Deploying DanSpeech models instead of using an external API will, in addition to reducing costs, also reduce latency drastically, if deployed locally with a GPU. Systems, or companies who simply do not wish to outsource this part of their pipeline. With a desire to utilize speech recognition in the development of Danish technologies might be hindered by cost barriers.Īs such DanSpeech can be used commercially by companies without the resources to develop their own speech recognition The following code snippet demonstrates how to extract and print all the PoS tags from a given sentence: from textblob import TextBlob sentence 'The cat is sleeping.' blob TextBlob (sentence) for word, tag in blob.tags: print (word, '-', tag) In the code above, we. And without an easy-to-use, and free system, innovative spirits Now, lets explore the basic implementation of PoS tagging using TextBlob. Speech recognition will inevitably be a big part of future IT innovations. Systems are not continually out-shined by English systems. We believe that an open-source solution can play an important role in ensuring that Danish speech recognition Therefore we decided to developĪn open-source, and easy-to-use automatic speech recognition system for Danish. We believe that speech recognition in Danish should be freely available for everyone to use. Pre-trained models of varying sizes and complexities. Not perfect, and results are conditioned on specific use-cases.Īn easy-to-use Recognizer that supports different use-cases for Danish speech recognition. While DanSpeech models perform state-of-the-art speech recognition in Danish, performance is To achieve the best results, DanSpeech provides language models trained on a large danish corpus as part of the released package. The models may be combined with a language model through beam-search decoding The models are trained with various data agumentations to multiply the rather small amount of public speech recognition It was developed as part of a Master’s thesis at DTU computeīy Martin Carsten Nielsen and Rasmus Arpe Fogh Jensen, supervised by Professor Lars Kai Hansen.Īll DanSpeech models are end-to-end DeepSpeech 2 models, trained on danish data with a CTC loss. DanSpeech is an open-source Danish speech recognition (speech-to-text) python package based on the
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