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TTS-Clean44k / README.md
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huseinzolkepliscicom
labeled44k: add speaker column (100% coverage, 206102 speakers); shards rebuilt with 512-row row groups for the viewer
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  - config_name: labeled44k
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      - name: source
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      - name: sr
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      - name: lang
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      - name: speaker
        dtype: string
      - name: text
        dtype: string
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        num_examples: 2870194
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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 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 

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, 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.