Dinesh Manocha, Balaji Srinivasan, Sanjoy Chowdhury, Sayan Nag, † Joseph
We lifted 12 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| schowdhury671/melfusion | canonical | 12 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| Normalize | Ran | schowdhury671/melfusion/audioldm/variational_autoencoder/modules.py pointer only (licence: NONE) · get_code("9fcdaa6e423e8aa7") |
| disabled_train | Ran | schowdhury671/melfusion/audioldm/ldm.py pointer only (licence: NONE) · get_code("4cb732f513d69dfd") |
| exists | Ran | schowdhury671/melfusion/audioldm/utils.py pointer only (licence: NONE) · get_code("608e364a9d2376a3") |
| get_bit_depth | Ran | schowdhury671/melfusion/audioldm/utils.py pointer only (licence: NONE) · get_code("33e4f7a8bf80ee3a") |
| get_duration | Ran | schowdhury671/melfusion/audioldm/utils.py pointer only (licence: NONE) · get_code("159fc130dc64d332") |
| get_image_latents | Ran | schowdhury671/melfusion/models.py pointer only (licence: NONE) · get_code("1586badee8c8ddb7") |
| get_padding | Ran | schowdhury671/melfusion/audioldm/hifigan/models.py pointer only (licence: NONE) · get_code("a26f85d7c72ef39a") |
| get_timestep_embedding | Ran | schowdhury671/melfusion/audioldm/variational_autoencoder/modules.py pointer only (licence: NONE) · get_code("cb49209c125de1b4") |
| load_model | Ran | schowdhury671/melfusion/models.py pointer only (licence: NONE) · get_code("bb989094ba0ffe5f") |
| make_batch_for_text_to_audio | Ran | schowdhury671/melfusion/audioldm/pipeline.py pointer only (licence: NONE) · get_code("409a71c470a245cd") |
| nonlinearity | Ran | schowdhury671/melfusion/audioldm/variational_autoencoder/modules.py pointer only (licence: NONE) · get_code("3137073275f8c21a") |
| round_up_duration | Ran | schowdhury671/melfusion/audioldm/pipeline.py pointer only (licence: NONE) · get_code("444fd0303f1aff5a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media, ranging from movies to social media posts. Machine learning models that can synthesize music are predominantly conditioned on textual descriptions of it. Inspired by how musicians compose music not just from a movie script, but also through visualizations, we propose MELFUSION, a model that can effectively use cues from a textual description and the corresponding image to synthesize music. MELFUSION is a text-to-music diffusion model with a novel "visual synapse", which effectively infuses the semantics from the visual modality into the generated music. To facilitate research in this area, we introduce a new dataset MeLBench, and propose a new evaluation metric IMSM. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music, measured both objectively and subjectively, with a relative gain of up to 67.98% on the FAD score. We hope that our work will gather attention to this pragmatic, yet relatively under-explored research area.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.04673")
get_code_for_paper("2406.04673")
have("2406.04673")
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