Datasets:
dataset_info:
- config_name: seen
features:
- name: id
dtype: string
- name: device_class
dtype: string
- name: array_id
dtype: string
- name: condition
dtype: string
- name: n_mics
dtype: int32
- name: n_samples
dtype: int64
- name: sir_db
dtype: float32
- name: snr_db
dtype: float32
- name: sensor_snr_db
dtype: float32
- name: rt60
dtype: float32
- name: drr_db
dtype: float32
- name: mic_positions
sequence:
sequence: float32
- name: mix
dtype:
audio:
sampling_rate: 48000
- name: target
dtype:
audio:
sampling_rate: 48000
- name: enroll
dtype:
audio:
sampling_rate: 48000
splits:
- name: train
num_bytes: 23028272020
num_examples: 10000
- name: dev
num_bytes: 1382574300
num_examples: 600
- name: test
num_bytes: 2763509714
num_examples: 1200
download_size: 26944466147
dataset_size: 27174356034
- config_name: unseen
features:
- name: id
dtype: string
- name: device_class
dtype: string
- name: array_id
dtype: string
- name: condition
dtype: string
- name: n_mics
dtype: int32
- name: n_samples
dtype: int64
- name: sir_db
dtype: float32
- name: snr_db
dtype: float32
- name: sensor_snr_db
dtype: float32
- name: rt60
dtype: float32
- name: drr_db
dtype: float32
- name: mic_positions
sequence:
sequence: float32
- name: mix
dtype:
audio:
sampling_rate: 48000
- name: target
dtype:
audio:
sampling_rate: 48000
- name: enroll
dtype:
audio:
sampling_rate: 48000
splits:
- name: test
num_bytes: 3023450256
num_examples: 1200
download_size: 2983819608
dataset_size: 3023450256
configs:
- config_name: seen
data_files:
- split: train
path: seen/train-*
- split: dev
path: seen/dev-*
- split: test
path: seen/test-*
- config_name: unseen
data_files:
- split: test
path: unseen/test-*
license: cc-by-nc-4.0
pretty_name: 'WEAVE: Cross-Device Array-Agnostic Wearable Speech Enhancement'
tags:
- speech-enhancement
- microphone-array
- beamforming
- wearable
- array-agnostic
- zero-shot
task_categories:
- audio-to-audio
language:
- en
WEAVE — Cross-Device, Array-Agnostic Wearable Speech Enhancement Benchmark
WEAVE is a benchmark for target-speaker speech enhancement on wearable microphone arrays that measures the hardest generalization question in the field: does a model trained on one set of device geometries still work zero-shot on unseen array geometries it never saw in training? Unlike prior wearable benchmarks that fix a single microphone layout (e.g. a 4-mic glasses array), WEAVE spans glasses, earbuds, and neckbands with 2–8 microphones.
Generator code, docs, and evaluation: https://github.com/pujariaditya/WEAVE
⚠️ Licensing — please read first
WEAVE mixtures are rendered from three source corpora that are non-commercial / custom-licensed: EARS (clean speech), WHAM! (ambient noise), and TAU-SRIR DB (real room impulse responses, used only to derive reverberation statistics). The audio here is derivative of those sources and therefore inherits their non-commercial terms — released under CC BY-NC 4.0 for non-commercial research use only. By using it you agree to comply with the upstream licenses:
| Corpus | Role in WEAVE | License | Source |
|---|---|---|---|
| EARS | clean target + interferer speech | non-commercial research | https://sp-uhh.github.io/ears_dataset/ |
| WHAM! (high_res) | ambient diffuse noise | CC BY-NC 4.0 | http://wham.whisper.ai/ |
| TAU-SRIR DB | reverberation statistics (RT60, DRR) | non-commercial research | https://zenodo.org/record/6408611 |
For a license-clean path, the generator (Apache-2.0) at the GitHub repo regenerates this benchmark byte-identically from corpora you obtain yourself.
The benchmark
- 13,000 utterances, 48 kHz. Each is a simulated scene: one frontal (0°) target speaker + up to 5 interferers + diffuse noise + sensor noise, recorded by a device's mic array.
