isort formatted

egs/vctk/TTS/local/compute_spectrogram_vctk.py

@@ -32,9 +32,9 @@ from pathlib import Path
 import torch
 from lhotse import (
     CutSet,
+    LilcomChunkyWriter,
     Spectrogram,
     SpectrogramConfig,
-    LilcomChunkyWriter,
     load_manifest,
 )
 from lhotse.audio import RecordingSet

egs/vctk/TTS/vits/duration_predictor.py

@@ -14,7 +14,6 @@ from typing import Optional
 
 import torch
 import torch.nn.functional as F
-
 from flow import (
     ConvFlow,
     DilatedDepthSeparableConv,

egs/vctk/TTS/vits/flow.py

@@ -13,7 +13,6 @@ import math
 from typing import Optional, Tuple, Union
 
 import torch
-
 from transform import piecewise_rational_quadratic_transform
 
 

egs/vctk/TTS/vits/generator.py

@@ -16,9 +16,6 @@ from typing import List, Optional, Tuple
 import numpy as np
 import torch
 import torch.nn.functional as F
-
-from icefall.utils import make_pad_mask
-
 from duration_predictor import StochasticDurationPredictor
 from hifigan import HiFiGANGenerator
 from posterior_encoder import PosteriorEncoder
@@ -26,6 +23,8 @@ from residual_coupling import ResidualAffineCouplingBlock
 from text_encoder import TextEncoder
 from utils import get_random_segments
 
+from icefall.utils import make_pad_mask
+
 
 class VITSGenerator(torch.nn.Module):
     """Generator module in VITS, `Conditional Variational Autoencoder

egs/vctk/TTS/vits/infer.py

@@ -36,13 +36,12 @@ import k2
 import torch
 import torch.nn as nn
 import torchaudio
-
-from train import get_model, get_params
 from tokenizer import Tokenizer
+from train import get_model, get_params
+from tts_datamodule import LJSpeechTtsDataModule
 
 from icefall.checkpoint import load_checkpoint
 from icefall.utils import AttributeDict, setup_logger
-from tts_datamodule import LJSpeechTtsDataModule
 
 
 def get_parser():

egs/vctk/TTS/vits/loss.py

@@ -14,7 +14,6 @@ from typing import List, Tuple, Union
 import torch
 import torch.distributions as D
 import torch.nn.functional as F
-
 from lhotse.features.kaldi import Wav2LogFilterBank
 
 

egs/vctk/TTS/vits/posterior_encoder.py

@@ -12,9 +12,9 @@ This code is based on https://github.com/jaywalnut310/vits.
 from typing import Optional, Tuple
 
 import torch
+from wavenet import Conv1d, WaveNet
 
 from icefall.utils import make_pad_mask
-from wavenet import WaveNet, Conv1d
 
 
 class PosteriorEncoder(torch.nn.Module):

egs/vctk/TTS/vits/residual_coupling.py

@@ -12,7 +12,6 @@ This code is based on https://github.com/jaywalnut310/vits.
 from typing import Optional, Tuple, Union
 
 import torch
-
 from flow import FlipFlow
 from wavenet import WaveNet
 

egs/vctk/TTS/vits/train.py

@@ -18,21 +18,25 @@
 
 import argparse
 import logging
-import numpy as np
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple, Union
 
 import k2
+import numpy as np
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from lhotse.cut import Cut
 from lhotse.utils import fix_random_seed
-from torch.optim import Optimizer
+from tokenizer import Tokenizer
 from torch.cuda.amp import GradScaler, autocast
 from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
 from torch.utils.tensorboard import SummaryWriter
+from tts_datamodule import LJSpeechTtsDataModule
+from utils import MetricsTracker, plot_feature, save_checkpoint
+from vits import VITS
 
 from icefall import diagnostics
 from icefall.checkpoint import load_checkpoint
@@ -41,11 +45,6 @@ from icefall.env import get_env_info
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, setup_logger, str2bool
 
-from tokenizer import Tokenizer
-from tts_datamodule import LJSpeechTtsDataModule
-from utils import MetricsTracker, plot_feature, save_checkpoint
-from vits import VITS
-
 LRSchedulerType = torch.optim.lr_scheduler._LRScheduler
 
 
@@ -296,6 +295,7 @@ def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device):
     audio_lens = batch["audio_lens"].to(device)
     features_lens = batch["features_lens"].to(device)
     text = batch["text"]
+    speakers = batch["speakers"]
 
     tokens = tokenizer.texts_to_token_ids(text)
     tokens = k2.RaggedTensor(tokens)
@@ -306,7 +306,7 @@ def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device):
     # a tensor of shape (B, T)
     tokens = tokens.pad(mode="constant", padding_value=tokenizer.blank_id)
 
-    return audio, audio_lens, features, features_lens, tokens, tokens_lens
+    return audio, audio_lens, features, features_lens, tokens, tokens_lens, speakers
 
 
 def train_one_epoch(
@@ -385,9 +385,15 @@ def train_one_epoch(
         params.batch_idx_train += 1
 
         batch_size = len(batch["text"])
-        audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input(
-            batch, tokenizer, device
-        )
+        (
+            audio,
+            audio_lens,
+            features,
+            features_lens,
+            tokens,
+            tokens_lens,
+            speakers,
+        ) = prepare_input(batch, tokenizer, device)
 
         loss_info = MetricsTracker()
         loss_info["samples"] = batch_size

egs/vctk/TTS/vits/tts_datamodule.py

@@ -29,10 +29,10 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     CutConcatenate,
     CutMix,
     DynamicBucketingSampler,
-    SpeechSynthesisDataset,
     PrecomputedFeatures,
     SimpleCutSampler,
     SpecAugment,
+    SpeechSynthesisDataset,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
     AudioSamples,

egs/vctk/TTS/vits/utils.py

@@ -14,15 +14,15 @@
 # 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.
-from typing import Any, Dict, List, Optional, Tuple, Union
 import collections
 import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-import torch.nn as nn
 import torch.distributed as dist
+import torch.nn as nn
 from lhotse.dataset.sampling.base import CutSampler
-from pathlib import Path
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer

egs/vctk/TTS/vits/vits.py

@@ -9,8 +9,7 @@ from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.cuda.amp import autocast
-
+from generator import VITSGenerator
 from hifigan import (
     HiFiGANMultiPeriodDiscriminator,
     HiFiGANMultiScaleDiscriminator,
@@ -25,9 +24,8 @@ from loss import (
     KLDivergenceLoss,
     MelSpectrogramLoss,
 )
+from torch.cuda.amp import autocast
 from utils import get_segments
-from generator import VITSGenerator
-
 
 AVAILABLE_GENERATERS = {
     "vits_generator": VITSGenerator,

egs/vctk/TTS/vits/wavenet.py

@@ -9,9 +9,8 @@ This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
 
 """
 
-import math
 import logging
-
+import math
 from typing import Optional, Tuple
 
 import torch