mirror of
https://github.com/aigc3d/LAM_Audio2Expression.git
synced 2026-02-04 17:39:24 +08:00
93 lines
2.4 KiB
Python
93 lines
2.4 KiB
Python
weight = 'pretrained_models/lam_audio2exp.tar' # path to model weight
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ex_vol = True # Isolates vocal track from audio file
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audio_input = './assets/sample_audio/BarackObama.wav'
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save_json_path = 'bsData.json'
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audio_sr = 16000
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fps = 30.0
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movement_smooth = True
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brow_movement = True
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id_idx = 153
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resume = False # whether to resume training process
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evaluate = True # evaluate after each epoch training process
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test_only = False # test process
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seed = None # train process will init a random seed and record
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save_path = "exp/audio2exp"
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num_worker = 16 # total worker in all gpu
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batch_size = 16 # total batch size in all gpu
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batch_size_val = None # auto adapt to bs 1 for each gpu
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batch_size_test = None # auto adapt to bs 1 for each gpu
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epoch = 100 # total epoch, data loop = epoch // eval_epoch
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eval_epoch = 100 # sche total eval & checkpoint epoch
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sync_bn = False
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enable_amp = False
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empty_cache = False
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find_unused_parameters = False
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mix_prob = 0
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param_dicts = None # example: param_dicts = [dict(keyword="block", lr_scale=0.1)]
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# model settings
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model = dict(
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type="DefaultEstimator",
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backbone=dict(
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type="Audio2Expression",
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pretrained_encoder_type='wav2vec',
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pretrained_encoder_path='facebook/wav2vec2-base-960h',
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wav2vec2_config_path = 'configs/wav2vec2_config.json',
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num_identity_classes=5016,
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identity_feat_dim=64,
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hidden_dim=512,
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expression_dim=52,
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norm_type='ln',
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use_transformer=True,
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num_attention_heads=8,
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num_transformer_layers=6,
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),
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criteria=[dict(type="L1Loss", loss_weight=1.0, ignore_index=-1)],
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)
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dataset_type = 'audio2exp'
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data_root = './'
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data = dict(
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train=dict(
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type=dataset_type,
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split="train",
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data_root=data_root,
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test_mode=False,
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),
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val=dict(
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type=dataset_type,
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split="val",
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data_root=data_root,
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test_mode=False,
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),
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test=dict(
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type=dataset_type,
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split="val",
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data_root=data_root,
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test_mode=True
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),
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)
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# hook
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hooks = [
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dict(type="CheckpointLoader"),
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dict(type="IterationTimer", warmup_iter=2),
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dict(type="InformationWriter"),
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dict(type="SemSegEvaluator"),
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dict(type="CheckpointSaver", save_freq=None),
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dict(type="PreciseEvaluator", test_last=False),
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]
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# Trainer
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train = dict(type="DefaultTrainer")
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# Tester
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infer = dict(type="Audio2ExpressionInfer",
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verbose=True)
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