Changelog

0.14.1

Improvement

  • Clip LUFS and SoundPower model output to avoid -inf values

0.14.0

Features

  • Added a LUFS model to compute the instantaneous loudness following the standard ITU-R BS.1770.

Improvement

  • Added Sonarqube integration to inspect the code quality.

0.13.0

Breaking Changes

  • Add support for Python 3.10

  • Drop support for Python 3.7

Improvements

  • Update TensorFlow to 2.8.0

  • Support arm-64 (M1)

  • Update numpy to 1.22.3 and scipy to 1.8.0

0.12.2

Improvements

  • Support older linux distros (ubuntu18, debian10, …)

0.12.1

Vulnerability Fixes

  • Updated numpy to 1.21.4 to fix vulnerability CVE-2021-33430. This also required an update of scipy to 1.7.3

0.12.0

Breaking Changes

  • Changes the expected input specification of the SpeakerMap model. 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.

Features

  • Updates the Gender and UnderageSpeaker model for improved performance in environments with reverberation.

Bug Fixes

  • Updates the dependency version constraints for the NumPy and SciPy dependencies. Previously the version constraints allowed the installation of incompatible NumPy and SciPy versions.

0.11.1

Features

  • Updates the SoundPower class ranges to more adequately reflect real audio recordings (class names remain unaltered).

Bug Fixes

  • Adds missing reliability_threshold to SoundPower and AudioEvent models, which was causing the analysis to crash when processing files with at least one of these models requested in combination with use_chunking set to True.

0.11.0

Features

  • Updates the Arousal model for improved performance on environments with background disruptive noise.

  • Updates the Gender model receptive field to 3130ms.

  • Adds new AudioEvent model that is able to identify various categories of human produced sounds.

  • Adds new SoundPower model that is able to measure the intensity level of the audio in dB.

  • Adds new UnderageSpeaker model that is able to classify speakers as adult/children.

  • Updates tensorflow to 2.4.0.

0.10.0

Features

  • 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_f32le, pcm_f64le, pcm_s16le, pcm_s32le and pcm_u8 description here on the ffmpeg-docs

0.9.0

Breaking Changes

  • 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

Bug Fixes

  • Fixed bug in the audio resampling function. This bug only affected users that processed audio with a sampling rate other than 16000 Hz.

Features

  • 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

0.8.0

Breaking Changes

  • 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:

    1. 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.

    2. 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: speech, music, other

Fixed Bugs

  • 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.

Features

  • 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 volume threshold (0.005). This default value is more robust in common scenarios.

0.7.0

Bug Fixes

  • Remove unnecessary tensorflow log lines

Features

  • Add Emotions model for emotions classification

  • Update to tensorflow 2.3

0.6.0

Bug Fixes

  • Fixed bug in stream processing to account correctly for receptive field of the model

  • When use_chunking=True, the chunking method is actually used

Features

  • Add SpeechRT model for low-latency speech predictions (decision latency <100ms)

  • Add new methods of processing - process_audio_bytes, process_audio_chunk - more suitable for analysing byte numpy arrays directly

  • Make the SDK thread-safe

0.5.0

Breaking Changes

Bug Fixes

  • Performance bug in the output calculation

  • File processing results are now consistent with the streaming results

  • Typo in ‘GENDER_UNKNOWN’ constant

Features

  • Add ability to retrieve raw model outputs to allow for customisation. For example usage see here.

  • Add silence detection to all models

  • Optimize performance when using more than one model

0.4.0

Initial release with the Speech, Gender and Arousal models.