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"training_data": "synthetic only: an edge-tts voice grid, OmniVoice clips, with six held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.994,
"false_activations_per_hour": 0.19,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 338,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.935,
"false_activations_per_hour": 0.04,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 338,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_hey_scout",
"file": "models/wakehubert_hey_scout.onnx",
"word": "hey scout",
"language": "en",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.36,
"sha256": "c72fba4f93cf69b4bd9510682a3a80db67f663a0686f4d331a34d44ff031fdf3",
"license": "Apache-2.0",
"training_data": "synthetic only: an edge-tts voice grid, OmniVoice clips, with six held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.959,
"false_activations_per_hour": 0.09,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 390,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.938,
"false_activations_per_hour": 0.04,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 390,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_wake_up",
"file": "models/wakehubert_wake_up.onnx",
"word": "wake up",
"language": "en",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.36,
"sha256": "524e54bc24cf790339928bf8656d0c23d533108328e7b316afbd65b8cdfe28aa",
"license": "Apache-2.0",
"training_data": "synthetic only: an edge-tts voice grid, OmniVoice clips, with six held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.981,
"false_activations_per_hour": 1.1,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 378,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.968,
"false_activations_per_hour": 0.19,
"recall_test_set": "held-out synthetic voices",
"recall_test_clips": 378,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_computer",
"file": "models/wakehubert_computer.onnx",
"word": "computer",
"language": "en",
"featurizer": "wakehubert",
"calibrated": false,
"default_threshold": 0.99,
"sha256": "4ecda088bcce9951730de9fc8b1c4f801ea208add0cbf8a1b9397ed7d8329496",
"license": "Apache-2.0",
"training_data": "TigreGotico/synthetic-wakeword-computer (CC BY 4.0); negatives: TigreGotico/not-wake-words-speech-en (CC BY 4.0) and AudioSet-derived clips",
"measured": null
},
{
"name": "wakehubert_hey_mycroft",
"file": "models/wakehubert_hey_mycroft.onnx",
"word": "hey mycroft",
"language": "en",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.18,
"sha256": "5eb388364e489785fd34edf668dd5f780b0481d0c1de3d286720ed3cf545a193",
"license": "Apache-2.0",
"training_data": "synthetic only: text-to-speech clips from TigreGotico/synthetic-wakeword-hey_mycroft (edge-tts, Piper with LibriTTS-R speakers, Amazon Polly, OVOS TTS plugins, OmniVoice, and Chatterbox and OpenVoice conversions onto Common Voice speakers, CC BY 4.0) and further edge-tts and Google Translate TTS clips, with eight held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.974,
"false_activations_per_hour": 1.57,
"recall_test_set": "held-out synthetic voices: held-out edge-tts voices (285 clips) and Kokoro voices converted to unseen speakers (379 clips)",
"recall_test_clips": 664,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.848,
"false_activations_per_hour": 0.24,
"recall_test_set": "held-out synthetic voices: held-out edge-tts voices (285 clips) and Kokoro voices converted to unseen speakers (379 clips)",
"recall_test_clips": 664,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_hey_ziggy",
"file": "models/wakehubert_hey_ziggy.onnx",
"word": "hey ziggy",
"language": "en",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.27,
"sha256": "55e849cb7a20ce037cbd9c6d6d4508f182731e35b1a3be159673c571d0890b02",
"license": "Apache-2.0",
"training_data": "synthetic only: an edge-tts voice grid, voice-converted copies of edge-tts clips, OmniVoice clips, each checked against a speech recogniser transcript, with eight held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.936,
