Hugging Face's logo

Respair
/
NeMo_Canary

NeMo
TensorBoard
Model card Files
xet
 Metrics Community
NeMo_Canary
File size: 4,399 Bytes
b386992
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
name: "predictive_model"

model:
  type: predictive
  sample_rate: 16000
  skip_nan_grad: false
  num_outputs: 1
  normalize_input: true # normalize the input signal to 0dBFS

  train_ds:
    manifest_filepath: ???
    input_key: noisy_filepath
    target_key: clean_filepath
    audio_duration: 2.04 # Number of STFT time frames = 1 + audio_duration // encoder.hop_length = 256
    random_offset: true
    normalization_signal: input_signal
    batch_size: 8 # batch size may be increased based on the available memory
    shuffle: true
    num_workers: 8
    pin_memory: true

  validation_ds:
    manifest_filepath: ???
    input_key: noisy_filepath
    target_key: clean_filepath
    batch_size: 8
    shuffle: false
    num_workers: 4
    pin_memory: true

  encoder:
    _target_: nemo.collections.audio.modules.transforms.AudioToSpectrogram
    fft_length: 510 # Number of subbands in the STFT = fft_length // 2 + 1 = 256
    hop_length: 128
    magnitude_power: 0.5
    scale: 0.33

  decoder:
    _target_: nemo.collections.audio.modules.transforms.SpectrogramToAudio
    fft_length: ${model.encoder.fft_length} 
    hop_length: ${model.encoder.hop_length}
    magnitude_power: ${model.encoder.magnitude_power}
    scale: ${model.encoder.scale}

  estimator:
    _target_: nemo.collections.audio.parts.submodules.ncsnpp.SpectrogramNoiseConditionalScoreNetworkPlusPlus
    in_channels: 1 # single-channel noisy input
    out_channels: 1 # single-channel estimate
    num_res_blocks: 3 # increased number of res blocks
    pad_time_to: 64 # pad to 64 frames for the time dimension
    pad_dimension_to: 0 # no padding in the frequency dimension
    
  loss:
    _target_: nemo.collections.audio.losses.MSELoss # computed in the time domain

  metrics:
    val:
      sisdr: # output SI-SDR
        _target_: torchmetrics.audio.ScaleInvariantSignalDistortionRatio
    
  optim:
    name: adam
    lr: 1e-4
    # optimizer arguments
    betas: [0.9, 0.999]
    weight_decay: 0.0

trainer:
  devices: -1 # number of GPUs, -1 would use all available GPUs
  num_nodes: 1
  max_epochs: -1
  max_steps: -1 # computed at runtime if not set
  val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
  accelerator: auto
  strategy: ddp
  accumulate_grad_batches: 1
  gradient_clip_val: null
  precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
  log_every_n_steps: 25  # Interval of logging.
  enable_progress_bar: true
  num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
  check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
  sync_batchnorm: true
  enable_checkpointing: false  # Provided by exp_manager
  logger: false  # Provided by exp_manager

exp_manager:
  exp_dir: null
  name: ${name}

  # use exponential moving average for model parameters
  ema:
      enable: true
      decay: 0.999  # decay rate
      cpu_offload: false  # offload EMA parameters to CPU to save GPU memory
      every_n_steps: 1  # how often to update EMA weights
      validate_original_weights: False  # use original weights for validation calculation?

  # logging
  create_tensorboard_logger: true

  # checkpointing
  create_checkpoint_callback: true
  checkpoint_callback_params:
    # in case of multiple validation sets, first one is used
    monitor: val_sisdr
    mode: max
    save_top_k: 5
    always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints

  # early stopping
  create_early_stopping_callback: true
  early_stopping_callback_params:
    monitor: val_sisdr
    mode: max
    min_delta: 0.0
    patience: 20 # patience in terms of check_val_every_n_epoch
    verbose: true
    strict: false # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training.

  resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
  # you need to set these two to true to continue the training
  resume_if_exists: false
  resume_ignore_no_checkpoint: false

  # You may use this section to create a W&B logger
  create_wandb_logger: false
  wandb_logger_kwargs:
    name: null
    project: null