REVIEW 23 cited by
Stable Audio Open
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Stable Audio Open
read the original abstract
Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.
Forward citations
Cited by 23 Pith papers
-
What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music
Proposes an attribution-aware compensation framework for generative music that derives closed-form payments from catalog-level attribution informativeness and quantifies welfare effects under competition.
-
One-Step Token-to-Waveform Generation with MeanFlow in Latent Space
MeanFlow applied in latent space enables true one-step Token2Wav generation with up to 17x RTF improvement and negligible quality loss versus multi-step baselines.
-
Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
Rectified flows exhibit a universal bell-shaped membership signal along the interpolation path that peaks at a derivable location and enables membership inference attacks.
-
ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics
ArtifactNet extracts codec residuals from spectrograms with a 4M-parameter network to detect AI music at F1=0.9829 and 1.49% FPR on unseen tracks from 22 generators, outperforming larger baselines.
-
Moshi: a speech-text foundation model for real-time dialogue
Moshi is the first real-time full-duplex spoken large language model that casts dialogue as speech-to-speech generation using parallel audio streams and an inner monologue of time-aligned text tokens.
-
Doppelganger: Sound Effects and Their Synthetic Twins
Instance-pair training matches synthetic sound-effect twins to their real sources on unseen events (~80% R@1), while class supervision degrades below the frozen baseline and the mapping stays generator-specific.
-
Qwen-Music Technical Report
Qwen-Music generates high-fidelity vocal songs via 25 Hz semantic tokens, Melody-CoT planning, and DiT rendering, claiming SOTA on 13/16 metrics and expert preference over proprietary systems.
-
Unified Audio Intelligence Without Regressing on Text Intelligence
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
-
Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation
A data-free streaming consistency distillation framework enables single-step autoregressive generation from text-to-music models for real-time interactive use while preserving timbre and rhythm via latent, spectral, a...
-
FSD50K-Solo: Automated Curation of Single-Source Sound Events
A curation pipeline combining diffusion-based synthetic mixtures with a discriminative classifier produces and releases FSD50K-Solo, a single-source subset of FSD50K that matches human expert labels on a test set.
-
FSD50K-Solo: Automated Curation of Single-Source Sound Events
The authors present a scalable curation method that combines diffusion-based mixture synthesis with a discriminative classifier to automatically extract single-source sound events from FSD50K and release the cleaned F...
-
Audio-Omni: Extending Multi-modal Understanding to Versatile Audio Generation and Editing
Audio-Omni unifies audio understanding, generation, and editing in one end-to-end model across domains, backed by a new million-pair AudioEdit dataset, and achieves strong benchmark results.
-
Unified Audio Intelligence Without Regressing on Text Intelligence
A unified 30B MoE audio-text LLM achieves state-of-the-art audio understanding, generation, and speech tasks while preserving text reasoning comparable to its text-only backbone.
-
GPC: Large-Scale Generative Pretraining for Transferable Motor Control
GPC learns a motion vocabulary via Finite Scalar Quantization and end-to-end RL, then trains an autoregressive transformer for next-token control generation, achieving 99.98% motion reproduction success with emergent ...
-
AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation
A distilled multimodal diffusion model generates audio from text, video, or audio in four steps with claimed superior quality and ~25× fewer function evaluations.
-
AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation
AudioX-Turbo distills a Multimodal Diffusion Transformer into a 4-step student model for efficient multimodal anything-to-audio generation, trained on a new 9.2M-sample dataset IF-caps-Pro.
-
Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models
Causal probing of attention in audio separation transformers identifies dual pathways and asynchronous convergence, enabling a training-free Layer-Selective Attention Caching method that reduces self-attention computa...
-
UniVoice: A Unified Model for Speech and Singing Voice Generation
UniVoice is a conditional flow matching model with a Diffusion Transformer backbone that unifies TTS and SVS via modality-specific encoders and a null melody token for speech, achieving 5.26% speech PER and 16.22% sin...
-
Instrumental Text-to-Music Generation with Auxiliary Conditioning Branches
Auxiliary lyric and timbre branches improve instrumental text-to-music generation quality in a controlled DiT setting even with degenerate inputs, outperforming parameter-reallocated depth variants and external baseli...
-
Deterministic Decomposition of Stochastic Generative Dynamics
Stochastic generative dynamics admit a transport-osmotic decomposition of the deterministic field, supporting Bridge Matching for interpretable and tunable generation.
-
Deterministic Decomposition of Stochastic Generative Dynamics
Stochastic generative dynamics are decomposed into transport and osmotic parts via b_t = u_t + d_t, with Bridge Matching proposed to learn the components for controllable sampling.
-
Woosh: A Sound Effects Foundation Model
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
-
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art det...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.