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Rithesh Kumar

Alumni

Publications

Chunked Autoregressive GAN for Conditional Waveform Synthesis
Max Morrison
Rithesh Kumar
Kundan Kumar
Prem Seetharaman
Aaron Courville
Yoshua Bengio
Conditional waveform synthesis models learn a distribution of audio waveforms given conditioning such as text, mel-spectrograms, or MIDI. Th… (voir plus)ese systems employ deep generative models that model the waveform via either sequential (autoregressive) or parallel (non-autoregressive) sampling. Generative adversarial networks (GANs) have become a common choice for non-autoregressive waveform synthesis. However, state-of-the-art GAN-based models produce artifacts when performing mel-spectrogram inversion. In this paper, we demonstrate that these artifacts correspond with an inability for the generator to learn accurate pitch and periodicity. We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression. We discuss the inductive bias that autoregression provides for learning the relationship between instantaneous frequency and phase, and show that this inductive bias holds even when autoregressively sampling large chunks of the waveform during each forward pass. Relative to prior state-of-the-art GAN-based models, our proposed model, Chunked Autoregressive GAN (CARGAN) reduces pitch error by 40-60%, reduces training time by 58%, maintains a fast generation speed suitable for real-time or interactive applications, and maintains or improves subjective quality.
2022-04-24
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
NU-GAN: High resolution neural upsampling with GAN
Rithesh Kumar
Kundan Kumar
Vicki Anand
Yoshua Bengio
Aaron Courville
In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an… (voir plus) important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset.
2020-10-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Maximum Entropy Generators for Energy-Based Models
Rithesh Kumar
Anirudh Goyal
Aaron Courville
Yoshua Bengio
Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient. In t… (voir plus)his work, we propose learning both the energy function and an amortized approximate sampling mechanism using a neural generator network, which provides an efficient approximation of the log-likelihood gradient. The resulting objective requires maximizing entropy of the generated samples, which we perform using recently proposed nonparametric mutual information estimators. Finally, to stabilize the resulting adversarial game, we use a zero-centered gradient penalty derived as a necessary condition from the score matching literature. The proposed technique can generate sharp images with Inception and FID scores competitive with recent GAN techniques, does not suffer from mode collapse, and is competitive with state-of-the-art anomaly detection techniques.
2019-01-23
ArXiv (prépublication)
doi.org
arxiv.org
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Kundan Kumar
Rithesh Kumar
Thibault De Boissière
Lucas Gestin
Wei Zhen Teoh
Jose Sotelo
Alexandre De Brébisson
Yoshua Bengio
Aaron Courville
2018-12-31
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (publié)
arxiv.org
arxiv.org
Harmonic Recomposition using Conditional Autoregressive Modeling
Kyle Kastner
Rithesh Kumar
Tim Cooijmans
Aaron Courville
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(20… (voir plus)17). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.
2018-11-17
ArXiv (prépublication)
arxiv.org
arxiv.org
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models
Rithesh Kumar
Anirudh Goyal
Aaron Courville
Yoshua Bengio
2018-09-26
(publié)
openreview.net
openreview.net
ObamaNet: Photo-realistic lip-sync from text
Rithesh Kumar
Jose Sotelo
Kundan Kumar
Alexandre De Brébisson
Yoshua Bengio
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Co… (voir plus)ntrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to generate the video frames conditioned on the keypoints.
2017-12-05
ArXiv (prépublication)
arxiv.org
arxiv.org
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Soroush Mehri
Kundan Kumar
Ishaan Gulrajani
Rithesh Kumar
Shubham Jain
Jose Sotelo
Aaron Courville
Yoshua Bengio
In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our m… (voir plus)odel which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
2016-12-31
ICLR.cc/2017/conference (poster)
openreview.net
openreview.net