"false_activations_per_hour": 0.64,
"recall_test_set": "held-out synthetic voices: OmniVoice clips and held-out edge-tts voices",
"recall_test_clips": 374,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.893,
"false_activations_per_hour": 0.24,
"recall_test_set": "held-out synthetic voices: OmniVoice clips and held-out edge-tts voices",
"recall_test_clips": 374,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_hey_potato",
"file": "models/wakehubert_hey_potato.onnx",
"word": "hey potato",
"language": "en",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.15,
"sha256": "3951a1f258bbe8d2685bb8c4c3921de0b8b26c89783af37d9ce999fb8219170e",
"license": "Apache-2.0",
"training_data": "synthetic only: an edge-tts voice grid, voice-converted copies of edge-tts clips, OmniVoice clips, each checked against a speech recogniser transcript, with eight held-out edge-tts test voices excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.889,
"false_activations_per_hour": 0.34,
"recall_test_set": "held-out synthetic voices: 360 clips in held-out edge-tts voices only, without the OmniVoice test clips used for the other words",
"recall_test_clips": 360,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.806,
"false_activations_per_hour": 0.02,
"recall_test_set": "held-out synthetic voices: 360 clips in held-out edge-tts voices only, without the OmniVoice test clips used for the other words",
"recall_test_clips": 360,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_hey_stemcom",
"file": "models/wakehubert_hey_stemcom.onnx",
"word": "hey stemcom",
"language": "nl",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.28,
"sha256": "0610626100881dc8da8b416a0bad7bd03f4ea52537359ac9ebbe18ff49731b7a",
"license": "Apache-2.0",
"training_data": "synthetic only: a Dutch edge-tts voice grid (nl-NL and nl-BE), voice-converted copies of edge-tts clips, OmniVoice clips, each checked against a speech recogniser transcript, with one held-out edge-tts test voice excluded; LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv and the even half of an AudioSet noise sample",
"measured": {
"default": {
"recall": 0.89,
"false_activations_per_hour": 0.21,
"recall_test_set": "held-out synthetic voices: OmniVoice clips and held-out edge-tts voices",
"recall_test_clips": 337,
"negative_hours": 46.5
},
"0.8": {
"recall": 0.831,
"false_activations_per_hour": 0.06,
"recall_test_set": "held-out synthetic voices: OmniVoice clips and held-out edge-tts voices",
"recall_test_clips": 337,
"negative_hours": 46.5
}
}
},
{
"name": "wakehubert_despierta",
"file": "models/wakehubert_despierta.onnx",
"word": "despierta",
"language": "es",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.51,
"sha256": "8e993142a4ee30572f1338ce205a6466e61645b3f47465658d0bf7161c4e3ba2",
"license": "Apache-2.0",
"training_data": "synthetic only: OmniVoice clips with no reference speaker, one seed per clip, each checked against a speech recogniser transcript, and the training clips of TigreGotico/synthetic-wakeword-despierta (CC BY 4.0); LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv, the even half of an AudioSet noise sample, and Spanish speech from Common Voice 17 train (CC0) and FLEURS train (CC BY 4.0) cut into 1.5 s windows",
"measured": {
"default": {
"recall": 0.984,
"false_activations_per_hour": 0.58,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 370,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.6,
"in_language_negative_hours": 3.34
},
"0.8": {
"recall": 0.941,
"false_activations_per_hour": 0.03,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 370,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.3,
"in_language_negative_hours": 3.34
}
},
"notes": "In-language false activations: 0.60 per hour over 3.3 h of Spanish Common Voice 17 test and FLEURS dev speech at the default threshold. At the default threshold it fires on 19 of 20 edge-tts near-word probe clips (\"despiertas\", \"despierto\", \"depierta\", \"desperta\" and \"despertar\")."
