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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import torch
from omegaconf import DictConfig
from nemo.collections.asr.models import EncDecDenoiseMaskedTokenPredModel, SpeechEncDecSelfSupervisedModel
from nemo.core.classes.common import typecheck
@pytest.fixture()
def ssl_model():
preprocessor = {
'cls': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor',
'params': dict({'pad_to': 16, 'dither': 0}),
}
model_defaults = {'enc_hidden': 32, 'dec_out': 128}
encoder = {
'cls': 'nemo.collections.asr.modules.ConvASREncoder',
'params': {
'feat_in': 64,
'activation': 'relu',
'conv_mask': True,
'jasper': [
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
],
},
}
spec_augment = {
'_target_': 'nemo.collections.asr.modules.MaskedPatchAugmentation',
'freq_masks': 3,
'freq_width': 20,
'patch_size': 16,
'mask_patches': 0.5,
}
loss_list_contr_mlm = {
'contr': {
'decoder': {
'_target_': 'nemo.collections.asr.modules.ConvASRDecoderReconstruction',
'feat_in': model_defaults['enc_hidden'],
'feat_hidden': 128,
'feat_out': model_defaults['dec_out'],
'stride_layers': 0,
'non_stride_layers': 0,
'stride_transpose': False,
},
'loss': {
'_target_': 'nemo.collections.asr.losses.ContrastiveLoss',
'in_dim': 64,
'proj_dim': model_defaults['dec_out'],
'combine_time_steps': 1,
'quantized_targets': True,
'codebook_size': 64,
'sample_from_same_utterance_only': True,
'sample_from_non_masked': False,
'num_negatives': 3,
},
},
'mlm': {
'decoder': {
'_target_': 'nemo.collections.asr.modules.ConvASRDecoder',
'feat_in': model_defaults['enc_hidden'],
'num_classes': 4096,
},
'loss': {'_target_': 'nemo.collections.asr.losses.MLMLoss', 'combine_time_steps': 1},
'targets_from_loss': "contr",
},
}
modelConfig_contr_mlm = DictConfig(
{
'preprocessor': DictConfig(preprocessor),
'spec_augment': DictConfig(spec_augment),
'model_defaults': DictConfig(model_defaults),
'encoder': DictConfig(encoder),
'loss_list': DictConfig(loss_list_contr_mlm),
}
)
ssl_model = SpeechEncDecSelfSupervisedModel(cfg=modelConfig_contr_mlm)
return ssl_model
@pytest.fixture()
def denoise_mlm_ssl_model():
model_defaults = {
"subsampling_factor": 1,
'enc_hidden': 32,
'dec_out': 128,
"sample_rate": 16000,
"num_classes": 32,
"num_books": 1,
"code_dim": 16,
"squeeze_single": False,
"mask_position": "pre_conv", # position to apply masking, before or after conv subsampling, choices in ['pre_conv', 'post_conv']
}
preprocessor = {
"_target_": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
"sample_rate": model_defaults["sample_rate"],
"normalize": "per_feature",
"window_size": 0.025,
"window_stride": 0.01,
"window": "hann",
"features": 80,
"n_fft": 512,
"log": True,
"frame_splicing": 1,
"dither": 0.00001,
"pad_to": 16,
"pad_value": 0.0,
}
encoder = {
'cls': 'nemo.collections.asr.modules.ConvASREncoder',
'params': {
'feat_in': preprocessor["features"],
'activation': 'relu',
'conv_mask': True,
'jasper': [
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
{
'filters': model_defaults['enc_hidden'],
'repeat': 1,
'kernel': [1],
'stride': [1],
'dilation': [1],
'dropout': 0.0,
'residual': False,
'separable': True,
'se': True,
'se_context_size': -1,
},
],
},
}
spec_augment = {
'_target_': 'nemo.collections.asr.modules.SpectrogramAugmentation',
'freq_masks': 0,
'time_masks': 0,
'freq_width': 16,
'time_width': 0.05,
}
masking = {
"_target_": "nemo.collections.asr.modules.RandomBlockMasking",
