speechbrain.lobes.models

Package defining neural netword models (CRDNN, Xvectors …)

`speechbrain.lobes.models.CRDNN`	A combination of Convolutional, Recurrent, and Fully-connected networks.
`speechbrain.lobes.models.ContextNet`	The SpeechBrain implementation of ContextNet by https://arxiv.org/pdf/2005.03191.pdf
`speechbrain.lobes.models.ECAPA_TDNN`	A popular speaker recognition and diarization model.
`speechbrain.lobes.models.ESPnetVGG`	This lobes replicate the encoder first introduced in ESPNET v1
`speechbrain.lobes.models.EnhanceResnet`	Wide ResNet for Speech Enhancement.
`speechbrain.lobes.models.HifiGAN`	Neural network modules for the HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
`speechbrain.lobes.models.MetricGAN`	Generator and discriminator used in MetricGAN
`speechbrain.lobes.models.MetricGAN_U`	Generator and discriminator used in MetricGAN-U
`speechbrain.lobes.models.RNNLM`	Implementation of a Recurrent Language Model.
`speechbrain.lobes.models.Tacotron2`	Neural network modules for the Tacotron2 end-to-end neural Text-to-Speech (TTS) model
`speechbrain.lobes.models.VanillaNN`	Vanilla Neural Network for simple tests.
`speechbrain.lobes.models.Xvector`	A popular speaker recognition and diarization model.
`speechbrain.lobes.models.conv_tasnet`	Implementation of a popular speech separation model.
`speechbrain.lobes.models.convolution`	This is a module to ensemble a convolution (depthwise) encoder with or without residule connection.
`speechbrain.lobes.models.dual_path`	Library to support dual-path speech separation.
`speechbrain.lobes.models.fairseq_wav2vec`	This lobe enables the integration of fairseq pretrained wav2vec models.
`speechbrain.lobes.models.resepformer`	Library for the Reseource-Efficient Sepformer.
`speechbrain.lobes.models.segan_model`	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).
`speechbrain.lobes.models.wav2vec`	Components necessary to build a wav2vec 2.0 architecture following the original paper: https://arxiv.org/abs/2006.11477.

`speechbrain.lobes.models.g2p`
`speechbrain.lobes.models.transformer`	High level processing blocks.