- Zero-shot cross-device split — the only variable between seen and unseen is device geometry:
| Config / split | Rows | Device classes | Mic counts |
|---|---|---|---|
seen / train |
10,000 | glasses, earbuds | 2, 4, 6 |
seen / dev |
600 | glasses, earbuds | 2, 4, 6 |
seen / test |
1,200 | glasses, earbuds | 2, 4, 6 |
unseen / test |
1,200 | glasses, earbuds, neckband | 3, 4, 6, 8 |
Train on head-worn devices (glasses/earbuds); the unseen split holds out a torso-worn
neckband (a different acoustic body) and an unseen mic count (3).
How it was built
Array-agnosticism lives in the renderer, not the corpora, so the same acoustic scene can be
materialized for any mic layout. Per utterance: mix = reverberant_target + Σ interferers + diffuse noise + sensor noise. The direct path is a parametric DRTF (rigid-sphere head for glasses/earbuds,
two-sphere "snowman" torso+head for neckbands, + device coloration + propagation delay). Late reverb
is an isotropic diffuse field synthesized from real TAU-SRIR statistics (per-octave RT60, DRR),
with inter-mic coherence sinc(2·f·d/c). TAU audio never enters a mixture — only its statistics.
Audio format
mix, target, and enroll are stored as the HuggingFace Audio feature (WAV, 48 kHz), so you
can listen to every clip in the dataset viewer. mix is a multichannel WAV (one channel per
microphone); target (the clean dry reference at mic 0) and enroll are mono. Each signal is
peak-normalized to 0.95 so nothing clips — this does not affect the benchmark because SI-SDR is
scale-invariant.
Loading
from datasets import load_dataset, Audio
import soundfile as sf, io, numpy as np
ds = load_dataset("RootAccess4Life/WEAVE", "seen", split="test") # or config "unseen"
row = ds[0]
target = np.asarray(row["target"]["array"]) # [T] clean dry target at mic 0 (label)
enroll = np.asarray(row["enroll"]["array"]) # [Te] enrollment of the target speaker
# full multichannel mix (all microphones): read the raw WAV bytes
raw = ds.cast_column("mix", Audio(decode=False))
mix, sr = sf.read(io.BytesIO(raw[0]["mix"]["bytes"])) # [T, n_mics]
mic_positions = np.asarray(row["mic_positions"], np.float32) # [n_mics, 3], device-frame, centroid-relative
Columns
| Column | Type | Notes |
|---|---|---|
id |
string | utterance id |
device_class |
string | glasses / earbuds / neckband |
array_id |
string | topology id (e.g. 0, eb2, nb8) |
condition |
string | <device_class>:<array_id> |
n_mics, n_samples |
int | array shape |
mic_positions |
list<list<float>> | [n_mics, 3], device-frame, centroid-relative (metres) |
mix |
Audio | multichannel WAV — the array recording (input) |
target |
Audio | mono WAV — dry direct-path target at mic 0 (the enhancement label / SI-SDR reference) |
enroll |
Audio | mono WAV — a different utterance of the target speaker |
sir_db, snr_db, sensor_snr_db, rt60, drr_db |
float | per-scene acoustic metadata |
Task & evaluation
Recover target from mix (single-channel estimate of the frontal target). The headline metric is
the cross-device generalization gap — the drop from the seen-test score to the zero-shot
unseen-test score (small = geometry-robust) — alongside SI-SDR / STOI / PESQ improvement over the
noisy mic-0 input. An evaluation module (weave-eval) is provided in the GitHub repo.
Limitations
Pure simulation (no real wearable-array recordings); parametric acoustics, not measured HRTFs; the neckband "snowman" omits inter-sphere multiple scattering (documented v1 approximation); reverb is a diffuse field synthesized from real room statistics, not full measured-RIR convolution; the dry reference is a modeled target. Real-measured-device validation is future work.
Citation
See CITATION.cff in the GitHub repository. Please also cite
the source corpora (EARS, WHAM!, TAU-SRIR DB).