def test_single_channel_model(
encoder, decoder, separator, stft_consistency, loss_type, mask_type, training
):
if loss_type == "ci_sdr":
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
else:
# ci_sdr will fail if length is too short
inputs = torch.randn(2, 100)
ilens = torch.LongTensor([100, 80])
speech_refs = [torch.randn(2, 100).float(), torch.randn(2, 100).float()]
if loss_type not in ("snr", "si_snr", "ci_sdr") and isinstance(
encoder, ConvEncoder
):
with pytest.raises(TypeError):
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
return
if stft_consistency and loss_type in ("mask_mse", "snr", "si_snr", "ci_sdr"):
with pytest.raises(ValueError):
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
return
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_single_channel_model(encoder, decoder, separator, stft_consistency,
loss_type, mask_type, training):
if not is_torch_1_2_plus:
pytest.skip("Pytorch Version Under 1.2 is not supported for Enh task")
inputs = torch.randn(2, 100)
ilens = torch.LongTensor([100, 80])
speech_refs = [torch.randn(2, 100).float(), torch.randn(2, 100).float()]
if loss_type != "si_snr" and isinstance(encoder, ConvEncoder):
with pytest.raises(TypeError):
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
return
if stft_consistency and loss_type in ["mask_mse", "si_snr"]:
with pytest.raises(ValueError):
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
return
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i]
for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_ineube(n_mics, training, loss_wrappers, output_from):
if not is_torch_1_9_plus:
return
inputs = torch.randn(1, 300, n_mics)
ilens = torch.LongTensor([300])
speech_refs = [torch.randn(1, 300).float(), torch.randn(1, 300).float()]
from espnet2.enh.decoder.null_decoder import NullDecoder
from espnet2.enh.encoder.null_encoder import NullEncoder
encoder = NullEncoder()
decoder = NullDecoder()
separator = iNeuBe(
2, mic_channels=n_mics, output_from=output_from, tcn_blocks=1, tcn_repeats=1
)
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
mask_module=None,
loss_wrappers=loss_wrappers,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_dptnet(training, loss_wrappers):
encoder = ConvEncoder(channel=16, kernel_size=36, stride=18)
decoder = ConvDecoder(channel=16, kernel_size=36, stride=18)
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=dptnet_separator,
decoder=decoder,
mask_module=None,
loss_wrappers=loss_wrappers,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i]
for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel:
assert check_argument_types()
encoder = encoder_choices.get_class(args.encoder)(**args.encoder_conf)
separator = separator_choices.get_class(args.separator)(
encoder.output_dim, **args.separator_conf)
decoder = decoder_choices.get_class(args.decoder)(**args.decoder_conf)
loss_wrappers = []
for ctr in args.criterions:
criterion = criterion_choices.get_class(ctr["name"])(**ctr["conf"])
loss_wrapper = loss_wrapper_choices.get_class(ctr["wrapper"])(
criterion=criterion, **ctr["wrapper_conf"])
loss_wrappers.append(loss_wrapper)
