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TTS-Clean44k / README.md
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labeled44k: add speaker column (100% coverage, 206102 speakers); shards rebuilt with 512-row row groups for the viewer
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---
dataset_info:
- config_name: aishell3
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
 - name: train
 num_bytes: 1434674764.444
 num_examples: 2814
 download_size: 1298956633
 dataset_size: 1434674764.444
- config_name: argentinian_es
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_bytes: 2401506764.784
 num_examples: 4362
 download_size: 1953967499
 dataset_size: 2401506764.784
- config_name: basque
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
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 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_bytes: 4510039899.312
 num_examples: 6907
 download_size: 3694399344
 dataset_size: 4510039899.312
- config_name: burmese
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_examples: 2071
 download_size: 1030489997
 dataset_size: 1263153045.248
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 features:
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 dtype:
 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_examples: 4018
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 dataset_size: 3304304924.488
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 features:
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 audio:
 sampling_rate: 48000
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 dtype: float32
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 dtype: float32
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 dtype: float32
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 features:
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 dataset_size: 3476292735.872
- config_name: gujarati
 features:
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 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 dtype: float32
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 dtype: float32
 splits:
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 num_examples: 3789
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- config_name: hifitts
 features:
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 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
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 dtype: float32
 - name: ovrl
 dtype: float32
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 splits:
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 num_examples: 19447
 download_size: 10167653025
 dataset_size: 10105661759.938
- config_name: hui_german
 features:
 - name: audio
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 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
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 dtype: float32
 - name: ovrl
 dtype: float32
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 splits:
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 num_examples: 28359
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 dataset_size: 24422866511.75
- config_name: javanese
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
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 - name: ovrl
 dtype: float32
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 splits:
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 num_examples: 2906
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 dataset_size: 1455115751.828
- config_name: kannada
 features:
 - name: audio
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 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
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 dtype: float32
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 features:
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 audio:
 sampling_rate: 48000
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 dtype: float32
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 dtype: float32
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 dtype: float32
 splits:
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 num_examples: 2098
 download_size: 856044100
 dataset_size: 1109767557.416
- config_name: kss_korean
 features:
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 audio:
 sampling_rate: 48000
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 dtype: float32
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 splits:
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 num_examples: 3994
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 features:
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 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 dtype: float32
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 dtype: float32
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 dtype: float32
 splits:
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 num_examples: 2470
 download_size: 1197392249
 dataset_size: 1492333104.46
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 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 splits:
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 num_examples: 1400
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- config_name: nepali
 features:
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 audio:
 sampling_rate: 48000
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 dtype: float32
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 dtype: float32
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 dtype: float32
 splits:
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 num_examples: 1157
 download_size: 669613252
 dataset_size: 693370714.84
- config_name: nigerian_en
 features:
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 audio:
 sampling_rate: 48000
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 num_examples: 2687
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 dataset_size: 1774719446.76
- config_name: peruvian_es
 features:
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 audio:
 sampling_rate: 48000
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 dtype: float32
