---
dataset_info:
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- config_name: cv22_central_asian
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configs:
- config_name: arabic_misc
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    path: arabic_misc/train-*
- config_name: cv22_african
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    path: cv22_african/train-*
- config_name: cv22_central_asian
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    path: cv22_central_asian/train-*
- config_name: cv22_de
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    path: cv22_de/train-*
- config_name: cv22_es
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    path: cv22_es/train-*
- config_name: cv22_european
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    path: cv22_european/train-*
- config_name: cv22_fr
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    path: cv22_fr/train-*
- config_name: cv22_mena
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    path: cv22_mena/train-*
- config_name: cv22_sea
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- config_name: cv22_sidon
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    path: cv22_sidon/train-*
- config_name: elise
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    path: elise/train-*
- config_name: elise_hindi
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    path: elise_hindi/train-*
- config_name: emodb_neucodec
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    path: emodb_neucodec/train-*
- config_name: expresso_neucodec
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    path: expresso_neucodec/train-*
- config_name: indictts
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    path: indictts/train-*
- config_name: ivr
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    path: ivr/train-*
- config_name: kathbath
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    path: kathbath/train-*
- config_name: maya_distill_neucodec
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    path: maya_distill_neucodec/train-*
- config_name: msft_indian
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    path: msft_indian/train-*
- config_name: nonverbal_tts
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    path: nonverbal_tts/train-*
- config_name: orpheus_distill_neucodec
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    path: orpheus_distill_neucodec/train-*
- config_name: rasa
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- config_name: shrutilipi
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- config_name: spicor
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    path: spicor/train-*
- config_name: synthetic_v1
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    path: synthetic_v1/train-*
- config_name: syspin
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    path: syspin/train-*
- config_name: uq_speech
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    path: uq_speech/train-*
- config_name: multilingual_tts
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    path: multilingual_tts/train-*
- config_name: hifi_tts
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    path: hifi_tts/train-*
- config_name: cml_tts_pl
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    path: cml_tts_pl/train-*
- config_name: cml_tts_pt
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    path: cml_tts_pt/train-*
- config_name: cml_tts_it
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    path: cml_tts_it/train-*
- config_name: cml_tts_fr
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    path: cml_tts_fr/train-*
- config_name: cml_tts_es
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    path: cml_tts_es/train-*
- config_name: cml_tts_nl
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    path: cml_tts_nl/train-*
- config_name: libritts_r
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    path: libritts_r/train-*
- config_name: cml_tts_de
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    path: cml_tts_de/train-*
- config_name: nsfw_tts_single
  data_files:
  - split: train
    path: nsfw_tts_single/train-*
license: cc-by-4.0
language:
- hi
- bn
- ta
- te
- mr
- gu
- kn
- ml
- pa
- or
- ur
- as
- sa
- en
- ne
- doi
- kok
- mai
- ug
- ar
- de
- fr
- es
- it
- nl
- tr
- ru
- pt
- pl
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- indic
- indian-languages
- tts
- speech
- audio
- multilingual
- uyghur
- arabic
- european-languages
size_categories:
- 10M<n<100M
---

# Indic TTS Unified v1

A large-scale, unified collection of speech data for text-to-speech (TTS) and speech research. This dataset consolidates 17 distinct source datasets into a single, schema-normalized resource covering Indian / South Asian languages, plus major European, African, MENA, and Central Asian languages, with over **13.7 million utterances** and **26,000+ hours** of audio.

All audio is resampled to **24 kHz mono**. Every row follows an identical schema regardless of source, enabling seamless multi-dataset training without per-source preprocessing.

