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91 lines
1.9 KiB
YAML
91 lines
1.9 KiB
YAML
# @package __global__
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# This is the training loop solver
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# for the base MusicGen model (text-to-music)
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# on monophonic audio sampled at 32 kHz
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defaults:
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- musicgen/default
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- /model: lm/musicgen_lm
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- override /dset: audio/default
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- _self_
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lm_model: transformer_lm_magnet
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solver: magnet
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autocast: true
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autocast_dtype: float16
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# EnCodec large trained on mono-channel music audio sampled at 32khz
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# with a total stride of 640 leading to 50 frames/s.
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# rvq.n_q=4, rvq.bins=2048, no quantization dropout
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# (transformer_lm card and n_q must be compatible)
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compression_model_checkpoint: //pretrained/facebook/encodec_32khz
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efficient_attention_backend: xformers # restricted attention implementation supports only xformers at the moment
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channels: 1
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sample_rate: 32000
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deadlock:
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use: true # deadlock detection
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dataset:
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batch_size: 192 # 32 GPUs
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sample_on_weight: false # Uniform sampling all the way
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sample_on_duration: false # Uniform sampling all the way
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optim:
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epochs: 500
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optimizer: dadam
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lr: 1
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ema:
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use: true
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updates: 10
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device: cuda
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logging:
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log_tensorboard: true
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schedule:
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lr_scheduler: cosine
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cosine:
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warmup: 4000
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lr_min_ratio: 0.0
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cycle_length: 1.0
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codebooks_pattern:
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modeling: parallel
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parallel:
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empty_initial: -1
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transformer_lm:
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card: 2048
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causal: false
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subcodes_context: 5
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compression_model_framerate: 50 # NOTE: Must match the actual frame rate of the used compression model
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segment_duration: 0
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span_len: -1
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masking:
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span_len: 3
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generate:
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lm:
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max_prompt_len: null
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max_gen_len: null
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remove_prompts: false
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use_sampling: true
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temp: 3.0
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top_k: 0
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top_p: 0.9
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max_cfg_coef: 10.0
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min_cfg_coef: 1.0
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decoding_steps: [60, 10, 10, 10]
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anneal_temp: true
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span_scoring: 'max'
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span_arrangement: 'nonoverlap'
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prompted_samples: false
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samples:
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prompted: false
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unprompted: true
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