This dataset contains audio from multiple sources with different licenses. Some sources (TSync2, GigaSpeech2) are restricted to non-commercial use only. By accessing this dataset, you agree to comply with each source's license terms.

Vaja-Thai (วาจา) — Combined Thai TTS Dataset

A unified, quality-filtered Thai speech dataset combining multiple sources for Text-to-Speech (TTS) research. All audio is resampled to 24 kHz WAV format.

Dataset Summary

Metric	Value
Total samples	289,916
Total hours	554.6h
Sampling rate	24,000 Hz
Format	WAV 16-bit PCM
Language	Thai (ภาษาไทย)

Sources

Source	Samples	Hours	License	Description
tsync2	1,823	3.7h	CC-BY-NC-SA-3.0	NECTEC professional TTS corpus, single female speaker
porjai_central	177,714	412.5h	CC-BY-SA-4.0	CMKL crowdsourced Central Thai speech
gigaspeech2	5,656	8.1h	non-commercial-research-only	GigaSpeech2 Thai dev+test (human-annotated)
commonvoice	104,723	130.3h	CC-0	Mozilla Common Voice Thai (validated split)

Loading the Dataset

from datasets import load_dataset

# Load a specific source
ds = load_dataset("dubbing-ai/vaja-thai", "tsync2")
ds = load_dataset("dubbing-ai/vaja-thai", "porjai_central")
ds = load_dataset("dubbing-ai/vaja-thai", "gigaspeech2")
ds = load_dataset("dubbing-ai/vaja-thai", "commonvoice")

# Streaming mode (no full download needed)
ds = load_dataset("dubbing-ai/vaja-thai", "porjai_central", streaming=True)
for sample in ds["train"].take(10):
    print(sample["text"])

# Load all sources combined
ds = load_dataset("dubbing-ai/vaja-thai", "all")

Schema

Column	Type	Description
`id`	string	Unique sample ID (`{source}_{original_id}`)
`audio`	Audio(24000)	Audio waveform
`text`	string	Thai transcription
`source`	string	Origin dataset name
`speaker_id`	string	Speaker identifier
`speaker_gender`	string	Gender if known (male/female/None)
`duration_s`	float	Duration in seconds
`original_sr`	int	Original sampling rate before resampling
`quality_tier`	int	1–4 refined quality tier (see below)
`snr_db`	float	Estimated Signal-to-Noise Ratio in dB
`whisper_cer`	float	Character Error Rate from Whisper validation (None if skipped)
`license`	string	License of the source dataset

Quality Filtering

Whisper validation: All sources transcribed with openai/whisper-large-v3-turbo and filtered by Character Error Rate (CER ≤ 0.15).
CER normalization (via pythainlp): Thai character normalization, Arabic digits converted to Thai number words, spaces removed, non-Thai characters stripped. Whisper sometimes outputs English for loan words (~4–6% of samples) — these are stripped, which may inflate CER for affected samples.
Duration: 1.0s – 30.0s
Audio energy: Minimum RMS > -50 dBFS (removes near-silent clips)
Clipping: < 1% clipped samples

Upsampling

Sources at 16 kHz (Porjai, GigaSpeech2) were upsampled using AP-BWE (IEEE/ACM Trans. ASLP 2024), a GAN-based bandwidth extension model with dual-stream amplitude-phase prediction. 292x real-time on GPU.
TSync2 (44.1 kHz) was downsampled with librosa kaiser_best.
Common Voice (48 kHz MP3) was decoded and downsampled with librosa.

Quality Tiers

Each sample has a quality_tier column (1–4) assigned based on both source provenance and measured audio quality (CER + SNR). This ensures noisy ASR-origin samples don't pollute TTS training, while clean ASR samples can still be promoted.

