100x better for subtrain/eval to be by group instead
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@ -211,7 +211,7 @@ class Dataset(_Dataset):
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self.training = training
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self.dataset_type = "training" if self.training else "validation"
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self.dataset = cfg.dataset.training if self.training else cfg.dataset.validation
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self.sampler_type = cfg.dataset.sample_type if self.dataset_type == "training" else "path"
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self.sampler_type = cfg.dataset.sample_type if self.dataset_type == "training" else "group"
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# to-do: do not do validation if there's nothing in the validation
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# this just makes it be happy
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@ -319,6 +319,8 @@ class AR_NAR(Base):
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def example_usage():
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#cfg.trainer.backend = "local"
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cfg.hyperparameters.gradient_accumulation_steps = 1
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if cfg.inference.audio_backend == "dac":
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cfg.sample_rate = 44_000
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from functools import partial
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from einops import repeat
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@ -371,7 +373,7 @@ def example_usage():
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'n_layers': 8, # 32
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'n_experts': 1,
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'p_dropout': 0.0,
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'p_dropout': 0.1,
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'l_padding': 8 if cfg.optimizations.fp8 else 0,
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@ -386,7 +388,7 @@ def example_usage():
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"""
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model = AR_NAR(**kwargs).to(device)
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steps = 1000
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steps = 200
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optimizer = cfg.hyperparameters.optimizer.lower() if cfg.cfg_path is not None else "prodigy"
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scheduler = cfg.hyperparameters.scheduler.lower() if cfg.cfg_path is not None else ""
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