speechbrain.lobes.models
Package defining neural netword models (CRDNN, Xvectors …)
A combination of Convolutional, Recurrent, and Fully-connected networks. |
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This file implements the CNN14 model from https://arxiv.org/abs/1912.10211 |
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The SpeechBrain implementation of ContextNet by https://arxiv.org/pdf/2005.03191.pdf |
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Neural network modules for DIFFWAVE: A VERSATILE DIFFUSION MODEL FOR AUDIO SYNTHESIS |
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A popular speaker recognition and diarization model. |
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This lobes replicate the encoder first introduced in ESPNET v1 |
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Wide ResNet for Speech Enhancement. |
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Neural network modules for the FastSpeech 2: Fast and High-Quality End-to-End Text to Speech synthesis model Authors * Sathvik Udupa 2022 * Pradnya Kandarkar 2023 * Yingzhi Wang 2023 |
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Neural network modules for the HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis |
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This file implements the necessary classes and functions to implement Listen-to-Interpret (L2I) interpretation method from https://arxiv.org/abs/2202.11479v2 |
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Generator and discriminator used in MetricGAN |
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Generator and discriminator used in MetricGAN-U |
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This file implements the necessary classes and functions to implement Posthoc Interpretations via Quantization. |
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Implementation of a Recurrent Language Model. |
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ResNet PreActived for speaker verification |
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Neural network modules for the Tacotron2 end-to-end neural Text-to-Speech (TTS) model |
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Vanilla Neural Network for simple tests. |
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A popular speaker recognition and diarization model. |
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Implementation of a popular speech separation model. |
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This is a module to ensemble a convolution (depthwise) encoder with or without residule connection. |
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Library to support dual-path speech separation. |
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This lobe enables the integration of fairseq pretrained wav2vec models. |
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This lobe enables the integration of huggingface pretrained wav2vec2/hubert/wavlm models. |
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This lobe enables the integration of huggingface pretrained whisper model. |
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Library for the Reseource-Efficient Sepformer. |
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This file contains two PyTorch modules which together consist of the SEGAN model architecture (based on the paper: Pascual et al. https://arxiv.org/pdf/1703.09452.pdf). |
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Components necessary to build a wav2vec 2.0 architecture following the original paper: https://arxiv.org/abs/2006.11477. |
High level processing blocks. |