AI Briefingnoisekit released for ASR testing
Key point
It's a CLI tool that converts clean speech data into noisy data resembling real call environments to test ASR performance.
Details
When testing ASR (Automatic Speech Recognition) models in real-world environments, this tool bridges the gap between clean datasets (FLEURS, CommonVoice, etc.) and real production environments mixed with noise.
noisekit applies various degradations to existing annotated clean datasets, generating noisy datasets while preserving the same annotations. This allows you to instantly calculate WER (Word Error Rate) across various environments without any additional labeling.
Key features provided:
- telecom: Implements real phone call environments by applying G.711 narrowband, 8-bit BitCrush, and low-bitrate MP3
- noise: Mixes ambient noise using MUSAN and others
- reverb: Reverb effects simulating far-field microphone environments
- low_bitrate / clipping: Reproduces low-bitrate compression and microphone saturation phenomena
The output is compatible with the HuggingFace AudioFolder format and can be loaded as a dataset immediately, with PESQ, SNR, and NISQA scores provided together via metadata.jsonl for analyzing quality correlations.
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