 splits:
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 num_examples: 4991
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 dataset_size: 2985297200.36
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 features:
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 audio:
 sampling_rate: 48000
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 splits:
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 num_examples: 516
 download_size: 244476478
 dataset_size: 312198021.0
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 features:
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 audio:
 sampling_rate: 48000
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 splits:
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 num_examples: 241
 download_size: 64632225
 dataset_size: 98138536.0
- config_name: south_african
 features:
 - name: audio
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 audio:
 sampling_rate: 48000
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 dtype: string
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 dtype: float32
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 dtype: float32
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 dtype: float32
 splits:
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 num_bytes: 2609797704.28
 num_examples: 5240
 download_size: 2595787393
 dataset_size: 2609797704.28
- config_name: sundanese
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
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 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_bytes: 1222348478.408
 num_examples: 2116
 download_size: 1182371107
 dataset_size: 1222348478.408
- config_name: tamil
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
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 num_bytes: 2328867763.712
 num_examples: 3633
 download_size: 1620299433
 dataset_size: 2328867763.712
- config_name: telugu
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
 - name: train
 num_bytes: 1280168624.328
 num_examples: 2517
 download_size: 888160549
 dataset_size: 1280168624.328
- config_name: uk_ireland_en
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
 - name: train
 num_bytes: 9282270221.4
 num_examples: 14925
 download_size: 8067467815
 dataset_size: 9282270221.4
- config_name: venezuelan_es
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
 - name: train
 num_bytes: 1452261660.888
 num_examples: 2466
 download_size: 1080960605
 dataset_size: 1452261660.888
- config_name: yoruba
 features:
 - name: audio
 dtype:
 audio:
 sampling_rate: 48000
 - name: source
 dtype: string
 - name: bak
 dtype: float32
 - name: sig
 dtype: float32
 - name: ovrl
 dtype: float32
 - name: duration
 dtype: float32
 splits:
 - name: train
 num_bytes: 752974618.2
 num_examples: 1464
 download_size: 611970946
 dataset_size: 752974618.2
- config_name: labeled44k
 features:
 - name: audio
 dtype: audio
 - name: source
 dtype: string
 - name: duration
 dtype: float32
 - name: sr
 dtype: int32
 - name: lang
 dtype: string
 - name: speaker
 dtype: string
 - name: text
 dtype: string
 splits:
 - name: train
 num_examples: 2870194
 download_size: 150319828130
configs:
- config_name: aishell3
 data_files:
 - split: train
 path: aishell3/train-*
- config_name: argentinian_es
 data_files:
 - split: train
 path: argentinian_es/train-*
- config_name: basque
 data_files:
 - split: train
 path: basque/train-*
- config_name: burmese
 data_files:
 - split: train
 path: burmese/train-*
- config_name: catalan
 data_files:
 - split: train
 path: catalan/train-*
- config_name: chilean_es
 data_files:
 - split: train
 path: chilean_es/train-*
- config_name: colombian_es
 data_files:
 - split: train
 path: colombian_es/train-*
- config_name: cv_ta
 data_files:
 - split: train
 path: cv_ta/train-*
- config_name: cv_zhcn
 data_files:
 - split: train
 path: cv_zhcn/train-*
- config_name: daps
 data_files:
 - split: train
 path: daps/train-*
- config_name: galician
 data_files:
 - split: train
 path: galician/train-*
- config_name: gujarati
 data_files:
 - split: train
 path: gujarati/train-*
- config_name: hifitts
 data_files:
 - split: train
 path: hifitts/train-*
- config_name: hui_german
 data_files:
 - split: train
 path: hui_german/train-*
- config_name: javanese
 data_files:
 - split: train
 path: javanese/train-*
- config_name: kannada
 data_files:
 - split: train
 path: kannada/train-*
- config_name: khmer
 data_files:
 - split: train
 path: khmer/train-*
- config_name: kss_korean
 data_files:
 - split: train
 path: kss_korean/train-*
- config_name: malayalam
 data_files:
 - split: train
 path: malayalam/train-*
- config_name: marathi
 data_files:
 - split: train
 path: marathi/train-*
- config_name: nepali
 data_files:
 - split: train
 path: nepali/train-*
- config_name: nigerian_en
 data_files:
 - split: train
 path: nigerian_en/train-*
- config_name: peruvian_es
 data_files:
 - split: train
 path: peruvian_es/train-*
- config_name: puertorico_es
 data_files:
 - split: train
 path: puertorico_es/train-*
- config_name: ravdess
 data_files:
 - split: train
 path: ravdess/train-*
- config_name: south_african
 data_files:
 - split: train
 path: south_african/train-*
- config_name: sundanese
 data_files:
 - split: train
 path: sundanese/train-*
- config_name: tamil
 data_files:
 - split: train
 path: tamil/train-*
- config_name: telugu
 data_files:
 - split: train
 path: telugu/train-*
- config_name: uk_ireland_en
 data_files:
 - split: train
 path: uk_ireland_en/train-*
- config_name: venezuelan_es
 data_files:
 - split: train
 path: venezuelan_es/train-*
- config_name: yoruba
 data_files:
 - split: train
 path: yoruba/train-*
- config_name: labeled44k
 data_files:
 - split: train
 path: labeled44k/train-*
---
# TTS-Clean44k
A **multilingual pool of verified-clean, wideband speech** for training and evaluating speech
restoration / text-to-speech (TTS) models. Every utterance is independently checked on **two**
axes and stored as parquet with its per-utterance quality scores attached:
1. **Native sample rate ≥ 44.1 kHz** — measured per file with `ffprobe`, *never* trusting the
 source's advertised rate. Anything below 44.1 kHz is dropped.