# 1. Build model
model = ESPnetEnhancementModel(encoder=encoder,
separator=separator,
decoder=decoder,
loss_wrappers=loss_wrappers,
**args.model_conf)
# FIXME(kamo): Should be done in model?
# 2. Initialize
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model
def test_criterion_behavior_noise(encoder, decoder, separator):
if not isinstance(encoder, STFTEncoder) and isinstance(
separator, (DCCRNSeparator, DC_CRNSeparator)
):
# skip because DCCRNSeparator and DC_CRNSeparator only work
# for complex spectrum features
return
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
noise_ref = torch.randn(2, 300)
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
mask_module=None,
loss_wrappers=[PITSolver(criterion=SISNRLoss(is_noise_loss=True))],
)
enh_model.train()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(2)},
"noise_ref1": noise_ref,
}
loss, stats, weight = enh_model(**kwargs)
def test_single_channel_model(encoder, decoder, separator, training, loss_wrappers):
# DCCRN separator dose not support ConvEncoder and ConvDecoder
if isinstance(encoder, ConvEncoder) and isinstance(separator, DCCRNSeparator):
return
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
loss_wrappers=loss_wrappers,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_criterion_behavior(training):
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
enh_model = ESPnetEnhancementModel(
encoder=stft_encoder,
separator=rnn_separator,
decoder=stft_decoder,
mask_module=None,
loss_wrappers=[PITSolver(criterion=SISNRLoss(only_for_test=True))],
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i]
for i in range(2)},
}
if training:
with pytest.raises(AttributeError):
loss, stats, weight = enh_model(**kwargs)
else:
loss, stats, weight = enh_model(**kwargs)
def test_single_channel_model(encoder, decoder, separator, training,
loss_wrappers):
if not isinstance(encoder, STFTEncoder) and isinstance(
separator, (DCCRNSeparator, DC_CRNSeparator)):
# skip because DCCRNSeparator and DC_CRNSeparator only work
# for complex spectrum features
return
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
loss_wrappers=loss_wrappers,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i]
for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_enh_asr_model(
enh_encoder,
enh_decoder,
enh_separator,
training,
loss_wrappers,
frontend,
s2t_encoder,
s2t_decoder,
s2t_ctc,
):
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_ref = torch.randn(2, 300).float()
text = torch.LongTensor([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]])
text_lengths = torch.LongTensor([5, 5])
enh_model = ESPnetEnhancementModel(
encoder=enh_encoder,
separator=enh_separator,
decoder=enh_decoder,
mask_module=None,
loss_wrappers=loss_wrappers,
)
s2t_model = ESPnetASRModel(
vocab_size=len(token_list),
token_list=token_list,
frontend=frontend,
encoder=s2t_encoder,
decoder=s2t_decoder,
ctc=s2t_ctc,
specaug=None,
normalize=None,
preencoder=None,
postencoder=None,
joint_network=None,
)
enh_s2t_model = ESPnetEnhS2TModel(
enh_model=enh_model,
s2t_model=s2t_model,
)
if training:
enh_s2t_model.train()
else:
enh_s2t_model.eval()
kwargs = {
"speech": inputs,
"speech_lengths": ilens,
"speech_ref1": speech_ref,
"text": text,
"text_lengths": text_lengths,
}
loss, stats, weight = enh_s2t_model(**kwargs)
def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel:
assert check_argument_types()
enh_model = enh_choices.get_class(args.enh)(**args.enh_conf)
# 1. Build model
model = ESPnetEnhancementModel(enh_model=enh_model)
# FIXME(kamo): Should be done in model?
# 2. Initialize
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model
def test_enh_diar_model(
enh_encoder,
enh_decoder,
enh_separator,
mask_module,
training,
loss_wrappers,
diar_frontend,
diar_encoder,
diar_decoder,
label_aggregator,
):
inputs = torch.randn(2, 300)
speech_ref = torch.randn(2, 300).float()
text = torch.randint(high=2, size=(2, 300, 2))
enh_model = ESPnetEnhancementModel(
encoder=enh_encoder,
separator=enh_separator,
decoder=enh_decoder,
mask_module=mask_module,
loss_wrappers=loss_wrappers,
)
diar_model = ESPnetDiarizationModel(
label_aggregator=label_aggregator,
frontend=diar_frontend,
encoder=diar_encoder,
decoder=diar_decoder,
specaug=None,
normalize=None,
attractor=None,
)
enh_s2t_model = ESPnetEnhS2TModel(
enh_model=enh_model,
s2t_model=diar_model,
)
if training:
enh_s2t_model.train()
else:
enh_s2t_model.eval()
kwargs = {
"speech": inputs,
"speech_ref1": speech_ref,
"speech_ref2": speech_ref,
"text": text,
}
loss, stats, weight = enh_s2t_model(**kwargs)
def test_criterion_behavior_dereverb(loss_type, num_spk):
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float() for _ in range(num_spk)]
dereverb_ref = [torch.randn(2, 300).float() for _ in range(num_spk)]
beamformer = NeuralBeamformer(
input_dim=17,
loss_type=loss_type,
num_spk=num_spk,
use_wpe=True,
wlayers=2,
wunits=2,
wprojs=2,
use_dnn_mask_for_wpe=True,
multi_source_wpe=True,
use_beamformer=True,
blayers=2,
bunits=2,
bprojs=2,
badim=2,
ref_channel=0,
use_noise_mask=False,
)
if loss_type == "mask_mse":
loss_wrapper = PITSolver(
criterion=FrequencyDomainMSE(
compute_on_mask=True, mask_type="PSM", is_dereverb_loss=True
)
)
else:
loss_wrapper = PITSolver(criterion=SISNRLoss(is_dereverb_loss=True))
enh_model = ESPnetEnhancementModel(
encoder=stft_encoder,
separator=beamformer,
decoder=stft_decoder,
mask_module=None,
loss_wrappers=[loss_wrapper],
)
enh_model.train()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(num_spk)},
"dereverb_ref1": dereverb_ref[0],
}
loss, stats, weight = enh_model(**kwargs)
def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel:
assert check_argument_types()
encoder = encoder_choices.get_class(args.encoder)(**args.encoder_conf)
separator = separator_choices.get_class(args.separator)(
encoder.output_dim, **args.separator_conf)
decoder = decoder_choices.get_class(args.decoder)(**args.decoder_conf)
if args.separator.endswith("nomask"):
mask_module = mask_module_choices.get_class(args.mask_module)(
input_dim=encoder.output_dim,
**args.mask_module_conf,
)
else:
mask_module = None
loss_wrappers = []
if getattr(args, "criterions", None) is not None:
# This check is for the compatibility when load models
# that packed by older version
for ctr in args.criterions:
criterion_conf = ctr.get("conf", {})
criterion = criterion_choices.get_class(
ctr["name"])(**criterion_conf)
loss_wrapper = loss_wrapper_choices.get_class(ctr["wrapper"])(
criterion=criterion, **ctr["wrapper_conf"])
loss_wrappers.append(loss_wrapper)
# 1. Build model
model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
loss_wrappers=loss_wrappers,
mask_module=mask_module,
**args.model_conf,
)