},
{
"name": "wakehubert_aufwachen",
"file": "models/wakehubert_aufwachen.onnx",
"word": "aufwachen",
"language": "de",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.57,
"sha256": "4df0063c8087a39677ee708da05570447081571401e3142a8515abbd95c7fafb",
"license": "Apache-2.0",
"training_data": "synthetic only: OmniVoice clips with no reference speaker, one seed per clip, each checked against a speech recogniser transcript, and the training clips of TigreGotico/synthetic-wakeword-aufwachen (CC BY 4.0); LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv, the even half of an AudioSet noise sample, and German speech from Common Voice 17 train (CC0) and FLEURS train (CC BY 4.0) cut into 1.5 s windows",
"measured": {
"default": {
"recall": 0.995,
"false_activations_per_hour": 0.22,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 390,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 1.85,
"in_language_negative_hours": 3.25
},
"0.8": {
"recall": 0.972,
"false_activations_per_hour": 0.0,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 390,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.31,
"in_language_negative_hours": 3.25
}
},
"notes": "In-language false activations: 1.85 per hour over 3.2 h of German Common Voice 17 test and FLEURS dev speech at the default threshold. At the default threshold it fires on 8 of 8 edge-tts near-word probe clips (\"aufmachen\" and \"aufwachten\")."
},
{
"name": "wakehubert_wakker_worden",
"file": "models/wakehubert_wakker_worden.onnx",
"word": "wakker worden",
"language": "nl",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.56,
"sha256": "5c77d9de56c923ce0aa61506d6faaf6378949fd6df1a764110eae3323427d4ad",
"license": "Apache-2.0",
"training_data": "synthetic only: OmniVoice clips with no reference speaker, one seed per clip, each checked against a speech recogniser transcript, and the training clips of TigreGotico/synthetic-wakeword-wakker_worden (CC BY 4.0); LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv, the even half of an AudioSet noise sample, and Dutch speech from Common Voice 17 train (CC0) and FLEURS train (CC BY 4.0) cut into 1.5 s windows",
"measured": {
"default": {
"recall": 0.997,
"false_activations_per_hour": 0.29,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 390,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.0,
"in_language_negative_hours": 2.43
},
"0.8": {
"recall": 0.985,
"false_activations_per_hour": 0.03,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 390,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.0,
"in_language_negative_hours": 2.43
}
},
"notes": "In-language false activations: 0.00 per hour over 2.4 h of Dutch Common Voice 17 test and FLEURS dev speech at the default threshold. At the default threshold it fires on none of 8 edge-tts near-word probe clips (\"wakker\", \"worden\")."
},
{
"name": "wakehubert_sveglia",
"file": "models/wakehubert_sveglia.onnx",
"word": "sveglia",
"language": "it",
"featurizer": "wakehubert-int8",
"calibrated": true,
"default_threshold": 0.47,
"sha256": "523544cc3629f4bd995b9acd4ec98ef217c1240b1c93ddd2f594f71399fdc473",
"license": "Apache-2.0",
"training_data": "synthetic only: OmniVoice clips with no reference speaker, one seed per clip, each checked against a speech recogniser transcript, and the training clips of TigreGotico/synthetic-wakeword-sveglia (CC BY 4.0); LibriSpeech train-clean-100 (CC BY 4.0) mixed in as background babble; negatives from wakeforge datasets/train.csv, the even half of an AudioSet noise sample, and Italian speech from Common Voice 17 train (CC0) and FLEURS train (CC BY 4.0) cut into 1.5 s windows",
"measured": {
"default": {
"recall": 0.995,
"false_activations_per_hour": 1.16,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 222,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 1.7,
"in_language_negative_hours": 3.53
},
"0.8": {
"recall": 0.977,
"false_activations_per_hour": 0.1,
"recall_test_set": "held-out synthetic voices: OmniVoice clips whose seeds no training clip uses",
"recall_test_clips": 222,
"negative_hours": 31.1,
"false_activations_per_hour_in_language": 0.28,
"in_language_negative_hours": 3.53
}
},
"notes": "In-language false activations: 1.70 per hour over 3.5 h of Italian Common Voice 17 test and FLEURS dev speech at the default threshold. At the default threshold it fires on 18 of 24 edge-tts near-word probe clips (\"sveglio\", \"sveglie\", \"svegliati\", and the rhymes \"meraviglia\", \"bottiglia\" and \"voglia\")."
}
]
}
|