---

## Dataset Summary

| Statistic | Value |
|-----------|-------|
| Total utterances | 13,777,541 |
| Total audio duration | 26,300+ hours |
| Languages covered | 58+ |
| Audio format | 24 kHz, mono, float32 |
| Configs (subsets) | 26 |

---

## Subsets

| Config | Rows | Hours | Speakers | Languages | Source Dataset |
|--------|-----:|------:|---------:|:---------:|---------------|
| `orpheus_distill_neucodec` | 400 | ~2 | -- | 1 | [BarryFutureman/orpheus-distill-neucodec](https://huggingface.co/datasets/BarryFutureman/orpheus-distill-neucodec) |
| `maya_distill_neucodec` | 14,000 | 33.6 | 12,801 | 1 | [BarryFutureman/maya-distill-data-neucodec](https://huggingface.co/datasets/BarryFutureman/maya-distill-data-neucodec) |
| `emodb_neucodec` | 22,043 | 40.3 | 5 | 1 | [BarryFutureman/EmoDB-neucodec](https://huggingface.co/datasets/BarryFutureman/EmoDB-neucodec) |
| `expresso_neucodec` | 11,599 | 10.9 | 4 | 1 | [BarryFutureman/expresso-neucodec](https://huggingface.co/datasets/BarryFutureman/expresso-neucodec) |
| `nonverbal_tts` | 6,222 | 17.6 | 2,296 | 1 | [deepvk/NonverbalTTS](https://huggingface.co/datasets/deepvk/NonverbalTTS) |
| `elise` | 1,194 | 2.6 | 1 | 1 | [MrDragonFox/Elise](https://huggingface.co/datasets/MrDragonFox/Elise) |
| `elise_hindi` | 1,147 | 2.4 | 1 | 1 | [ronith09/Elise-Hindi](https://huggingface.co/datasets/ronith09/Elise-Hindi) |
| `synthetic_v1` | 10,759 | 22.8 | 50 | 9 | [kenpath/tts-synthetic-v1](https://huggingface.co/datasets/kenpath/tts-synthetic-v1) |
| `spicor` | 50,468 | 99.4 | 2 | 1 | [kenpath/tts-SPICOR](https://huggingface.co/datasets/kenpath/tts-SPICOR) |
| `indictts` | 294,008 | 527.0 | 151,247 | 14 | [SPRINGLab/IndicTTS](https://huggingface.co/SPRINGLab) (14 datasets) |
| `msft_indian` | 115,392 | 134.9 | 115,390 | 3 | [deepdml/microsoft-speech-corpus-indian](https://huggingface.co/datasets/deepdml/microsoft-speech-corpus-indian) |
| `syspin` | 786,625 | 1,706.5 | 18 | 9 | [kenpath/tts-SYSPIN](https://huggingface.co/datasets/kenpath/tts-SYSPIN) |
| `ivr` | 664,208 | 1,656.5 | 10,152 | 22 | [ai4bharat/indicvoices_r](https://huggingface.co/datasets/ai4bharat/indicvoices_r) |
| `rasa` | 582,195 | 1,035.5 | 40 | 22 | [ai4bharat/Rasa](https://huggingface.co/datasets/ai4bharat/Rasa) |
| `kathbath` | 805,721 | 1,475.2 | 985 | 12 | [ai4bharat/Kathbath](https://huggingface.co/datasets/ai4bharat/Kathbath) |
| `shrutilipi` | 2,226,753 | 4,665.0 | -- | 16 | [ai4bharat/Shrutilipi](https://huggingface.co/datasets/ai4bharat/Shrutilipi) |
| `cv22_sidon` | 3,212,858 | 4,614.2 | -- | 17 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) |
| `cv22_african` | 725,125 | ~1,500 | -- | 6 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (African subset: sw, lg, ha, yo, ig, am) |
| `cv22_central_asian` | 404,288 | ~800 | -- | 4 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (Central Asian subset: uz, ka, az, kk) |
| `cv22_mena` | 194,077 | ~370 | -- | 2 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (MENA subset: ar, fa) |
| `cv22_de` | 699,462 | ~1,450 | -- | 1 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (German) |
| `cv22_fr` | 700,202 | ~1,450 | -- | 1 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (French) |
| `cv22_es` | 1,592,537 | ~3,300 | -- | 1 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (Spanish) |
| `cv22_european` | 591,663 | ~1,200 | -- | 6 | [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon) (European subset: it, nl, tr, ru, pt, pl) |
| `arabic_misc` | 49,412 | 122.7 | 39,898 | 1 | Mixed: MohamedRashad, Nourhann, NeoBoy, saleh1312, KejueAI |
| `uq_speech` | 16,183 | 28.0 | 16,183 | 1 | [ixxan/mms-tts-uig-script_arabic-UQSpeech](https://huggingface.co/datasets/ixxan/mms-tts-uig-script_arabic-UQSpeech) |
| `libritts_r` | 358,000 | 585.0 | 2,456 | 1 | [parler-tts/libritts_r_filtered](https://huggingface.co/datasets/parler-tts/libritts_r_filtered) |
| **Total** | **14,135,541** | **26,885+** | | **58+** | |