2. **DNSMOS P.835 `bak` ≥ 3.644** — the background-noise MOS from the [DNSMOS P.835](https://github.com/microsoft/DNS-Challenge)
 model. Only genuinely clean recordings pass.
The dataset was assembled as the **clean teacher pool** for Sidon call-centre
speech restoration (the decoder is trained to *reproduce* its teacher, so the teacher must be
genuinely clean and full-band), but it is broadly useful as a filtered multilingual TTS corpus.
## Composition
**28 language/source configs · 119,950 utterances · 208.7 h · utterance-weighted DNSMOS bak 4.036 / sig 3.484 / ovrl 3.203.**
| # | config | utts | hours | bak | sig | ovrl |
|--:|--------|-----:|------:|----:|----:|-----:|
| 1 | hifitts | 19,447 | 30.0 | 4.026 | 3.533 | 3.247 |
| 2 | uk_ireland_en | 14,925 | 28.0 | 4.077 | 3.572 | 3.313 |
| 3 | cv_zhcn | 8,948 | 15.4 | 3.952 | 3.466 | 3.145 |
| 4 | basque | 6,907 | 13.5 | 4.043 | 3.521 | 3.230 |
| 5 | galician | 5,343 | 10.0 | 4.055 | 3.521 | 3.248 |
| 6 | catalan | 4,018 | 9.0 | 3.977 | 3.433 | 3.127 |
| 7 | peruvian_es | 4,991 | 8.8 | 4.052 | 3.423 | 3.154 |
| 8 | south_african | 5,240 | 8.3 | 3.977 | 3.460 | 3.149 |
| 9 | kannada | 3,542 | 7.6 | 4.045 | 3.436 | 3.165 |
| 10 | gujarati | 3,789 | 7.4 | 4.079 | 3.489 | 3.226 |
| 11 | argentinian_es | 4,362 | 6.8 | 4.077 | 3.520 | 3.246 |
| 12 | colombian_es | 4,105 | 6.8 | 4.052 | 3.404 | 3.144 |
| 13 | chilean_es | 3,755 | 6.6 | 4.056 | 3.424 | 3.160 |
| 14 | tamil | 3,633 | 6.4 | 4.027 | 3.300 | 3.035 |
| 15 | nigerian_en | 2,687 | 5.1 | 4.102 | 3.548 | 3.299 |
| 16 | javanese | 2,906 | 4.2 | 4.004 | 3.467 | 3.163 |
| 17 | malayalam | 2,470 | 4.1 | 4.034 | 3.367 | 3.095 |
| 18 | aishell3 | 2,814 | 4.1 | 4.047 | 3.513 | 3.218 |
| 19 | telugu | 2,517 | 4.0 | 4.053 | 3.358 | 3.098 |
| 20 | venezuelan_es | 2,466 | 4.0 | 4.057 | 3.473 | 3.208 |
| 21 | burmese | 2,071 | 3.6 | 4.020 | 3.495 | 3.203 |
| 22 | sundanese | 2,116 | 3.5 | 3.999 | 3.443 | 3.140 |
| 23 | khmer | 2,098 | 3.2 | 4.092 | 3.448 | 3.202 |
| 24 | marathi | 1,400 | 2.8 | 4.088 | 3.433 | 3.186 |
| 25 | yoruba | 1,464 | 2.2 | 4.035 | 3.476 | 3.178 |
| 26 | nepali | 1,157 | 2.0 | 4.000 | 3.496 | 3.183 |
| 27 | puertorico_es | 516 | 0.9 | 4.050 | 3.443 | 3.169 |
| 28 | cv_ta | 263 | 0.4 | 3.855 | 3.116 | 2.780 |
## Schema
Each config is a single `train` split with columns:
| column | type | description |
|---|---|---|
| `audio` | `Audio(sampling_rate=48000)` | mono waveform, decoded to 48 kHz |
| `source` | `string` | source/config name |
| `bak` | `float32` | DNSMOS P.835 background-noise MOS (≥ 3.644 for every row) |
| `sig` | `float32` | DNSMOS P.835 signal MOS |
| `ovrl` | `float32` | DNSMOS P.835 overall MOS |
| `duration` | `float32` | clip length in seconds |
Long recordings are chunked to ≤ 15 s; short utterances (≥ 4 s) are kept whole.
