{"id":18293461,"url":"https://github.com/archinetai/audio-encoders-pytorch","last_synced_at":"2025-07-11T19:36:44.284Z","repository":{"id":62108819,"uuid":"557846135","full_name":"archinetai/audio-encoders-pytorch","owner":"archinetai","description":"A collection of audio autoencoders, in PyTorch.","archived":false,"fork":false,"pushed_at":"2023-03-07T00:08:24.000Z","size":81,"stargazers_count":40,"open_issues_count":2,"forks_count":6,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-03-21T03:34:23.974Z","etag":null,"topics":["artificial-intelligence","audio","deep-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/archinetai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-10-26T12:18:29.000Z","updated_at":"2025-02-21T13:19:04.000Z","dependencies_parsed_at":"2023-02-09T23:15:19.196Z","dependency_job_id":null,"html_url":"https://github.com/archinetai/audio-encoders-pytorch","commit_stats":null,"previous_names":[],"tags_count":22,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/archinetai%2Faudio-encoders-pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/archinetai%2Faudio-encoders-pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/archinetai%2Faudio-encoders-pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/archinetai%2Faudio-encoders-pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/archinetai","download_url":"https://codeload.github.com/archinetai/audio-encoders-pytorch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247226171,"owners_count":20904464,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","audio","deep-learning"],"created_at":"2024-11-05T14:24:39.764Z","updated_at":"2025-04-05T11:31:02.722Z","avatar_url":"https://github.com/archinetai.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Audio Encoders - PyTorch\n\nA collection of audio autoencoders, in PyTorch. Pretrained models can be found at [`archisound`](https://github.com/archinetai/archisound).\n\n## Install\n\n```bash\npip install audio-encoders-pytorch\n```\n\n[![PyPI - Python Version](https://img.shields.io/pypi/v/audio-encoders-pytorch?style=flat\u0026colorA=black\u0026colorB=black)](https://pypi.org/project/audio-encoders-pytorch/)\n\n\n## Usage\n\n### AutoEncoder1d\n```py\nfrom audio_encoders_pytorch import AutoEncoder1d\n\nautoencoder = AutoEncoder1d(\n    in_channels=2,              # Number of input channels\n    channels=32,                # Number of base channels\n    multipliers=[1, 1, 2, 2],   # Channel multiplier between layers (i.e. channels * multiplier[i] -\u003e channels * multiplier[i+1])\n    factors=[4, 4, 4],          # Downsampling/upsampling factor per layer\n    num_blocks=[2, 2, 2]        # Number of resnet blocks per layer\n)\n\nx = torch.randn(1, 2, 2**18)    # [1, 2, 262144]\nx_recon = autoencoder(x)        # [1, 2, 262144]\n```\n\n### Discriminator1d\n```py\nfrom audio_encoders_pytorch import Discriminator1d\n\ndiscriminator = Discriminator1d(\n    in_channels=2,                  # Number of input channels\n    channels=32,                    # Number of base channels\n    multipliers=[1, 1, 2, 2],       # Channel multiplier between layers (i.e. channels * multiplier[i] -\u003e channels * multiplier[i+1])\n    factors=[8, 8, 8],              # Downsampling factor per layer\n    num_blocks=[2, 2, 2],           # Number of resnet blocks per layer\n    use_loss=[True, True, True]     # Whether to use this layer as GAN loss\n)\n\nwave_true = torch.randn(1, 2, 2**18)\nwave_fake = torch.randn(1, 2, 2**18)\n\nloss_generator, loss_discriminator = discriminator(wave_true, wave_fake)\n# tensor(0.613949, grad_fn=\u003cMeanBackward0\u003e) tensor(0.097330, grad_fn=\u003cMeanBackward0\u003e)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farchinetai%2Faudio-encoders-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farchinetai%2Faudio-encoders-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farchinetai%2Faudio-encoders-pytorch/lists"}