---

## Schema

All configs share the same column schema:

| Column | Type | Description |
|--------|------|-------------|
| `audio` | `Audio` (24 kHz) | Audio waveform, resampled to 24 kHz mono |
| `text` | `string` | Transcript text. Rasa transcripts may include emotion tags (see below) |
| `speaker_id` | `string` | Speaker identifier (see Speaker ID Policy below) |
| `source` | `string` | Name of the originating dataset (e.g., `"kathbath"`, `"rasa"`) |
| `language` | `string` | Full language name (e.g., `"Hindi"`, `"Bengali"`, `"Tamil"`) |
| `gender` | `string` | `"Male"`, `"Female"`, or `"Unknown"` |
| `duration` | `float64` | Audio duration in seconds |

### Speaker ID Policy

Speaker identification varies by source dataset:

- **Deterministic 8-character hash**: For datasets that provide speaker metadata (`kathbath`, `syspin`, `ivr`, `rasa`, `spicor`, `synthetic_v1`, `cv22_sidon`, `cv22_african`, `cv22_central_asian`, `cv22_mena`, `cv22_de`, `cv22_es`, `cv22_fr`, `cv22_european`), the `speaker_id` is a deterministic hash derived from the original speaker label, ensuring consistency across rows from the same speaker.
- **Random UUID**: For datasets without reliable speaker metadata (`shrutilipi`, `msft_indian`, `indictts`), each row receives a unique random UUID. These should not be used for speaker-level grouping.

### Duration

Duration values are **unfiltered** -- no minimum or maximum duration threshold (such as 0.5--60s) has been applied. Downstream consumers should apply their own filtering as needed.

### Emotion Tags (Rasa)

The `rasa` config contains expressive/emotional speech. Transcript text in this subset may include inline emotion tags such as `<happy>`, `<sad>`, `<angry>`, `<surprise>`, `<fear>`, `<disgust>`, and `<neutral>`. These tags indicate the intended emotion of the utterance and can be used for emotion-conditioned TTS training.

---

## Language Coverage

The dataset spans a broad range of Indian languages. The table below lists languages and the configs in which they appear:

