AudioCC-Lab/PICSAFEv1

Speech Quality Test Labels

It publishes this README, metadata.jsonl, and a tag-to-binary conversion script, because the 14 source datasets have different copyright and access terms.

The manifest contains the 10,728-sample PICSAFEv1 subset. It is not a license to redistribute the underlying recordings.

Metadata fields

• file_name: audio basename.
• id: globally unique sample identifier with the [source] prefix.
• source: source dataset name.
• source_path: path relative to the origin directory.
• tags: list of labels.

Source datasets and official download paths

Source dataset	Samples	License / access note	Official download or access path
CSTR-NAM-TIMIT-Plus	842	ODC-By 1.0	Edinburgh DataShare
DNS5_LibriVox	510	LibriVox public domain; jurisdiction restrictions may apply	Microsoft DNS5 download instructions
EARS	1,042	CC BY-NC 4.0; non-commercial use	Official GitHub repository
MSceneSpeech	1,000	Dataset audio terms are not stated separately; confirm with authors	Project page / Google Drive
NISQA (NISQA_TEST_FOR)	724	Original source terms; this subset is restricted to non-commercial research/forensic use	NISQA Corpus wiki / DepositOnce archive
SOMOSv2	500	Research and non-commercial use only; redistribution under the same terms	Zenodo record
TencentCorpus	1,010	No separate public audio license located; challenge access terms apply	ConferencingSpeech2022 repository / challenge plan
VCTK	1,000	CC BY 4.0 for the official VCTK release; verify the exact version	Edinburgh DataShare
WenetSpeech	600	CC BY 4.0; access requires the official form/password workflow	OpenSLR SLR121
Whisper40	500	No explicit dataset license located in the official repository	Official GitHub repository
WSJ	1,000	LDC User Agreement; license required	WSJ0 / LDC93S6A and WSJ1 / LDC94S13A
zhvoice	500	Mixed upstream datasets; no unified license	Official GitHub repository (Baidu download link is on the page)
LibriTTS-R	500	CC BY 4.0	OpenSLR SLR141
LibriTTS	1,000	CC BY 4.0	OpenSLR SLR60

Label schema

In article, it is described that the 33 labels are:

Nspk, distortion, emotional, enhanced, excessive_sibilance, fast_speed, female, high_pitch, imperceptible_noise_level, instantaneous_noise, low_noise_level, low_pitch, male, microphone_popping, mid_high_noise_level, music_or_effect, nb_noise, non_binary, non_speech, read_speech, real_recording, regular_pitch, regular_speed, reverberation, singing_voice, slow_speed, speech_overlap, spontaneous_speech, synthetic, vocal_sound, wb_noise, whispered_speech, and wrong_transcript.

Converting tags to binary labels

Labels are derived from the human tags, not predictions from a decision tree or metric thresholds.

• 1: accepted under the selected policy (positive).
• 0: rejected because at least one negative tag is present (negative).

The label is 0 if any annotation tag belongs to the mode-specific negative set below or the additional --negative_tags set; otherwise it is 1.

Mode	Tags that produce label 0 (any match)
strict (default)	Nspk, speech_overlap, low_noise_level, mid_high_noise_level, reverberation, music_or_effect, microphone_popping, excessive_sibilance, distortion
mid	speech_overlap, mid_high_noise_level, reverberation, music_or_effect, microphone_popping, excessive_sibilance, distortion
loose	speech_overlap, mid_high_noise_level, reverberation, music_or_effect, distortion

mid allows Nspk and low_noise_level; loose additionally allows microphone_popping and excessive_sibilance. Other tags do not independently cause rejection. In particular, non_speech, wrong_transcript, singing_voice, vocal_sound, nb_noise, wb_noise, and instantaneous_noise are not default negative tags. Label 1 means policy acceptance, not a universal claim of clean speech or a correct transcript. Add rejection tags explicitly if needed. Names are case-sensitive (Nspk). An empty tag list yields 1, matching the reference function, but does not establish annotation completeness. Missing tag fields and unknown tag names are errors.

Run from the repository directory:

# Default policy.
python3 tags_to_binary.py --input metadata.jsonl --output /tmp/picsafe_strict.jsonl

# Alternative policy (also supports loose).
python3 tags_to_binary.py --input metadata.jsonl --output /tmp/picsafe_mid.jsonl --positive_mode mid

# Custom policy, different from the default labels.
python3 tags_to_binary.py --input metadata.jsonl --output /tmp/picsafe_custom.jsonl --negative_tags non_speech wrong_transcript

# Original annotations: UID and semicolon-separated Tags columns.
python3 tags_to_binary.py --input annotations.tsv --output /tmp/picsafe_from_tsv.jsonl

Output JSONL preserves all original fields and adds binary_label and label_policy (the selected positive_mode and additional negative_tags). TSV input also adds id from UID and tags from Tags. The script prints class counts, validates unique IDs and tag names, and refuses to overwrite existing output files. The source metadata.jsonl is unchanged. Join with metric scores using globally unique id; basenames can collide. To reproduce a threshold-search run, use the same mode, additional negative tags, and sample subset.

Licensing and limitations

This repository does not grant, aggregate, or override any source-dataset license. Obtain the audio directly from the source providers and follow their current terms, attribution requirements, access restrictions, and applicable privacy/publicity rules. Some sources require non-commercial research use, registration, a license, or direct permission from the authors.