Danilo Mandic, Zehua Chen, Haohe Liu, Yi Yuan, Xinhao Mei, Xubo Liu, Wenwu Wang, Mark Plumbley, Vae Encoder
We lifted 21 functions out of this paper's own repositories and ran 15 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| haoheliu/AudioLDM | canonical | 5 of 9 |
| haoheliu/audioldm_eval | pwc_unofficial | 9 of 11 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| DiagonalGaussianDistribution | Ran | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("2cda99b6def85873") |
| clip_bce | Ran | haoheliu/audioldm_eval/audioldm_eval/feature_extractors/panns/losses.py code served (permissive licence) · get_code("97d45ba74474fdfc") |
| disabled_train | Ran | this paper's copy was not recorded; identical code first harvested from zhouc20/LatentGraphDiffusion pointer only · get_code("4cb732f513d69dfd") |
| extract_into_tensor | Ran | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("8971042e12016a60") |
| get_loss_func | Ran | haoheliu/audioldm_eval/audioldm_eval/feature_extractors/panns/losses.py code served (permissive licence) · get_code("e424ea90cdf3fd97") |
| get_mel_from_wav | Ran | haoheliu/audioldm_eval/audioldm_eval/audio/tools.py code served (permissive licence) · get_code("af0a67f05ca8144d") |
| griffin_lim | Ran | haoheliu/audioldm_eval/audioldm_eval/audio/audio_processing.py code served (permissive licence) · get_code("9f9ec26d4cdfdf7d") |
| interpolate_bilinear_2d_like_tensorflow1x | Ran | haoheliu/audioldm_eval/audioldm_eval/feature_extractors/inception3.py code served (permissive licence) · get_code("21e50186ab33dd24") |
| load_json | Ran | haoheliu/audioldm_eval/audioldm_eval/audio/tools.py code served (permissive licence) · get_code("777e661635ced528") |
| load_module2model | Ran | haoheliu/audioldm_eval/audioldm_eval/feature_extractors/melception_audioset.py code served (permissive licence) · get_code("ffc354ebaecde324") |
| load_pickle | Ran | haoheliu/audioldm_eval/audioldm_eval/audio/tools.py code served (permissive licence) · get_code("78222542af18c6c5") |
| make_ddim_sampling_parameters | Ran | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("3ee640131c9d4362") |
| make_ddim_timesteps | Ran | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("0ea4e960ea54514c") |
| noise_like | Ran | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("2d7200ac60d9a4bd") |
| pad_short_audio | Ran | haoheliu/audioldm_eval/audioldm_eval/datasets/load_mel.py code served (permissive licence) · get_code("e0e3ae45a19356fa") |
| DDIMSampler | Not yet run | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("332deacf272549e4") |
| DDPM | Not yet run | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("b7521b6c520cc691") |
| DiffusionWrapper | Not yet run | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("bb9a12b66a9694f1") |
| LatentDiffusion | Not yet run | haoheliu/AudioLDM/audioldm/ldm.py pointer only (licence: NOASSERTION) · get_code("1d0e58f8ba043419") |
| dynamic_range_compression | Not yet run | haoheliu/audioldm_eval/audioldm_eval/audio/audio_processing.py code served (permissive licence) · get_code("0b80ec09e3ba47ae") |
| load_npy_data | Not yet run | haoheliu/audioldm_eval/audioldm_eval/datasets/load_mel.py code served (permissive licence) · get_code("98e449b3a6b45350") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Text-to-audio (TTA) systems have recently gained attention for their ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn continuous audio representations from contrastive language-audio pretraining (CLAP) embeddings. The pretrained CLAP models enable us to train LDMs with audio embeddings while providing text embeddings as the condition during sampling. By learning the latent representations of audio signals without modelling the cross-modal relationship, Au-dioLDM improves both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves stateof-the-art TTA performance compared to other open-sourced systems, measured by both objective and subjective metrics. AudioLDM is also the first TTA system that enables various textguided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at https://audioldm. github.io.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.12503")
get_code_for_paper("2301.12503")
have("2301.12503")
Connect an agent — have() is free.