Models and credits
Hearsay is built on open work by the people and teams below. Their models run on your own device, in your browser; Hearsay uses them without modification, in the ONNX versions published by the onnx-community group on Hugging Face so that they can run in a web page.
Models
Whisper
OpenAI
Turns speech into words, with the time of each word. Hearsay offers three sizes: whisper-base (Fast), whisper-small (Balanced) and whisper-large-v3-turbo (Most accurate).
- License
- whisper-large-v3-turbo: MIT. whisper-base and whisper-small: Apache 2.0.
- Source
- github.com/openai/whisper
- Paper
- Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey and Ilya Sutskever. “Robust Speech Recognition via Large-Scale Weak Supervision.” 2022. doi:10.48550/arXiv.2212.04356
pyannote segmentation 3.0
Hervé Bredin, Alexis Plaquet and the pyannote.audio project
Finds where people are speaking and when the speaker changes, which Hearsay uses to cut the audio at pauses and to tell voices apart.
- License
- MIT
- Source
- huggingface.co/pyannote/segmentation-3.0 · github.com/pyannote/pyannote-audio
- Papers
- Alexis Plaquet and Hervé Bredin. “Powerset multi-class cross entropy loss for neural speaker diarization.” Interspeech 2023.
Hervé Bredin. “pyannote.audio 2.1 speaker diarization pipeline: principle, benchmark, and recipe.” Interspeech 2023.
WeSpeaker ResNet34 (VoxCeleb)
The WeSpeaker team (WeNet community), packaged by pyannote
Makes a “voiceprint” from a few seconds of someone's voice, so that Hearsay can group the parts of a recording spoken by the same person. Trained on the VoxCeleb dataset.
- License
- Creative Commons Attribution 4.0 International (CC BY 4.0), following the VoxCeleb dataset. Used without changes.
- Source
- huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM · github.com/wenet-e2e/wespeaker
- Paper
- Hongji Wang, Chengdong Liang, Shuai Wang, Zhengyang Chen, Binbin Zhang, Xu Xiang, Yanlei Deng and Yanmin Qian. “WeSpeaker: A research and production oriented speaker embedding learning toolkit.” ICASSP 2023.
Sample recording
The “Try a sample” interview is synthetic: a script read by the Microsoft Zira and David speech voices in Windows. The people in it are fictional.
Copyright
Hearsay's own code and design are © 2026 Applied Arts. The models, software and typefaces above belong to their authors and are used under the licenses shown. Transcripts you make are yours.