Tier	Criteria	Description	Use case
1	Studio/human-annotated, OR ASR with CER ≤ 0.03 + SNR ≥ 25 dB	Highest quality	Fine-tuning, high-quality single/few-speaker TTS
2	CER ≤ 0.08 + SNR ≥ 15 dB	Clean ASR samples	Multi-speaker TTS with verified transcriptions
3	CER ≤ 0.15 + SNR ≥ 10 dB	Acceptable quality	Pre-training, data augmentation
4	Passes basic filters but lower measured quality	Marginal	Large-scale pre-training only, use with caution

Base assignments (before refinement by CER + SNR):

TSync2 → Tier 2 (studio recording, but some transcription issues found via CER validation)
GigaSpeech2 → Tier 3 ("human-annotated" but high CER variance in Thai)
Common Voice → Tier 2 (community up/down vote validated)
Porjai Central → Tier 3 (crowdsourced, Whisper-filtered only)

Example — train only on tier 1+2 (recommended for TTS):

ds = load_dataset("dubbing-ai/vaja-thai", "porjai_central")
ds_high_quality = ds.filter(lambda x: x["quality_tier"] <= 2)

Example — filter by SNR directly:

ds_clean = ds.filter(lambda x: x["snr_db"] >= 20)

Speaker Labels

tsync2: Single known professional female speaker (tsync2_nun)
porjai_central: No speaker labels available (porjai_central_unknown)
gigaspeech2: YouTube channel ID used as speaker proxy
commonvoice: client_id hash used as speaker proxy, with optional gender metadata

License

Each config has its own license. When combining configs, the most restrictive license applies (non-commercial):

Config	License	Commercial use
`tsync2`	CC-BY-NC-SA 3.0	No
`porjai_central`	CC-BY-SA 4.0	Yes
`gigaspeech2`	Non-commercial research/education only	No
`commonvoice`	CC-0 (public domain)	Yes

Check the license column in each sample for per-sample license info.

Citation

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

@inproceedings{ardila-etal-2020-common,
    title = "Common Voice: A Massively-Multilingual Speech Corpus",
    author = "Ardila, Rosana and Branson, Megan and Davis, Kelly and Kohler, Michael
              and Meyer, Josh and Henretty, Michael and Morais, Reuben and Saunders, Lindsay
              and Tyers, Francis and Weber, Gregor",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    year = "2020",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2020.lrec-1.520/",
    pages = "4218--4222"
}

@inproceedings{suwanbandit23_interspeech,
    title = "Thai Dialect Corpus and Transfer-based Curriculum Learning
             Investigation for Dialect Automatic Speech Recognition",
    author = "Suwanbandit, Artit and Naowarat, Burin and Sangpetch, Orathai
              and Chuangsuwanich, Ekapol",
    booktitle = "Interspeech 2023",
    year = "2023",
    pages = "4069--4073",
    doi = "10.21437/Interspeech.2023-1828"
}

@article{gigaspeech2,
    title = "GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus
             for Low-Resource Languages with Automated Crawling, Transcription and Refinement",
    author = "Yang, Yifan and Song, Zheshu and Zhuo, Jianheng and Cui, Mingyu
              and Li, Jinpeng and Yang, Bo and Du, Yexing and Ma, Ziyang
              and Liu, Xunying and Wang, Ziyuan and Li, Ke and Fan, Shuai
              and Yu, Kai and Zhang, Wei-Qiang and Chen, Guoguo and Chen, Xie",
    journal = "arXiv preprint arXiv:2406.11546",
    year = "2024"
}

@inproceedings{wutiwiwatchai2007tsync,
    title = "An Intensive Design of a Thai Speech Synthesis Corpus",
    author = "Wutiwiwatchai, Chai and Saychum, Sudaporn and Rugchatjaroen, Anocha",
    booktitle = "International Symposium on Natural Language Processing (SNLP 2007)",
    year = "2007"
}

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Paper for dubbing-ai/vaja-thai

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement

Paper • 2406.11546 • Published Jun 17, 2024 • 1