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*[Sound Classification](#Application Specific Audio Models)
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*[Pitch Estimation](#PitchEstimation)
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*[Speech to Text](https://github.com/matlab-deep-learning/MATLAB-Deep-Learning-Model-Hub#speech-to-text-)
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@@ -41,27 +41,29 @@ These networks have been trained on more than a million images and can classify
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**Models available in MATLAB:**
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**Note 1: Since R2024a, please use the [imagePretrainedNetwork](https://www.mathworks.com/help/deeplearning/ref/imagepretrainednetwork.html) function instead and specify the pretrained model**.
|[BERT](https://github.com/matlab-deep-learning/transformer-models#bert-and-finbert)| Feature Extraction (Sentence and Word embedding), Text Classification, Token Classification, Masked Language Modeling, Question Answering |390 |[GitHub](https://github.com/matlab-deep-learning/transformer-models#bert-and-finbert)|
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|[BERT](https://www.mathworks.com/help/textanalytics/ref/bert.html)| Feature Extraction (Sentence and Word embedding), Text Classification, Token Classification, Masked Language Modeling, Question Answering |390 |[GitHub](https://github.com/matlab-deep-learning/transformer-models#bert-and-finbert) <br /> [Doc](https://www.mathworks.com/help/textanalytics/ref/bert.html)|
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|[all-MiniLM-L6-v2](https://www.mathworks.com/matlabcentral/fileexchange/156399-text-analytics-toolbox-model-for-all-minilm-l6-v2-network)| Document Embedding, Clustering, Information Retrieval |80 |[Doc](https://www.mathworks.com/matlabcentral/fileexchange/156399-text-analytics-toolbox-model-for-all-minilm-l6-v2-network)|
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|[all-MiniLM-L12-v2](https://www.mathworks.com/matlabcentral/fileexchange/156394-text-analytics-toolbox-model-for-all-minilm-l12-v2-network)| Document Embedding, Clustering, Information Retrieval |120 |[Doc](https://www.mathworks.com/matlabcentral/fileexchange/156394-text-analytics-toolbox-model-for-all-minilm-l12-v2-network)|
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**Application Specific Transformers**
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@@ -248,30 +252,31 @@ Inputs are sequences of text, outputs are text feature embeddings.
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## Audio Embeddings <aname="AudioEmbeddings"/>
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Audio Embedding pretrained models have already learned to extract powerful and informative features from audio signals. Use them as a starting point to learn a new task using transfer learning.
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Audio embedding pretrained models have already learned to extract powerful and informative features from audio signals. Use them as a starting point to learn a new task using transfer learning.
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Inputs are Audio signals, outputs are audio feature embeddings.
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Inputs are audio signals, outputs are audio feature embeddings.
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**Note 2: Since R2024a, please use the [audioPretrainedNetwork](https://www.mathworks.com/help/audio/ref/audiopretrainednetwork.html) function instead and specify the pretrained model**.
Speechto text pretrained models take an audio input and translate it into a text output. They are useful in digitizating audio files for downstream text processing tasks such as text summarization and sentiment analysis.
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Speech-to-text models provide a fast, efficient method to convert spoken language into written text, enhancing accessibility for individuals with disabilities, enabling downstream tasks like text summarization and sentiment analysis, and streamlining documentation processes. As a key element of human-machine interfaces, including personal assistants, it allows for natural and intuitive interactions, enabling machines to understand and execute spoken commands, improving usability and broadening inclusivity across various applications.
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