Marco Tagliasacchi
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TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition
Xuankai Chang
Neil Zeghidour
Interspeech 2023
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We present TokenSplit, a speech separation model that acts on discrete token sequences. The model is trained on multiple tasks simultaneously: separate and transcribe each speech source, and generate speech from text. The model operates on transcripts and audio token sequences and achieves multiple tasks through masking of inputs. The model is a sequence-to-sequence encoder-decoder model that uses the Transformer architecture. We also present a "refinement" version of the model that predicts enhanced audio tokens from the audio tokens of speech separated by a conventional separation model. Using both objective metrics and subjective MUSHRA listening tests, we show that our model achieves excellent performance in terms of separation, both with or without transcript conditioning. We also measure the automatic speech recognition (ASR) performance and provide audio samples of speech synthesis to demonstrate the additional utility of our model.
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Disentangling speech from surroundings with neural embeddings
Malcolm Slaney
Neil Zeghidour
ICASSP 2023 (2023)
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We present a method to separate speech signals from noisy environments in the embedding space of a neural audio codec. We introduce a new training procedure that allows our model to produce structured encodings of audio waveforms given by embedding vectors, where one part of the embedding vector represents the speech signal, and the rest represent the environment. We achieve this by partitioning the embeddings of different input waveforms and training the model to faithfully reconstruct audio from mixed partitions, thereby ensuring each partition encodes a separate audio attribute. As use cases, we demonstrate the separation of speech from background noise or from reverberation characteristics. Our method also allows for targeted adjustments of the audio output characteristics.
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MusicLM: Generating Music From Text
Andrea Agostinelli
Mauro Verzetti
Antoine Caillon
Qingqing Huang
Neil Zeghidour
Christian Frank
under review (2023)
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We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consistent over several minutes. Our experiments show that MusicLM outperforms previous systems both in audio quality and adherence to the text description. Moreover, we demonstrate that MusicLM can be conditioned on both text and a melody in that it can transform whistled and hummed melodies according to the style described in a text caption. To support future research, we publicly release MusicCaps, a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts.
Further links: samples, MusicCaps dataset
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LMCODEC: A LOW BITRATE SPEECH CODEC WITH CAUSAL TRANSFORMER MODELS
Bastiaan Kleijn
Michael Chinen
Neil Zeghidour
Teerapat Jenrungrot
ICASSP 2023 (2023)
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We introduce LMCodec, a fully-causal neural speech codec that provides high quality at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec first trains a Transformer language model to predict the fine tokens from the coarse ones in a generative fashion, allowing for the transmission of fewer codes. A second Transformer predicts the uncertainty of the next codes given the past transmitted codes, and is used to perform conditional entropy coding. A MUSHRA subjective test was conducted and shows that the quality is comparable to reference codecs at higher bitrates. Example audio is available at https://google.github.io/chrome-media-audio-papers/publications/lmcodec.
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We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input. We demonstrate that the model performs speech inpainting with the appropriate content, while maintaining speaker identity, prosody and recording environment conditions, and generalizing to unseen speakers. Our approach significantly outperforms baselines constructed using adaptive TTS, as judged by human raters in side-by-side preference and MOS tests.
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Real-time Speech Frequency Bandwidth Extension
Dominik Roblek
Oleg Rybakov
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (to appear)
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In this paper we propose a lightweight model that performs frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz, while restoring the high frequency content to a level that is indistinguishable from the original samples at 16kHz. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which adopts a combination of feature losses and adversarial losses to reconstruct an enhanced version of the input speech. In addition, we propose a version of SEANet that can be deployed on device in streaming mode, achieving an architecture latency of 16ms. When profiled on a single mobile CPU, processing one 16ms frame takes only 1.5ms, so that the total latency is compatible with a deployment in bi-directional voice communication systems.
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Semi-supervised batch active learning via bilevel optimization
Andreas Krause
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (to appear)
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\emph{Active learning} is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel \emph{batch acquisition strategy} for active learning in the setting when the model training is performed in a \emph{semi-supervised} manner. We formulate our approach as a \emph{data summarization} problem via \emph{bilevel optimization}, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in \emph{keyword detection} tasks in the regime when only \emph{few labeled samples} are available.
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One-shot conditional audio filtering of arbitrary sounds
Dominik Roblek
2021 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE (to appear)
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We consider the problem of separating a particular sound source from a single-channel mixture, based on
only a short sample of the target source. Using \tuneenv, a waveform-to-waveform neural network architecture, we are able to train a model in an
entirely unsupervised way.
Using a sound source encoder model which is learned jointly with the source separation network, the trained model can be ``configured'' to filter arbitrary sound sources, even ones that it has not seen during training. Evaluated on the FSD50k dataset, our model obtains an SI-SDR improvement of 9.6 dB, for mixtures of two sounds.
When trained on Librispeech, our model achieves an SI-SDR improvement of 12.3 dB when separating one voice from a mixture of two speakers.
Moreover, we show that the representation learned by the sound source encoder clusters acoustically similar sounds together in the embedding space, even if it is trained without using any labels.
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MicAugment: One-shot Microphone Style Transfer
2021 IEEE International Conference on Acoustics, Speech and Signal Processing (to appear)
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A critical aspect for the successful deployment of audio-based models ``in-the-wild'' is the robustness to the transformations introduced by heterogeneous microphones. In this work we propose a method that is able to perform \emph{one-shot microphone style} transfer. Given only a \emph{few seconds} of audio recorded by a target device, \emph{MicAugment} identifies the transformations associated to the microphone and uses the learned transformations to synthesize audio as if it were recorded by that device. We show that our method can successfully apply the style of a target microphone and that it significantly increases model robustness to microphone variability when used as \emph{data augmentation} in downstream tasks.
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Self-Supervised Learning from Automatically Separated Sound Scenes
Xavier Serra
WASPAA 2021 (2021)
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Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings. The association of these constituent sound events with their mixture and each other is semantically-constrained: the sound scene contains the union of source classes and not all classes naturally co-occur. With this motivation, this paper explores the use of unsupervised automatic sound separation to decompose unlabeled sound scenes into multiple semantically-linked views for use in self-supervised contrastive learning. We find that learning to associate input mixtures with their automatically separated outputs yields stronger representations than past approaches that use the mixtures alone. Further, we discover that optimal source separation is not required for successful contrastive learning by demonstrating that a range of separation system convergence states all lead to useful and often complementary example transformations. Our best system incorporates these unsupervised separation models into a single augmentation front-end and jointly optimizes similarity maximization and coincidence prediction objectives across the views. The result is an unsupervised audio representation that rivals state-of-the-art alternatives on the established shallow AudioSet classification benchmark.
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