| Language | Configs |
|----------|---------|
| Assamese | shrutilipi, ivr, rasa, cv22_sidon |
| Bengali | kathbath, shrutilipi, ivr, rasa, syspin, cv22_sidon |
| Bodo | ivr, rasa |
| Dhivehi | cv22_sidon |
| Dogri | shrutilipi, ivr, rasa |
| Dutch | cv22_european |
| English (Indian) | spicor, ivr, rasa, indictts |
| Arabic | arabic_misc, cv22_mena |
| French | cv22_fr |
| German | cv22_de |
| Italian | cv22_european |
| Polish | cv22_european |
| Portuguese | cv22_european |
| Russian | cv22_european |
| Spanish | cv22_es |
| Turkish | cv22_european |
| Uyghur | uq_speech |
| English (Common Voice) | cv22_sidon |
| Gujarati | kathbath, shrutilipi, ivr, rasa, syspin, indictts |
| Hindi | kathbath, shrutilipi, ivr, rasa, syspin, msft_indian, indictts, synthetic_v1, cv22_sidon |
| Kannada | kathbath, shrutilipi, ivr, rasa, syspin, indictts |
| Kashmiri | ivr |
| Konkani | shrutilipi, ivr, rasa |
| Maithili | shrutilipi, ivr, rasa |
| Malayalam | kathbath, shrutilipi, ivr, rasa, syspin, indictts, cv22_sidon |
| Manipuri | ivr, rasa |
| Marathi | kathbath, shrutilipi, ivr, rasa, syspin, indictts, cv22_sidon |
| Nepali | shrutilipi, ivr, rasa, cv22_sidon |
| Odia | kathbath, shrutilipi, ivr, rasa, syspin, indictts, cv22_sidon |
| Pashto | cv22_sidon |
| Punjabi | kathbath, shrutilipi, ivr, rasa, syspin, indictts, cv22_sidon |
| Rajasthani | indictts |
| Sanskrit | kathbath, shrutilipi, ivr, rasa |
| Santali | ivr, cv22_sidon |
| Saraiki | cv22_sidon |
| Sindhi | ivr, cv22_sidon |
| Tamil | kathbath, shrutilipi, ivr, rasa, syspin, msft_indian, indictts, cv22_sidon |
| Telugu | kathbath, shrutilipi, ivr, rasa, syspin, msft_indian, indictts, cv22_sidon |
| Urdu | kathbath, shrutilipi, ivr, rasa, cv22_sidon |

---

## Detailed Subset Descriptions

### orpheus_distill_neucodec

Decoded from [BarryFutureman/orpheus-distill-neucodec](https://huggingface.co/datasets/BarryFutureman/orpheus-distill-neucodec), an Orpheus distillation dataset stored as NeuCodec tokens. Contains 400 English utterances (~2 hours) with emotion conditioning. Text includes emotion wrapper tags (e.g., `<happy>...</happy>`) and converted vocal expression tags (e.g., `<sigh>`, `<laugh>`). Speaker IDs are random 8-character hex values (no speaker metadata in source).

### maya_distill_neucodec

Decoded from [BarryFutureman/maya-distill-data-neucodec](https://huggingface.co/datasets/BarryFutureman/maya-distill-data-neucodec), a Maya distillation dataset stored as NeuCodec tokens. Contains 14,000 English utterances (33.6 hours) with emotion conditioning and rich voice metadata. Text includes emotion wrapper tags (e.g., `<happy>...</happy>`) and vocal expression tags (e.g., `<giggle>`, `<sigh>`, `<yawn>`). Speaker IDs are deterministic 8-character hashes derived from `voice_description`, yielding 12,801 unique speakers. Gender breakdown: Male 4,602, Female 4,681, Unknown 4,717.

### emodb_neucodec

Decoded from [BarryFutureman/EmoDB-neucodec](https://huggingface.co/datasets/BarryFutureman/EmoDB-neucodec), a synthetic emotional speech dataset with GPT-4o-generated English text and NeuCodec-encoded audio. Contains 22,043 utterances (40.3 hours) after deduplication, with 5 speakers and 7 emotion styles (angry, happy, sad, fearful, surprised, disgusted, neutral). Text includes emotion wrapper tags (e.g., `<angry>...</angry>`). Speaker IDs are deterministic 8-character hashes of the speaker name. All gender values are `"Unknown"`.

### expresso_neucodec

Decoded from [BarryFutureman/expresso-neucodec](https://huggingface.co/datasets/BarryFutureman/expresso-neucodec), the Expresso corpus encoded as NeuCodec tokens. Contains 11,599 English utterances (10.9 hours) after deduplication, with 4 speakers and multiple expressive styles. Text includes style wrapper tags (e.g., `<confused>...</confused>`). Speaker IDs are deterministic 8-character hashes of the original speaker ID (e.g., `ex01`). All gender values are `"Unknown"`.

