31 citations · 48 across the 4 of their papers we have counts for
6 papers
Mixer-TTS: non-autoregressive, fast and compact text-to-speech model conditioned on language model embeddings
Oktai Tatanov, Stanislav Beliaev, Boris Ginsburg
This paper describes Mixer-TTS, a non-autoregressive model for mel-spectrogram generation. The model is based on the MLP-Mixer architecture adapted for speech synthesis. The basic…
TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction
Stanislav Beliaev, Boris Ginsburg
We propose TalkNet, a non-autoregressive convolutional neural model for speech synthesis with explicit pitch and duration prediction. The model consists of three feed-forward convo…
ConVoice: Real-Time Zero-Shot Voice Style Transfer with Convolutional Network
Yurii Rebryk, Stanislav Beliaev
We propose a neural network for zero-shot voice conversion (VC) without any parallel or transcribed data. Our approach uses pre-trained models for automatic speech recognition (ASR…
TalkNet: Fully-Convolutional Non-Autoregressive Speech Synthesis Model
Stanislav Beliaev, Yurii Rebryk, Boris Ginsburg
We propose TalkNet, a convolutional non-autoregressive neural model for speech synthesis. The model consists of two feed-forward convolutional networks. The first network predicts…
QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions
Samuel Kriman, Stanislav Beliaev, Boris Ginsburg +6
We propose a new end-to-end neural acoustic model for automatic speech recognition. The model is composed of multiple blocks with residual connections between them. Each block cons…
NeMo: a toolkit for building AI applications using Neural Modules
Oleksii Kuchaiev, Jason Li, Huyen Nguyen +11
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, c…