--- language: - en tags: - audio - remote-sensing - referring-image-segmentation - multimodal - webdataset task_categories: - image-segmentation configs: - config_name: clean data_files: - split: train path: data/clean/train-*.tar - split: validation path: data/clean/validation-*.tar - split: test path: data/clean/test-*.tar - config_name: hard data_files: - split: validation path: data/hard/validation-*.tar - split: test path: data/hard/test-*.tar --- # VoiceAeroRef VoiceAeroRef is an audio-guided referring image segmentation resource for aerial and remote-sensing imagery. It pairs RISBench image-expression-mask triplets with spoken English referring expressions and provides controlled speech-corruption splits for robustness evaluation.

VoiceAeroRef construction pipeline

## Dataset summary The release contains 262,696 audio-metadata records in 44 deterministic WebDataset TAR shards (approximately 45.05 GB). Audio is stored as 16 kHz, mono, PCM16 WAV. | Configuration | Split | Records | Voice policy | |---|---:|---:|---| | clean | train | 210,352 | eight voices per valid source | | clean | validation | 10,013 | one balanced voice per expression | | clean | test | 16,159 | one balanced voice per expression | | hard | validation | 10,013 | one controlled corruption per expression | | hard | test | 16,159 | one controlled corruption per expression | The source audit starts from 52,472 RISBench triplets. Six training sources whose expression contains only punctuation are excluded, leaving 52,466 valid image-expression-mask triplets. ## Speech inventory Clean speech uses eight English neural voices: | Locale | Accent | Female voice | Male voice | |---|---|---|---| | en-US | US | Jenny | Guy | | en-GB | GB | Sonia | Ryan | | en-AU | AU | Natasha | William | | en-IN | IN | Neerja | Prabhat | For training, every valid source has all eight voices. The intended training protocol samples exactly one voice for each source in each epoch. Validation and test use one deterministically balanced voice per expression. ## Hard configuration The hard validation and test splits use three synthetic interference types: - rotor noise; - wind noise; - mixed rotor and wind noise. Each type has low, medium and high severity, centred at 15 dB, 7.5 dB and 0 dB SNR respectively, with deterministic per-utterance perturbation. Training speech remains clean. ## Record format Every WebDataset example contains a WAV file and a JSON file with the same key: ```text test_000000__au_female.wav test_000000__au_female.json ``` The JSON metadata contains fields such as: ```json { "sample_id": "test_000000", "source_index": 0, "image": "img_rgb/test_0_0.png", "mask": "mask/test_0_0.png", "phrase": "The baseball field featured in the image ...", "voice": "en-AU-NatashaNeural", "accent": "AU", "gender": "female", "locale": "en-AU", "voice_id": "au_female", "config": "clean", "split": "test", "condition": "clean" } ``` Hard examples additionally contain `noise_type`, `severity`, `snr_db` and `noise_seed`. ## Loading with 🤗 Datasets Streaming avoids downloading the complete release: ```python from datasets import load_dataset dataset = load_dataset( "lironui/VoiceAeroRef", "clean", streaming=True, ) sample = next(iter(dataset["train"])) print(sample.keys()) ``` Hard evaluation data: ```python hard = load_dataset( "lironui/VoiceAeroRef", "hard", split="test", streaming=True, ) sample = next(iter(hard)) ``` ## Downloading files Install the Hugging Face CLI: ```bash pip install -U huggingface_hub ``` Download the complete repository: ```bash hf download lironui/VoiceAeroRef \ --repo-type dataset \ --local-dir VoiceAeroRef ``` Download only clean test shards and release metadata: ```bash hf download lironui/VoiceAeroRef \ --repo-type dataset \ --include "data/clean/test-*.tar" \ --include "release_manifest.json" \ --include "checksums.sha256" \ --local-dir VoiceAeroRef ``` The companion AeroReformer2 repository includes `scripts/prepare_voiceaeroref.py`, which downloads selected configurations, checks optional SHA-256 digests, extracts WAV files and writes training-ready JSONL manifests. ## Images and masks This repository distributes speech and metadata only. RISBench imagery and masks are not duplicated. The `image` and `mask` fields preserve the original RISBench references; users must obtain RISBench separately and comply with its terms. ## Integrity and exclusions - `release_manifest.json` records every shard, record count, byte size and SHA-256 digest. - `checksums.sha256` provides a checksum list suitable for independent verification. - `excluded_samples.jsonl` documents the six invalid punctuation-only source expressions. ## Intended use VoiceAeroRef supports research on: - audio-guided referring segmentation; - aerial and remote-sensing scene understanding; - speech robustness under rotor and wind interference; - accent- and gender-aware evaluation. It is not designed for speech recognition benchmarking, speaker identification, surveillance decisions or safety-critical deployment. ## Limitations - Speech is synthesized and does not reproduce the full variability of human speech. - The release contains English speech with four accent groups. - Corruptions are controlled synthetic conditions rather than recordings from every real flight environment. - Dataset performance depends on the coverage and annotation properties of RISBench. ## Licensing and attribution This dataset card does not grant additional rights to RISBench imagery or annotations. Users and redistributors must comply with the original RISBench terms and with any terms governing the speech-generation service. Before public release, the publisher should add the final dataset license selected after verifying compatibility with those upstream terms. ## Citation ```bibtex @dataset{voiceaeroref2026, title = {VoiceAeroRef: Audio-Guided Referring Segmentation for Aerial Imagery}, author = {AeroReformer2 Authors}, year = {2026}, url = {https://huggingface.co/datasets/lironui/VoiceAeroRef} } ```