### nonverbal_tts

Sourced from [deepvk/NonverbalTTS](https://huggingface.co/datasets/deepvk/NonverbalTTS), a nonverbal-annotated speech dataset combining Expresso and VoxCeleb data. Contains 6,222 English utterances (17.6 hours) with 2,296 unique speakers. Text uses the annotated `Result` column which includes emoji markers for nonverbal cues (e.g., 🌬️ for breath, 😤 for exhale). Emotion wrapping applied only for `happy` and `sad` categories. Gender breakdown: Male 3,872, Female 2,350.

### elise

Sourced from [MrDragonFox/Elise](https://huggingface.co/datasets/MrDragonFox/Elise), a single-speaker English female dataset. Contains 1,194 utterances (2.6 hours). Audio resampled from 22050 Hz to 24 kHz. Text passed through as-is (includes emotion expression tags).

### elise_hindi

Sourced from [ronith09/Elise-Hindi](https://huggingface.co/datasets/ronith09/Elise-Hindi), a Hindi version of the Elise dataset with the same speaker. Contains 1,147 utterances (2.4 hours). Audio resampled from 22050 Hz to 24 kHz.

### synthetic_v1

Synthetic TTS data generated for bootstrapping and augmentation. Covers 9 languages (primarily Hindi) with 50 distinct synthetic voices. 10,759 utterances totaling 22.8 hours.

### spicor

The SpiCor corpus of Indian English read speech. Contains 50,468 utterances (99.4 hours) from 2 speakers. Useful for high-quality single-speaker or few-speaker English TTS.

### indictts

Derived from the [SPRINGLab/IndicTTS](https://huggingface.co/SPRINGLab) collection, which spans 14 individual language datasets. Contains 294,008 utterances (527.0 hours) across 14 Indian languages. Speaker IDs are random UUIDs (no original speaker metadata available).

### msft_indian

Sourced from [deepdml/microsoft-speech-corpus-indian](https://huggingface.co/datasets/deepdml/microsoft-speech-corpus-indian). Covers 3 languages (Hindi, Tamil, Telugu) with 115,392 utterances (134.9 hours). Speaker IDs are random UUIDs.

### syspin

The [SYSPIN TTS dataset](https://huggingface.co/datasets/kenpath/tts-SYSPIN) provides high-quality studio-recorded speech across 9 languages from 18 speakers. With 786,625 utterances and 1,706.5 hours, this is one of the largest single-source contributions. Well-suited for single-speaker and multi-speaker TTS due to consistent recording conditions.

### ivr

Derived from [ai4bharat/indicvoices_r](https://huggingface.co/datasets/ai4bharat/indicvoices_r) (IndicVoices-R), a large-scale read speech corpus. Covers 22 languages with 664,208 utterances (1,656.5 hours) from 10,152 speakers. One of the most linguistically diverse configs in this collection.

### rasa

The [ai4bharat/Rasa](https://huggingface.co/datasets/ai4bharat/Rasa) dataset of expressive and emotional Indian language speech. Covers 22 languages with 582,195 utterances (1,035.5 hours) from 40 speakers. Transcripts include inline emotion tags (e.g., `<happy>`, `<sad>`, `<angry>`) that indicate the expressed emotion, making this subset uniquely valuable for emotion-conditioned TTS.

### kathbath

Sourced from [ai4bharat/Kathbath](https://huggingface.co/datasets/ai4bharat/Kathbath), a read speech dataset covering 12 Indian languages. Contains 805,721 utterances (1,475.2 hours) from 985 speakers.

**Language breakdown by hours:**

| Language | Hours |
|----------|------:|
| Tamil | 176.9 |
| Marathi | 152.0 |
| Kannada | 150.3 |
| Telugu | 146.7 |
| Hindi | 139.6 |
| Malayalam | 139.1 |
| Punjabi | 128.4 |
| Gujarati | 113.4 |
| Bengali | 88.0 |
| Odia | 81.8 |
| Sanskrit | 80.4 |
| Urdu | 78.6 |

**Gender breakdown:** Female 982.7h, Male 492.5h

### cv22_sidon

Sourced from [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon), a SIDON-processed variant of Mozilla Common Voice 22.0. A curated selection of 17 South Asian / Indic language configs is included, covering all splits (train, validation, test, other, invalidated) merged into a single `train` split per config. Contains 3,212,858 utterances totaling 4,614.2 hours.

