arcosoph/RACON_11h_v1

RACON (Real-world Audio Corpus for Negatives) v1

Overview

RACON is a comprehensive feature set derived from approximately 11 hours of diverse, real-world audio. It is designed to serve as a high-quality negative dataset for training and evaluating robust wake-word models, particularly within systems like Nanowakeword.

The primary goal of this corpus is to provide developers with a challenging and realistic collection of non-target acoustic events. By incorporating RACON into a development workflow, models can learn to be more resilient and avoid unintended activations in real-world deployment scenarios.

Key Statistics

• Total Duration: ~11 hours (of source audio)
• Format: NumPy Array (.npy)
• Size (Shape): (481345, 16, 96)
• Primary Use Case: Negative Training Data, Validation, and Benchmarking
• Acoustic Content: Multi-speaker conversations, ambient noise, music, and clean speech.

Dataset Composition

The features in RACON are generated from audio sourced from three distinct and publicly available corpora, ensuring a wide acoustic diversity:

Source Dataset	Original Duration	Description
DiPCo Corpus	~5.3 hours	The entire dataset, containing challenging multi-speaker conversations in noisy home environments.
Santa Barbara Corpus of Spoken American English (SBCSAE)	~3.7 hours	Selected clips featuring natural, unscripted American English conversations.
MUSDB18 Music Dataset	2.0 hours	A curated selection of music clips spanning various genres.
Total	~11 hours

Purpose and Usage

RACON is a versatile negative dataset, beneficial for both the training and evaluation phases of model development.

• During Training: By including these features as part of the negative class, a model learns to build a stronger decision boundary between the target wake-word and a vast array of common real-world sounds. This enhances the model's discriminative power and ability to generalize.

• During Validation & Testing: Using RACON as a validation set allows for a realistic estimation of a model's performance. It helps measure the model's robustness and calculate key metrics, such as the false activation rate in a controlled environment.

Example Manifest Configuration

Below is an example of how to integrate the RACON dataset into a feature_manifest.yaml for a Nanowakeword training pipeline, where it serves as the negative validation set (negatives_val).

feature_manifest:
  targets:
    t: "./trained_models/tadano_A_v176/features/positive_features_train.npy"

  negatives:
    AE28H_float32: "./trained_models/tadano_A_v100/features/AE29H_float32.npy" # Download from here: https://huggingface.co/datasets/arcosoph/AE29H_float32
    n: "./trained_models/tadano_A_v5/features/negative_features.npy"

  targets_val:
    tv: "./trained_models/tadano_A_v174/features/positive_features_train.npy"

  negatives_val:
    # RACON is used here for robust validation
    bv: "RACON_11h_v1.npy"

Feature Provenance and Attribution

The features in this dataset were generated using the same methodology as the openwakeword_features dataset.

Important: This dataset is a derivative work compiled for research purposes. Users must adhere to the original licensing terms of each constituent audio dataset (DiPCo, SBCSAE, and MUSDB18).