"block_size": 40, # for pre_conv masking, 10ms per frame, 400ms per block with block_size=40
"mask_prob": 0.01, # for allow_overlap=True, this means the mask prob for each frame; otherwise it means the overall masked proportion
"feat_in": preprocessor["features"],
"freeze": True,
"allow_overlap": True,
}
quantizer = {
"_target_": "nemo.collections.asr.modules.RandomProjectionVectorQuantizer",
"feat_in": preprocessor["features"],
"code_dim": model_defaults["code_dim"],
"num_books": model_defaults["num_books"],
"num_classes": model_defaults["num_classes"],
"dist_fn": "l2", # choices=["l2", "cosine"]
"freeze": True,
"squeeze_single": model_defaults["squeeze_single"],
"combine_time_steps": model_defaults["subsampling_factor"], # conformer sub-sampling ratio
}
decoder = {
"_target_": "nemo.collections.asr.modules.MultiSoftmaxDecoder",
"feat_in": model_defaults["enc_hidden"],
"num_classes": model_defaults["num_classes"],
"num_decoders": model_defaults["num_books"],
"squeeze_single": model_defaults["squeeze_single"],
"use_bias": True,
}
loss = {
"_target_": "nemo.collections.asr.losses.MultiMLMLoss",
"combine_time_steps": model_defaults[
"subsampling_factor"
], # conformer sub-sampling ratio for 'pre_conv', 1 for 'post_conv'
"mask_threshold": 0.8,
"num_decoders": model_defaults["num_books"],
"squeeze_single": model_defaults["squeeze_single"],
}
optim = {
"name": "adamw",
"lr": 5.0,
# optimizer arguments
"betas": [0.9, 0.98],
"weight_decay": 1e-3,
}
model_config = DictConfig(
{
"preprocessor": DictConfig(preprocessor),
"spec_augment": DictConfig(spec_augment),
'model_defaults': DictConfig(model_defaults),
"masking": DictConfig(masking),
"quantizer": DictConfig(quantizer),
"encoder": DictConfig(encoder),
"decoder": DictConfig(decoder),
"loss": DictConfig(loss),
"optim": DictConfig(optim),
}
)
ssl_model = EncDecDenoiseMaskedTokenPredModel(cfg=model_config)
return ssl_model
class TestSSLModel:
@pytest.mark.unit
def test_constructor(self, ssl_model):
confdict = ssl_model.to_config_dict()
instance2 = SpeechEncDecSelfSupervisedModel.from_config_dict(confdict)
assert isinstance(instance2, SpeechEncDecSelfSupervisedModel)
@pytest.mark.unit
def test_contr_nonquant(self, ssl_model):
modelConfig_contr_nonquant = ssl_model.to_config_dict()
loss_list_contr_nonquant = dict(modelConfig_contr_nonquant['loss_list'])
del loss_list_contr_nonquant['mlm']
loss_list_contr_nonquant['contr']['loss']['quantized_targets'] = False
modelConfig_contr_nonquant['loss_list'] = DictConfig(loss_list_contr_nonquant)
ssl_model = SpeechEncDecSelfSupervisedModel(cfg=modelConfig_contr_nonquant)
input_signal = torch.randn(size=(4, 64000))
length = torch.randint(low=48000, high=64000, size=[4])
with torch.no_grad():
spectrograms, spec_masks, encoded, encoded_len = ssl_model.forward(
input_signal=input_signal, input_signal_length=length
)
loss_value, loss_val_dict = ssl_model.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len)
assert len(loss_val_dict) == 1
@pytest.mark.unit
def test_contr_mlm(self, ssl_model):
input_signal = torch.randn(size=(4, 64000))
length = torch.randint(low=48000, high=64000, size=[4])
with torch.no_grad():
spectrograms, spec_masks, encoded, encoded_len = ssl_model.forward(
input_signal=input_signal, input_signal_length=length
)
loss_value, loss_val_dict = ssl_model.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len)
assert len(loss_val_dict) == 2