Speaker IDs are deterministic 8-character SHA256 hashes of the original Common Voice `client_id` (preserves speaker grouping across utterances while anonymizing).

**Language breakdown:**

| Language | Code | Rows | Hours |
|----------|:----:|-----:|------:|
| English | en | 1,687,562 | 2,670.8 |
| Bengali | bn | 957,937 | 1,129.8 |
| Tamil | ta | 181,715 | 314.2 |
| Urdu | ur | 201,883 | 244.8 |
| Pashto | ps | 57,167 | 79.3 |
| Odia | or | 23,329 | 36.5 |
| Dhivehi | dv | 23,875 | 33.3 |
| Sindhi | sd | 25,011 | 29.1 |
| Hindi | hi | 16,250 | 22.7 |
| Marathi | mr | 10,836 | 19.1 |
| Malayalam | ml | 9,121 | 10.7 |
| Assamese | as | 4,656 | 7.6 |
| Saraiki | skr | 5,825 | 6.7 |
| Punjabi | pa-IN | 3,136 | 4.2 |
| Telugu | te | 2,290 | 2.6 |
| Nepali | ne-NP | 1,416 | 1.6 |
| Santali | sat | 849 | 1.1 |

Processing pipeline: raw Common Voice audio (typically MP3 at 32--48 kHz) was decoded, downmixed to mono, and resampled to 24 kHz using high-quality resampling. All splits per language were concatenated. Gender values are mapped from the original `gender` field (`male_masculine` → `Male`, `female_feminine` → `Female`, otherwise `Unknown`).

### cv22_de

German (`de`) Common Voice 22, sourced from [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon). Contains 699,462 utterances (~1,450 hours). Same processing pipeline as `cv22_sidon`. Speaker IDs are deterministic 8-character SHA256 hashes of the original Common Voice `client_id`.

### cv22_fr

French (`fr`) Common Voice 22, sourced from [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon). Contains 700,202 utterances (~1,450 hours). Same processing pipeline as `cv22_sidon`. Speaker IDs are deterministic 8-character SHA256 hashes of the original Common Voice `client_id`.

### cv22_es

Spanish (`es`) Common Voice 22, sourced from [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon). Contains 1,592,537 utterances (~3,300 hours) across 320 train shards. Same processing pipeline as `cv22_sidon`. Speaker IDs are deterministic 8-character SHA256 hashes of the original Common Voice `client_id`.

### cv22_european

A combined config of mid-size European Common Voice 22 languages, sourced from [sarulab-speech/commonvoice22_sidon](https://huggingface.co/datasets/sarulab-speech/commonvoice22_sidon). Contains 591,663 utterances (~1,200 hours) across 6 languages: Italian (`it`), Dutch (`nl`), Turkish (`tr`), Russian (`ru`), Portuguese (`pt`), Polish (`pl`). Same processing pipeline as `cv22_sidon`. Speaker IDs are deterministic 8-character SHA256 hashes of the original Common Voice `client_id`.

### shrutilipi

The largest config, sourced from [ai4bharat/Shrutilipi](https://huggingface.co/datasets/ai4bharat/Shrutilipi). Contains 2,226,753 utterances (4,665.0 hours) across 16 languages: Assamese, Bengali, Dogri, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Marathi, Nepali, Odia, Punjabi, Sanskrit, Tamil, and Telugu. No speaker metadata is available -- each row has a unique UUID as `speaker_id`, and all `gender` values are `"Unknown"`.

