Diffusion Based Audio Enhancement With Multiband Diffusion

via “diffusion-based audio enhancement with multiband diffusion”

Meta's library for music and audio generation.

Unique: Applies diffusion-based refinement independently to frequency bands, enabling targeted enhancement of specific spectral regions while maintaining overall audio structure. Operates as a post-processing stage compatible with any audio source, not just AudioCraft-generated content.

vs others: More effective at artifact reduction than traditional filtering; enables quality improvements without model retraining. Slower than alternatives but produces higher perceptual quality.

via “diffusion-based waveform generation with conditional synthesis”

text-to-speech model by undefined. 3,08,930 downloads.

Unique: Uses diffusion-based waveform generation instead of vocoder-based approaches, eliminating the need for separate vocoder models and enabling end-to-end differentiable synthesis. The conditional diffusion architecture allows simultaneous conditioning on linguistic content and speaker identity through cross-attention, producing more coherent speaker-consistent speech than cascade approaches.

vs others: More unified than Tacotron2+Vocoder pipelines (eliminates vocoder mismatch); produces more natural prosody than autoregressive models due to diffusion's global context; more flexible than flow-based models for future prosody control extensions, though slower than both alternatives.

via “diffusion-based acoustic refinement with configurable denoising steps”

A high quality multi-voice text-to-speech library

Unique: Uses diffusion-based iterative denoising in mel spectrogram space rather than waveform space, making refinement computationally efficient while capturing acoustic details. Configurable step count enables explicit quality/speed tradeoff without model retraining.

vs others: More efficient than waveform-space diffusion (like DiffWave) because mel spectrograms are lower-dimensional; more flexible than fixed-quality systems because step count is tunable; captures acoustic details better than single-pass refinement networks.

via “diffusion models for audio and video generation”

Python materials for the online course on diffusion models by [@huggingface](https://github.com/huggingface).

via “latent-space diffusion sampling for audio generation”

* ⭐ 03/2023: [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (USM)](https://arxiv.org/abs/2303.01037)

Unique: Operates diffusion in CLAP embedding-derived latent space rather than raw audio space, enabling single-GPU training and efficient inference while maintaining audio quality through learned latent representations

vs others: More computationally efficient than raw waveform diffusion (typical in prior TTA systems) while maintaining quality by learning audio latent compositions in pretrained embedding space, reducing training time and inference latency

via “diffusion-based audio quality optimization”

via “diffusion-based audio synthesis and variation”