@pytest.mark.unit
def test_contr_mlm_multi(self, ssl_model):
modelConfig_contr_mlm_multi = ssl_model.to_config_dict()
model_defaults = modelConfig_contr_mlm_multi['model_defaults']
loss_list_contr_mlm_multi = dict(modelConfig_contr_mlm_multi['loss_list'])
loss_list_contr_mlm_multi['mlm_2'] = {
'decoder': {
'_target_': 'nemo.collections.asr.modules.ConvASRDecoder',
'feat_in': model_defaults['enc_hidden'],
'num_classes': 4096,
},
'loss': {'_target_': 'nemo.collections.asr.losses.MLMLoss', 'combine_time_steps': 1},
'output_from_layer': "encoder.0",
'targets_from_loss': "contr",
}
loss_list_contr_mlm_multi['mlm_3'] = {
'decoder': {
'_target_': 'nemo.collections.asr.modules.ConvASRDecoder',
'feat_in': model_defaults['enc_hidden'],
'num_classes': 4096,
},
'loss': {'_target_': 'nemo.collections.asr.losses.MLMLoss', 'combine_time_steps': 1},
'output_from_layer': "encoder.1",
'targets_from_loss': "contr",
}
modelConfig_contr_mlm_multi['loss_list'] = DictConfig(loss_list_contr_mlm_multi)
ssl_model = SpeechEncDecSelfSupervisedModel(cfg=modelConfig_contr_mlm_multi)
input_signal = torch.randn(size=(4, 64000))
length = torch.randint(low=48000, high=64000, size=[4])
with torch.no_grad():
spectrograms, spec_masks, encoded, encoded_len = ssl_model.forward(
input_signal=input_signal, input_signal_length=length
)
loss_value, loss_val_dict = ssl_model.decoder_loss_step(spectrograms, spec_masks, encoded, encoded_len)
assert len(loss_val_dict) == 4
class TestDenoiseMLMSSLModel:
@pytest.mark.unit
def test_forward(self, denoise_mlm_ssl_model):
input_signal = torch.randn(size=(4, 64000))
input_length = torch.randint(low=48000, high=64000, size=[4])
noise = 0.1 * torch.ones_like(input_signal)
noisy_input_signal = input_signal + noise
noisy_input_length = input_length
with torch.no_grad():
with typecheck.disable_checks():
log_probs, encoded_len, masks, tokens = denoise_mlm_ssl_model.forward(
input_signal=input_signal,
input_signal_length=input_length,
noisy_input_signal=noisy_input_signal,
noisy_input_signal_length=noisy_input_length,
)
assert log_probs.size(0) == 4
assert log_probs.size(2) == denoise_mlm_ssl_model.cfg.model_defaults.num_classes
assert encoded_len.size(0) == 4
assert masks.size(0) == 4
assert tokens.size(0) == 4
assert masks.sum() == 0.0 # no mask should be applied to the input by default
@pytest.mark.unit
def test_forward_masked(self, denoise_mlm_ssl_model: EncDecDenoiseMaskedTokenPredModel):
input_signal = torch.randn(size=(4, 64000))
input_length = torch.randint(low=48000, high=64000, size=[4])
noise = 0.1 * torch.ones_like(input_signal)
noisy_input_signal = input_signal + noise
noisy_input_length = input_length
with torch.no_grad():
with typecheck.disable_checks():
log_probs, encoded_len, masks, tokens = denoise_mlm_ssl_model.forward(
input_signal=input_signal,
input_signal_length=input_length,
noisy_input_signal=noisy_input_signal,
noisy_input_signal_length=noisy_input_length,
apply_mask=True,
)
loss_value = denoise_mlm_ssl_model.loss(
masks=masks, decoder_outputs=log_probs, targets=tokens, decoder_lengths=encoded_len
)
assert log_probs.size(0) == 4
assert log_probs.size(2) == denoise_mlm_ssl_model.cfg.model_defaults.num_classes
assert encoded_len.size(0) == 4
assert masks.size(0) == 4
assert tokens.size(0) == 4
assert masks.sum() > 0.0 # mask should be applied to the input
assert not torch.isnan(loss_value)
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