---

## Usage

### Load a specific config

```python
from datasets import load_dataset

ds = load_dataset("kenpath/indic-tts-unified-v1", "kathbath", split="train")
print(ds[0])
# {'audio': {'path': ..., 'array': array([...]), 'sampling_rate': 24000},
#  'text': '...', 'speaker_id': 'a1b2c3d4', 'source': 'kathbath',
#  'language': 'Tamil', 'gender': 'Female', 'duration': 5.32}
```

### Streaming mode (recommended for large configs)

```python
from datasets import load_dataset

ds = load_dataset(
    "kenpath/indic-tts-unified-v1", "shrutilipi",
    split="train", streaming=True
)

for example in ds:
    audio_array = example["audio"]["array"]
    text = example["text"]
    # Process as needed
    break
```

### Filter by language

```python
from datasets import load_dataset

ds = load_dataset(
    "kenpath/indic-tts-unified-v1", "ivr",
    split="train", streaming=True
)

hindi_ds = ds.filter(lambda x: x["language"] == "Hindi")

for example in hindi_ds:
    print(example["text"])
    break
```

### Load multiple configs

```python
from datasets import load_dataset, concatenate_datasets

configs = ["kathbath", "syspin", "rasa"]
datasets = []
for config in configs:
    ds = load_dataset(
        "kenpath/indic-tts-unified-v1", config, split="train"
    )
    datasets.append(ds)

combined = concatenate_datasets(datasets)
print(f"Combined: {len(combined)} rows")
```

### Duration filtering

```python
from datasets import load_dataset

ds = load_dataset(
    "kenpath/indic-tts-unified-v1", "syspin",
    split="train", streaming=True
)

# Keep only utterances between 1 and 30 seconds
filtered = ds.filter(lambda x: 1.0 <= x["duration"] <= 30.0)
```

---

## Data Processing

The following normalization steps were applied uniformly across all source datasets during construction:

1. **Audio resampling**: All audio resampled to 24 kHz mono using high-quality resampling.
2. **Schema alignment**: Every source dataset was mapped to the unified 7-column schema described above.
3. **Speaker hashing**: Where speaker labels were available, they were converted to deterministic 8-character hashes for privacy and consistency. Where unavailable, random UUIDs were assigned.
4. **Split merging**: Train and test splits from source datasets were combined into a single `train` split per config.

---

## Intended Use

This dataset is designed for:

- **Text-to-speech (TTS)** model training across Indian languages
- **Automatic speech recognition (ASR)** pretraining and fine-tuning
- **Speaker verification** and **speaker embedding** research (for configs with reliable speaker IDs)
- **Multilingual and cross-lingual** speech research
- **Emotion-conditioned speech synthesis** (using the `rasa` config)

---

## Limitations

- **Speaker IDs for shrutilipi, msft_indian, and indictts** are random UUIDs and do not represent actual speaker groupings. Do not use these for speaker-level analysis.
- **Gender metadata** is `"Unknown"` for the entire `shrutilipi` config and may be incomplete in other configs.
- **Duration is unfiltered**. Some utterances may be very short (sub-second) or very long. Apply duration filtering for TTS training.
- **Text quality** varies across sources. Some transcripts may contain noise, transliteration inconsistencies, or incomplete sentences.
- **Emotion tags in Rasa** are embedded in the transcript text and need to be parsed or stripped depending on the downstream task.

---

## Citation

If you use this dataset, please cite the original source datasets as appropriate:

- **IndicTTS**: SPRINGLab/IndicTTS
- **Kathbath**: ai4bharat/Kathbath
- **SYSPIN**: kenpath/tts-SYSPIN
- **IndicVoices-R**: ai4bharat/indicvoices_r
- **Rasa**: ai4bharat/Rasa
- **Shrutilipi**: ai4bharat/Shrutilipi
- **Microsoft Speech Corpus Indian**: deepdml/microsoft-speech-corpus-indian
- **SpiCor**: kenpath/tts-SPICOR
- **Common Voice 22 (SIDON)**: sarulab-speech/commonvoice22_sidon (derived from Mozilla Common Voice Corpus 22.0, CC-0)

---

## License

Please refer to the individual source dataset licenses. This unified collection is provided under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) for the aggregation and schema normalization work. The underlying audio and text data retain the licenses of their respective sources.