Initial model version

README.md

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+---
+tags:
+- audio
+- diffusion
+- waveform diffusion
+- audio diffusion
+- unet
+- korg
+- triton
+- grime
+---
+# Model Card for tiny-audio-diffusion-percussion
+
+General percussion/drum model for tiny-audio-diffusion, fine-tuned on a dataset of 382 Korg Triton drum samples for 56 epochs. Use with [tiny-audio-diffusion](https://github.com/crlandsc/tiny-audio-diffusion) repo to generate random drum samples of all types.

config.yaml

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+seed: 12345
+train: true
+ignore_warnings: true
+print_config: false
+work_dir: ${hydra:runtime.cwd}
+logs_dir: ${work_dir}${oc.env:DIR_LOGS}
+data_dir: ${work_dir}${oc.env:DIR_DATA}
+ckpt_dir: ${logs_dir}/runs/${now:%Y-%m-%d-%H-%M-%S}
+module: main.module_base
+batch_size: 16
+accumulate_grad_batches: 2
+num_workers: 16
+sampling_rate: 44100
+length: 32768
+channels: 2
+log_every_n_steps: 500
+model:
+  _target_: ${module}.Model
+  lr: 0.0001
+  lr_beta1: 0.95
+  lr_beta2: 0.999
+  lr_eps: 1.0e-06
+  lr_weight_decay: 0.001
+  ema_beta: 0.995
+  ema_power: 0.7
+  model:
+    _target_: main.DiffusionModel
+    net_t:
+      _target_: ${module}.UNetT
+    in_channels: 2
+    channels:
+    - 32
+    - 32
+    - 64
+    - 64
+    - 128
+    - 128
+    - 256
+    - 256
+    factors:
+    - 1
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    items:
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 2
+    - 4
+    - 4
+    attentions:
+    - 0
+    - 0
+    - 0
+    - 0
+    - 0
+    - 1
+    - 1
+    - 1
+    attention_heads: 8
+    attention_features: 64
+datamodule:
+  _target_: main.module_base.Datamodule
+  dataset:
+    _target_: audio_data_pytorch.WAVDataset
+    path: ./data/triton_aug
+    recursive: true
+    sample_rate: ${sampling_rate}
+    transforms:
+      _target_: audio_data_pytorch.AllTransform
+      crop_size: ${length}
+      stereo: true
+      source_rate: ${sampling_rate}
+      target_rate: ${sampling_rate}
+      loudness: -20
+  val_split: 0.05
+  batch_size: ${batch_size}
+  num_workers: ${num_workers}
+  pin_memory: true
+callbacks:
+  rich_progress_bar:
+    _target_: pytorch_lightning.callbacks.RichProgressBar
+  model_checkpoint:
+    _target_: pytorch_lightning.callbacks.ModelCheckpoint
+    monitor: valid_loss
+    save_top_k: 1
+    save_last: true
+    mode: min
+    verbose: false
+    dirpath: ${logs_dir}/ckpts/${now:%Y-%m-%d-%H-%M-%S}
+    filename: '{epoch:02d}-{valid_loss:.3f}'
+  model_summary:
+    _target_: pytorch_lightning.callbacks.RichModelSummary
+    max_depth: 2
+  audio_samples_logger:
+    _target_: main.module_base.SampleLogger
+    num_items: 4
+    channels: ${channels}
+    sampling_rate: ${sampling_rate}
+    length: ${length}
+    sampling_steps:
+    - 50
+    use_ema_model: true
+loggers:
+  wandb:
+    _target_: pytorch_lightning.loggers.wandb.WandbLogger
+    project: ${oc.env:WANDB_PROJECT}
+    entity: ${oc.env:WANDB_ENTITY}
+    name: percussion_v0-finetuned-triton
+    job_type: train
+    group: ''
+    save_dir: ${logs_dir}
+trainer:
+  _target_: pytorch_lightning.Trainer
+  gpus: 1
+  precision: 16
+  accelerator: gpu
+  min_epochs: 0
+  max_epochs: -1
+  enable_model_summary: false
+  log_every_n_steps: 1
+  check_val_every_n_epoch: null
+  val_check_interval: ${log_every_n_steps}
+  accumulate_grad_batches: ${accumulate_grad_batches}
+ckpt: saved_models/percussion-finetuned-triton/percussion_v0-finetuned-triton.ckpt

percussion_v0-finetuned-triton.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72c6f82de16914317fe163bb28d84459e0f7374804037fc622512cf782367980
+size 509086849