# FIXME(kamo): Should be done in model?
# 2. Initialize
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model
def build_model(cls, args: argparse.Namespace) -> ESPnetEnhancementModel:
assert check_argument_types()
encoder = encoder_choices.get_class(args.encoder)(**args.encoder_conf)
separator = separator_choices.get_class(args.separator)(
encoder.output_dim, **args.separator_conf)
decoder = decoder_choices.get_class(args.decoder)(**args.decoder_conf)
# 1. Build model
model = ESPnetEnhancementModel(encoder=encoder,
separator=separator,
decoder=decoder,
**args.model_conf)
# FIXME(kamo): Should be done in model?
# 2. Initialize
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model
def test_svoice_model(encoder, decoder, separator, training, loss_wrappers):
inputs = torch.randn(2, 300)
ilens = torch.LongTensor([300, 200])
speech_refs = [torch.randn(2, 300).float(), torch.randn(2, 300).float()]
enh_model = ESPnetEnhancementModel(
encoder=encoder,
separator=separator,
decoder=decoder,
loss_wrappers=loss_wrappers,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i]
for i in range(2)},
}
loss, stats, weight = enh_model(**kwargs)
def test_forward_with_beamformer_net(
training,
mask_type,
loss_type,
num_spk,
use_noise_mask,
stft_consistency,
use_builtin_complex,
):
# Skip some testing cases
if not loss_type.startswith("mask") and mask_type != "IBM":
# `mask_type` has no effect when `loss_type` is not "mask..."
return
if not is_torch_1_9_plus and use_builtin_complex:
# builtin complex support is only available in PyTorch 1.8+
return
ch = 2
inputs = random_speech[..., :ch].float()
ilens = torch.LongTensor([16, 12])
speech_refs = [torch.randn(2, 16, ch).float() for spk in range(num_spk)]
noise_ref1 = torch.randn(2, 16, ch, dtype=torch.float)
dereverb_ref1 = torch.randn(2, 16, ch, dtype=torch.float)
encoder = STFTEncoder(
n_fft=8, hop_length=2, use_builtin_complex=use_builtin_complex
)
decoder = STFTDecoder(n_fft=8, hop_length=2)
if stft_consistency and loss_type in ("mask_mse", "snr", "si_snr", "ci_sdr"):
# skip this condition
return
beamformer = NeuralBeamformer(
input_dim=5,
loss_type=loss_type,
num_spk=num_spk,
use_wpe=True,
wlayers=2,
wunits=2,
wprojs=2,
use_dnn_mask_for_wpe=True,
multi_source_wpe=True,
use_beamformer=True,
blayers=2,
bunits=2,
bprojs=2,
badim=2,
ref_channel=0,
use_noise_mask=use_noise_mask,
beamformer_type="mvdr_souden",
)
enh_model = ESPnetEnhancementModel(
encoder=encoder,
decoder=decoder,
separator=beamformer,
stft_consistency=stft_consistency,
loss_type=loss_type,
mask_type=mask_type,
)
if training:
enh_model.train()
else:
enh_model.eval()
kwargs = {
"speech_mix": inputs,
"speech_mix_lengths": ilens,
**{"speech_ref{}".format(i + 1): speech_refs[i] for i in range(num_spk)},
"noise_ref1": noise_ref1,
"dereverb_ref1": dereverb_ref1,
}
loss, stats, weight = enh_model(**kwargs)