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from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint |
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from fish_diffusion.datasets.audio_folder import AudioFolderDataset |
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_base_ = [ |
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"./_base_/archs/diff_svc_v2.py", |
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"./_base_/trainers/base.py", |
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"./_base_/schedulers/warmup_cosine_finetune.py", |
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"./_base_/datasets/audio_folder.py", |
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] |
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speaker_mapping = { |
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"Placeholder": 0, |
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} |
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dataset = dict( |
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train=dict( |
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_delete_=True, |
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type="ConcatDataset", |
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datasets=[ |
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dict( |
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type="AudioFolderDataset", |
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path="dataset/train", |
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speaker_id=speaker_mapping["Placeholder"], |
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), |
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], |
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collate_fn=AudioFolderDataset.collate_fn, |
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), |
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valid=dict( |
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_delete_=True, |
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type="ConcatDataset", |
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datasets=[ |
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dict( |
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type="AudioFolderDataset", |
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path="dataset/valid", |
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speaker_id=speaker_mapping["Placeholder"], |
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), |
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], |
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collate_fn=AudioFolderDataset.collate_fn, |
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), |
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) |
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model = dict( |
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speaker_encoder=dict( |
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input_size=len(speaker_mapping), |
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), |
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text_encoder=dict( |
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type="NaiveProjectionEncoder", |
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input_size=256, |
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output_size=256, |
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), |
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) |
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preprocessing = dict( |
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text_features_extractor=dict( |
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type="ChineseHubertSoft", |
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pretrained=True, |
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gate_size=25, |
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), |
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pitch_extractor=dict( |
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type="ParselMouthPitchExtractor", |
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), |
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) |
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trainer = dict( |
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val_check_interval=1000, |
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callbacks=[ |
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ModelCheckpoint( |
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filename="{epoch}-{step}-{valid_loss:.2f}", |
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every_n_train_steps=5000, |
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save_top_k=-1, |
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), |
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LearningRateMonitor(logging_interval="step"), |
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], |
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) |
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