From 3f553f0ba0f65986e6b168799c9fc767bae6f937 Mon Sep 17 00:00:00 2001 From: Bai-YT Date: Sat, 17 Aug 2024 23:48:17 -0700 Subject: [PATCH] TMLR Paper --- consistency_tta/index.html | 144 ++++++++++++++++++++++++++++++++----- consistency_tta/poster.pdf | Bin 1579720 -> 2098123 bytes consistency_tta/styles.css | 46 ++++++++++-- index.html | 25 +++++-- publications.html | 52 +++++++------- 5 files changed, 214 insertions(+), 53 deletions(-) diff --git a/consistency_tta/index.html b/consistency_tta/index.html index a116588..2b2e65d 100644 --- a/consistency_tta/index.html +++ b/consistency_tta/index.html @@ -97,38 +97,144 @@

Main Experiment Results

Our method reduce the computation of the core step of diffusion-based text-to-audio generation by a factor of 400 and enables on-device generation, while observing minimal performance degradation in - Fréchet Audio Distance (FAD), Fréchet Distance (FD), KL Divergence, and CLAP Scores. + Fréchet Audio Distance (FAD), Fréchet Distance (FD), KL Divergence, and CLAP Scores.
+ Generation Time is the time in minutes to generate the entire validation set (882 samples).
+ ↑: higher is better; ↓: lower is better.

- - - + + + + + - - - - + + + + + + + + + + + + - - - + + + + + - - - + + + + + + + + + + + +
# queries (↓)CLAPT (↑) CLAPA (↑)FAD (↓) FD (↓) KLD (↓)Model Queries
Generation Time
Subjective Quality
Subjective Text Align
CLAPT
CLAPA
FAD
FD
KLD
Diffusion (Baseline) 40024.57 72.791.908 19.57 1.350
AudioLDM-L (Baseline) 400- -- - -2.08 27.12 1.86
TANGO (Baseline)400 1684.136 4.06424.10 72.851.631 20.11 1.362
Consistency + CLAP FT (Ours) 124.69 72.542.406 20.97 1.358ConsistencyTTA + CLAP-FT1 2.33.830 4.06424.69 72.542.406 20.97 1.358
Consistency (Ours) 122.50 72.302.575 22.08 1.354ConsistencyTTA1 2.33.902 4.01022.50 72.302.575 22.081.354
Ground Truth -- - -26.71 100- - -

This benchmark demonstrates how our single-step models stack up with previous methods, - most of which mostly require hundreds of generation steps. + most of which requiring hundreds of generation steps. +

+ + +
+

Ablation Studies on Distillation Settings

+

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Guidance MethodCFG WeightTeacher SolverNoise ScheduleFAD ↓FD ↓KLD ↓
Unguided1DDIMUniform13.4845.752.409
External CFG3DDIMUniform8.56538.672.015
HeunKarras7.42139.361.976
CFG Distillation
with Fixed Weight
3HeunKarras5.70233.181.494
Uniform3.85927.791.421
CFG Distillation
with Random Weight
4HeunUniform3.18027.921.394
62.97528.631.378
+ Based on these results, we can conclude that: +

@@ -156,11 +262,11 @@

Human Evaluation

Citing Our Work (BibTeX)

-
@article{bai2023accelerating,
+
@inproceedings{bai2024accelerating,
   author = {Bai, Yatong and Dang, Trung and Tran, Dung and Koishida, Kazuhito and Sojoudi, Somayeh},
-  title = {Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation},
-  journal={arXiv preprint arXiv:2309.10740},
-  year = {2023}
+  title = {ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation},
+  booktitle = {INTERSPEECH},
+  year = {2024}
 }
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-.result-row:nth-of-type(odd) { - background-color: #f7f9fc; +.result-row { + background-color: #e7e9ee; +} +.result-row-2 { + background-color: #ffffff; } .result-row:hover { @@ -279,6 +291,32 @@ tr td:last-child { .result-data { padding: 7px 12px; border-bottom: 1px solid #e7ebef; + background-color: #dfe3f241; + font-size: 1.15em; +} +.result-data-400 { + padding: 7px 12px; + border-bottom: 1px solid #e7ebef; + background-color: #dfe3f241; + font-size: 1.15em; + font-weight: 400; +} +.result-data-2 { + padding: 7px 12px; + border-bottom: 1px solid #e7ebef; + font-size: 1.15em; +} +.result-data-2-400 { + padding: 7px 12px; + border-bottom: 1px solid #e7ebef; + font-size: 1.15em; + font-weight: 400; +} +.result-data-small { + padding: 7px 12px; + border-bottom: 1px solid #e7ebef; + background-color: #dfe3f241; + font-weight: 400; } /* Optional: Add transitions for smoother hover effects */ diff --git a/index.html b/index.html index 38d4702..641e746 100644 --- a/index.html +++ b/index.html @@ -111,10 +111,11 @@

    About

    @@ -123,7 +124,7 @@

    About

    Research interests

    - My interests include generative models (particulaly audio), robust deep learning, (convex) optimization, and controls. + My interests include generative models (particulaly audio/music), robust deep learning, (convex) optimization, and controls.

    Specifically, I enjoy working on ensuring the adversarial robustness of neural networks, addressing @@ -140,15 +141,29 @@

    Research interests

    News

    I have interned at + Adobe, Microsoft, Scale AI, Honda Aircraft Company, and - Tesla, Inc. + Tesla.

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