WildASR reverb speech-to-text benchmark dataset
284 clips with room echo applied to the clean recordings.
- Items
- 284
- fixed public inputs
- Models measured
- 0
- last 30 days
Inside the dataset
284 fixed public inputs — every model is tested on exactly these.
CLIP 1 · 13.4s
“"he [wales] basically lied to us from the start. first by acting as if this was for legal reasons. second by pretending he was listening to us right up to his art deletion.”
CLIP 2 · 14.3s
“a car bomb detonated at police headquarters in gaziantep turkey yesterday morning killed two police officers and injured more than twenty other people”
CLIP 3 · 13.2s
“a civilization is a singular culture shared by a significant large group of people who live and work co-operatively a society”
3 of 284 clips, streamed from the public benchmark storage bucket.
Source: bosonai/WildASR environment_degradation__en__fleurs_reverberation_en · License: Apache-2.0 (WildASR; audio derived from FLEURS, CC-BY-4.0)
What it tests
This condition adds room reflections to the clean recordings. It measures how recognition changes when speech is captured in reverberant spaces rather than through a close microphone.
This condition is derived from the WildASR environment family and FLEURS read speech. Each paired condition uses the same utterance and transcript as the WildASR clean baseline, so the WER change isolates the effect of the test condition.
Metrics reported for this dataset
Same datasets, prompts and metric definitions for every model, measured by Coval’s open-source runner. Full methodology on the overview.
Evaluate your own voice agent
Use Coval to test your production configuration, prompts and calls—not only the public benchmark endpoints.