Updates to the SpeakerMap model for improved performance
Adds VoiceSignatures functionality which can be combined with the SpeakerMap model to facilitate: ** identification of speakers across multiple files ** pre-training to improve identification of known speakers
Expands supported datatypes ** Until now only audio in signed 16-bit integer and 32-bit float was supported, now we support all float (little-endian) types and signed little endian integer types (except the s24le) and unsigned 8 bit data. ** Supported list as per the pcm spec ->
pcm_u8description here on the ffmpeg-docs
The model bundle format was improved to make it easier to integrate new models. This change is not backwards compatible, so users that are updating from an older version will need to use the new license-key that was provided via email. Alternatively, it is possible to request that the current license-key is updated to the new model bundle format such that no code changes are required.
The DeepTone SDK can no longer be used together with the tensorflow module. For more information visit https://docs.oto.ai/sdk/troubleshooting#deeptone-sdk-and-tensorflow
Fixed bug in the audio resampling function. This bug only affected users that processed audio with a sampling rate other than 16000 Hz.
Remove tensorflow python module dependency which makes the DeepTone SDK package slimmer
Add language model support Early access on request
Add speaker-map model support for file processing Alpha version - access on request
Add python 3.7+ support
Overall performance improvements
The DeepTone python sdk is now integrated with our licensing API. The model bundle will be downloaded automatically, so the user does not need to provide the model bundle location anymore. The sdk can be initialized by providing only the license key:
engine = Deeptone(license_key="YOUR_KEY")
NOTE: A new license key will be required. License keys that worked for previous SDK versions will not work anymore.
The update process will be easier as well. The license keys are linked to a model bundle version. Once we have new improved model versions ready a new license key linked to the new model bundle version is distributed to the users. There are two options for users to update:
Exchange the current license key with the new one whenever you are ready to update to the new model bundle version. The SDK will then download and use the new model bundle.
A user can alternatively request that their current license key is upgraded to the new model bundle version. The SDK will then download and use the new model bundle the next time it is initialized.
The SpeechRT model now has three classes:
Fixed conversion of input data of type int16 to float32, such that the results are consistent for models with built-in normalization and models without normalization.
The SpeechRT model is now the default VAD model
Performance improvements when using more than one model
Use optimized model bundle format to increase load and inference performance
Comes with improved versions of the Speech, SpeechRT, Arousal and Gender models
New default value for the
0.005). This default value is more robust in common scenarios.
Remove unnecessary tensorflow log lines
Add Emotions model for emotions classification
Update to tensorflow 2.3
Fixed bug in stream processing to account correctly for receptive field of the model
use_chunking=True, the chunking method is actually used
Add SpeechRT model for low-latency speech predictions (decision latency <100ms)
Add new methods of processing -
process_audio_chunk- more suitable for analysing byte numpy arrays directly
Make the SDK thread-safe
The output of the
process_filefunction changed to align with the
process_streamfunction. For more information on the new output structure see https://docs.oto.ai/sdk/output-specification.
Performance bug in the output calculation
File processing results are now consistent with the streaming results
Typo in ‘GENDER_UNKOWN’ constant
Initial release with the Speech, Gender and Arousal models.