## Usage
```python
from datasets import load_dataset
# load one config (source)
ds = load_dataset("Scicom-intl/TTS-Clean44k", "uk_ireland_en", split="train")
print(ds[0]["audio"], ds[0]["bak"], ds[0]["duration"])
# or stream a large config
ds = load_dataset("Scicom-intl/TTS-Clean44k", "hifitts", split="train", streaming=True)
```
## Sources
- **OpenSLR high-quality TTS** — Javanese (41), Sundanese (44), Tamil (65), Telugu (66),
 Malayalam (63), Marathi (64), Khmer (42), Nepali (43), Gujarati (78), Kannada (79), Burmese (80).
- **OpenSLR crowdsourced** — Argentinian/Chilean/Colombian/Peruvian/Puerto-Rican/Venezuelan Spanish
 (61/71/72/73/74/75), Catalan (69), Basque (76), Galician (77), Yoruba (86), Nigerian English (70),
 South-African English (32), UK & Ireland English (83).
- **AISHELL-3** (Mandarin), **Hi-Fi TTS** (English, capped at 30 h for balance), **Common Voice**
 (Mandarin `zh-CN`, Tamil `ta`).
## What the gates rejected
The verification is strict on purpose — sources that only *claim* to be high-fidelity were dropped:
- **Common Voice `id` (Indonesian)** and **`yue` (Cantonese)** — excluded entirely; every clip was
 below 44.1 kHz (Cantonese was uniformly 32 kHz).
- **VCTK** — omitted here only because its available mirror was download-throttled, not for quality.
Nothing below the sample-rate or DNSMOS bar is included.
## Licensing
Audio is redistributed from the upstream corpora listed above; each retains its **original license**
(OpenSLR corpora are variously CC BY / CC BY-SA / CC0, AISHELL-3 is research-use, Hi-Fi TTS is CC BY 4.0,
Common Voice is CC0). Consult the corresponding source before commercial use. The DNSMOS scores and the
48 kHz re-encoding are provided as-is.
## `labeled44k` — 2.87M transcribed clips, ≥44.1 kHz (added 2026-08)
The other 32 subsets are a DNSMOS-filtered clean-TTS pool and carry **no transcripts**;
their `audio.path` is also empty, which makes a row impossible to trace back to its
upstream metadata. `labeled44k` is a separate, much larger pool built to close both gaps.
| | |
|---|--:|
| clips | **2,870,194** |
| duration | **4,297 h** |
| size | 150.3 GB across 361 parquet shards |
| sample rate | 1,683,440 @ 48 kHz + 1,186,754 @ 44.1 kHz (**per-clip verified ≥ 44.1 kHz**) |
| transcripts | **every clip** (`text`) |
| speaker labels | **every clip** (`speaker`), 206,102 distinct |
Provenance: assembled from [`malaysia-ai/Multilingual-TTS`](https://huggingface.co/datasets/malaysia-ai/Multilingual-TTS),
joining each source's `<Source>/train-*.parquet` (`audio_filename` → `text`) against the
matching `<Source>_audio.zip`, across 317 sources. Audio bytes are the **original upstream
encoding**, not re-encoded.
Columns: `audio{bytes,path}`, `source`, `duration`, `sr`, `lang`, `speaker`, `text`.
**Speaker labels cover 100% of clips** (2,870,194/2,870,194; **206,102 distinct speakers**),
recovered from the upstream `speaker` column. The join is exact rather than fuzzy: each file
is named `{source}__{md5(upstream_filename)[:12]}`, so the md5 maps every clip back to its
upstream row deterministically — a clip either matches or is reported, never guessed. Note
speaker ids are namespaced per source (e.g. `hifi-tts_9017`), so they are unique across the
corpus and safe to group on directly.
Two caveats worth reading before you use it:
- **`audio` declares no fixed `sampling_rate`**, unlike the other subsets, because this
 pool is genuinely mixed. Read the real rate from `sr`; if you cast the column to a fixed
 rate you will resample 41% of the corpus.
- **`lang` is unreliable: 1,795,530 clips (62.6%) are `unk`.** The tag comes from
 keyword-matching the source name, and most of the 317 sources match no keyword; only 23
 distinct tags exist. The `text` itself is trustworthy and its script identifies the
 language — prefer script detection over this column.
No DNSMOS (`bak`/`sig`/`ovrl`) is provided for this subset: it was not measured for this
pool, and the fields are left absent rather than filled with placeholder values. Unlike the
curated subsets, `labeled44k` is **not** DNSMOS-filtered — it is filtered on sample rate,
decodability, duration and transcript presence only.