Cross-Utterance Conditioned VAE for Speech Generation

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech.Comment: 13 pages

Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes. Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins

Controllable music performance synthesis via hierarchical modelling

L’expression musicale requiert le contrôle sur quelles notes sont jouées ainsi que comment elles se jouent. Les synthétiseurs audios conventionnels offrent des contrôles expressifs détaillés, cependant au détriment du réalisme. La synthèse neuronale en boîte noire des audios et les échantillonneurs concaténatifs sont capables de produire un son réaliste, pourtant, nous avons peu de mécanismes de contrôle. Dans ce travail, nous introduisons MIDI-DDSP, un modèle hiérarchique des instruments musicaux qui permet tant la synthèse neuronale réaliste des audios que le contrôle sophistiqué de la part des utilisateurs. À partir des paramètres interprétables de synthèse provenant du traitement différentiable des signaux numériques (Differentiable Digital Signal Processing, DDSP), nous inférons les notes musicales et la propriété de haut niveau de leur performance expressive (telles que le timbre, le vibrato, l’intensité et l’articulation). Ceci donne naissance à une hiérarchie de trois niveaux (notes, performance, synthèse) qui laisse aux individus la possibilité d’intervenir à chaque niveau, ou d’utiliser la distribution préalable entraînée (notes étant donné performance, synthèse étant donné performance) pour une assistance créative. À l’aide des expériences quantitatives et des tests d’écoute, nous démontrons que cette hiérarchie permet de reconstruire des audios de haute fidélité, de prédire avec précision les attributs de performance d’une séquence de notes, mais aussi de manipuler indépendamment les attributs étant donné la performance. Comme il s’agit d’un système complet, la hiérarchie peut aussi générer des audios réalistes à partir d’une nouvelle séquence de notes. En utilisant une hiérarchie interprétable avec de multiples niveaux de granularité, MIDI-DDSP ouvre la porte aux outils auxiliaires qui renforce la capacité des individus à travers une grande variété d’expérience musicale.Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience

Simple and Controllable Music Generation

We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft

AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior

We propose AccoMontage-3, a symbolic music automation system capable of generating multi-track, full-band accompaniment based on the input of a lead melody with chords (i.e., a lead sheet). The system contains three modular components, each modelling a vital aspect of full-band composition. The first component is a piano arranger that generates piano accompaniment for the lead sheet by transferring texture styles to the chords using latent chord-texture disentanglement and heuristic retrieval of texture donors. The second component orchestrates the piano accompaniment score into full-band arrangement according to the orchestration style encoded by individual track functions. The third component, which connects the previous two, is a prior model characterizing the global structure of orchestration style over the whole piece of music. From end to end, the system learns to generate full-band accompaniment in a self-supervised fashion, applying style transfer at two levels of polyphonic composition: texture and orchestration. Experiments show that our system outperforms the baselines significantly, and the modular design offers effective controls in a musically meaningful way

Sparks of Large Audio Models: A Survey and Outlook

This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio processing, with its diverse signal representations and a wide range of sources--from human voices to musical instruments and environmental sounds--poses challenges distinct from those found in traditional Natural Language Processing scenarios. Nevertheless, \textit{Large Audio Models}, epitomized by transformer-based architectures, have shown marked efficacy in this sphere. By leveraging massive amount of data, these models have demonstrated prowess in a variety of audio tasks, spanning from Automatic Speech Recognition and Text-To-Speech to Music Generation, among others. Notably, recently these Foundational Audio Models, like SeamlessM4T, have started showing abilities to act as universal translators, supporting multiple speech tasks for up to 100 languages without any reliance on separate task-specific systems. This paper presents an in-depth analysis of state-of-the-art methodologies regarding \textit{Foundational Large Audio Models}, their performance benchmarks, and their applicability to real-world scenarios. We also highlight current limitations and provide insights into potential future research directions in the realm of \textit{Large Audio Models} with the intent to spark further discussion, thereby fostering innovation in the next generation of audio-processing systems. Furthermore, to cope with the rapid development in this area, we will consistently update the relevant repository with relevant recent articles and their open-source implementations at https://github.com/EmulationAI/awesome-large-audio-models.Comment: work in progress, Repo URL: https://github.com/EmulationAI/awesome-large-audio-model

Generative models for music using transformer architectures

openThis thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language. A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered. The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions. Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production. This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN. Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments.This thesis focus on growth and impact of Transformes architectures which are mainly used for Natural Language Processing tasks for Audio generation. We think that music, with its notes, chords, and volumes, is a language. You could think of symbolic representation of music as human language. A brief sound synthesis history which gives basic foundation for modern AI-generated music models is mentioned . The most recent in AI-generated audio is carefully studied and instances of AI-generated music is told in many contexts. Deep learning models and their applications to real-world issues are one of the key subjects that are covered. The main areas of interest include transformer-based audio generation, including the training procedure, encoding and decoding techniques, and post-processing stages. Transformers have several key advantages, including long-term consistency and the ability to create minute-long audio compositions. Numerous studies on the various representations of music have been explained, including how neural network and deep learning techniques can be used to apply symbolic melodies, musical arrangements, style transfer, and sound production. This thesis largely focuses on transformation models, but it also recognises the importance of numerous AI-based generative models, including GAN. Overall, this thesis enhances generative models for music composition and provides a complete understanding of transformer design. It shows the possibilities of AI-generated sound synthesis by emphasising the most current developments