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          "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\")."
    }
  ]
}