Deepgram open sources Kur to make DIY deep learning less painful

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Deepgram, a YC backed startup using machine learning to analyze audio data for businesses, is open sourcingan internal deep memorize tool called Kur .The liberate should furtherhelp those interested in the space get their ideas off the field more easily. The startup is also including 10 hours of transcribed audio, spliced into 10 second increments, to expedite the training process.

Similar to Keras, Kur further abstracts the process of constructing and training deep memorize models.By shaping deep memorize easier, Kur is also makingimage recognition and speech analysis more accessible.

Scott Stephenson, CEO of Deepgram, explained to me that when the company was first get off the field, the team usedLibriSpeech, an onlinedataset of audiobooks in the public domain split up and labeled for training early machine learningmodels.

Deepgram isnt reinventing the wheel with its liberate. Coupled with data dumps and open informant projects from startups, universities and big tech corporations alike, frames like Tensorflow, Caffe and Torch have become quite useable.The ImageNet database has worked wonders for image acknowledgment, and many developers use VoxForge for speech, but most open source data is never a bad thing.

You can start withclassifying images and end up with self driving autoes, addedStephenson. The phase is committing someone that first little part and then people can change the simulate and make it do something different.

Getting Kur into the hands of developerswill likewise help Deepgram with recruiting ability. The strategy has proved itself quite useful for huge tech corporations looking to recruit technical machine learning and data science engineers.

Via, developers will soon be able to share frameworks, data and information and weights to stimulation more invention in the space.Deepgram eventually wants to liberate weights for the data-set being released today so DIY-ers canavoid processor intensive educate altogether. Even with a relatively modest 10 hours of audio, modelsstill take about a period to train on a GPU and substantially longer with an off-the-shelf computer.

If you end up exhausting the Deepgram data set, you can also easily expand it with your own data. All you have to do is generate WAV files with embedded transcriptions in 10 second increments. You canfeed data-hungry deep memorize frameworks with moreresources in the public domain to improve accuracy.

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