iter(ds))
print(row["text"], row["audio"]["sampling_rate"], row["language_mix"])

all_langs = load_dataset("kapturecx/Chaashini", "default", split="train", streaming=True)
```

Filtering tips: `dnsmos_ovrl >= 3.2` and `enhanced == False` gives the most conservative subset;
`language_confidence >= 0.8` and `code_mixed == False` gives monolingual clips.

## Licensing and intended use

The corpus is released under Apache-2.0 for research and commercial speech-technology development.
Clips are short excerpts of publicly available spoken-word material processed for machine learning;
no personal identifiers are stored beyond an opaque source id. If you believe content should be
removed, open a discussion on this repository.

## Citation

```
@misc{chaashini2026,
  title  = {Chaashini: a quality-gated multilingual Indian speech corpus},
  author = {Kapture CX},
  year   = {2026},
  url    = {https://huggingface.co/datasets/kapturecx/Chaashini}
}
```