From d3e76b5ffa705af7c66a1b1531a2f50be4a42bf1 Mon Sep 17 00:00:00 2001
From: Bai-YT Description
We use the CLAP loss as an example, confirming that end-to-end fine-tuning further boosts the generation quality.
- Please join us at INTERSPEECH 2024 at Kos Island, Greece! + Please check out our poster at + INTERSPEECH 2024 at Kos Island, Greece!
@@ -113,40 +114,36 @@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. +
+ + ++
Guidance Method | +CFG Weight | +Teacher Solver | +Noise Schedule | +FAD ↓ | +FD ↓ | +KLD ↓ | +
---|---|---|---|---|---|---|
Unguided | +1 | +DDIM | +Uniform | +13.48 | +45.75 | +2.409 | +
External CFG | +3 | +DDIM | +Uniform | +8.565 | +38.67 | +2.015 | +
Heun | +Karras | +7.421 | +39.36 | +1.976 | +||
CFG Distillation with Fixed Weight |
+ 3 | +Heun | +Karras | +5.702 | +33.18 | +1.494 | +
Uniform | +3.859 | +27.79 | +1.421 | +|||
CFG Distillation with Random Weight |
+ 4 | +Heun | +Uniform | +3.180 | +27.92 | +1.394 | +
6 | +2.975 | +28.63 | +1.378 | +
@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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