notes.md
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config.yaml
task:
_target_: pyannote.audio.tasks.SpeakerDiarization
duration: 10.0
max_speakers_per_chunk: 3
max_speakers_per_frame: 2
model:
_target_: pyannote.audio.models.segmentation.PyanNet
sample_rate: 16000
num_channels: 1
sincnet:
stride: 10
lstm:
hidden_size: 128
num_layers: 4
bidirectional: true
monolithic: true
linear:
hidden_size: 128
num_layers: 2Model architecture of ./pytorch_model.bin
print(model):
PyanNet(
(sincnet): SincNet(
(wav_norm1d): InstanceNorm1d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(conv1d): ModuleList(
(0): Encoder(
(filterbank): ParamSincFB()
)
(1): Conv1d(80, 60, kernel_size=(5,), stride=(1,))
(2): Conv1d(60, 60, kernel_size=(5,), stride=(1,))
)
(pool1d): ModuleList(
(0-2): 3 x MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
)
(norm1d): ModuleList(
(0): InstanceNorm1d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(1-2): 2 x InstanceNorm1d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
)
)
(lstm): LSTM(60, 128, num_layers=4, batch_first=True, dropout=0.5, bidirectional=True)
(linear): ModuleList(
(0): Linear(in_features=256, out_features=128, bias=True)
(1): Linear(in_features=128, out_features=128, bias=True)
)
(classifier): Linear(in_features=128, out_features=7, bias=True)
(activation): LogSoftmax(dim=-1)
)>>> list(model.specifications)
[Specifications(problem=<Problem.MONO_LABEL_CLASSIFICATION: 1>, resolution=<Resolution.FRAME: 1>, duration=10.0, min_duration=None, warm_up=(0.0, 0.0), classes=['speaker#1', 'speaker#2', 'speaker#3'], powerset_max_classes=2, permutation_invariant=True)]>>> model.hparams
"linear": {'hidden_size': 128, 'num_layers': 2}
"lstm": {'hidden_size': 128, 'num_layers': 4, 'bidirectional': True, 'monolithic': True, 'dropout': 0.5, 'batch_first': True}
"num_channels": 1
"sample_rate": 16000
"sincnet": {'stride': 10, 'sample_rate': 16000}