The DeepTone™ SDK comes with a set of different models. Each model is optimized to give insights on a certain voice property.
Gender model predicts the gender of voice in a given audio snippet.
Finding voice properties only works if there is a voice to classify in a given audio snippet.
This is why all the models that give insights on voice are combined with our
Speech model can categorize audio into three categories:
- No Speech
Currently we consider everything that is neither speech nor music to be 'No Speech', including silence. We will be able to categorize audio into a wider range of categories with
Silence detector and
HumanSounds model (coming soon ...).
Each of our classifier models will return an iterable that contains a time-series of predictions. Each element of the time-series contains the following information:
- Timestamp - in milliseconds
- Confidence - float ∈ [0,1]
- Model result - depends on the model that is used
Optionally a user can also request a
transitions components in addition to the time-series result when
processing a file. More information about those can be found in the file-processing section.
Each model has a different receptive field - the minimum amount of data it needs to process before it produces reliable results. This means that if you request a model to process data it can output results as often as desired (minimum every 64ms and a multiple of it) but the initial results might not be accurate since there was not enough data to math the receptive field. Also note that if you request your data to be processed by multiple models at the same time, each might start producing accurate results at different points in time, depending on their receptive fields.
All models can output predictions at best at every 64ms of audio data.
Detailed information on how to configure the properties of a model can be found in the Usage section. The following models are currently included in the DeepTone™ SDK:
|Speech||Speech classifier that can detect music, general noise, or human speech.||speech | no_speech | music||1082ms|
|Gender||Speaker gender classifier||male | female | unknown | no_speech||1851 ms|
|Arousal||Speaker arousal classifier||low | neutral | high | no_speech||2107 ms|