Fix linter issues ofr p3.6

.pylintrc

@@ -64,6 +64,11 @@ disable=missing-docstring,
         too-many-public-methods,
         too-many-lines,
         bare-except,
+        ## for avoiding weird p3.6 CI linter error
+        ## TODO: see later if we can remove this
+        assigning-non-slot,
+        unsupported-assignment-operation,
+        ## end
         line-too-long,
         fixme,
         wrong-import-order,

TTS/speaker_encoder/speaker_encoder_config.py

@@ -1,5 +1,5 @@
 from dataclasses import asdict, dataclass, field
-from typing import List
+from typing import Dict, List
 
 from coqpit import MISSING
 
@@ -14,7 +14,7 @@ class SpeakerEncoderConfig(BaseTrainingConfig):
     audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
     datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
     # model params
-    model_params: dict = field(
+    model_params: Dict = field(
         default_factory=lambda: {
             "model_name": "lstm",
             "input_dim": 80,
@@ -25,9 +25,9 @@ class SpeakerEncoderConfig(BaseTrainingConfig):
         }
     )
 
-    audio_augmentation: dict = field(default_factory=lambda: {})
+    audio_augmentation: Dict = field(default_factory=lambda: {})
 
-    storage: dict = field(
+    storage: Dict = field(
         default_factory=lambda: {
             "sample_from_storage_p": 0.66,  # the probability with which we'll sample from the DataSet in-memory storage
             "storage_size": 15,  # the size of the in-memory storage with respect to a single batch

TTS/vocoder/configs/univnet_config.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass, field
+from typing import Dict
 
 from TTS.vocoder.configs.shared_configs import BaseGANVocoderConfig
 
@@ -95,7 +96,7 @@ class UnivnetConfig(BaseGANVocoderConfig):
     # model specific params
     discriminator_model: str = "univnet_discriminator"
     generator_model: str = "univnet_generator"
-    generator_model_params: dict = field(
+    generator_model_params: Dict = field(
         default_factory=lambda: {
             "in_channels": 64,
             "out_channels": 1,
@@ -120,7 +121,7 @@ class UnivnetConfig(BaseGANVocoderConfig):
 
     # loss weights - overrides
     stft_loss_weight: float = 2.5
-    stft_loss_params: dict = field(
+    stft_loss_params: Dict = field(
         default_factory=lambda: {
             "n_ffts": [1024, 2048, 512],
             "hop_lengths": [120, 240, 50],
@@ -132,7 +133,7 @@ class UnivnetConfig(BaseGANVocoderConfig):
     hinge_G_loss_weight: float = 0
     feat_match_loss_weight: float = 0
     l1_spec_loss_weight: float = 0
-    l1_spec_loss_params: dict = field(
+    l1_spec_loss_params: Dict = field(
         default_factory=lambda: {
             "use_mel": True,
             "sample_rate": 22050,
@@ -152,7 +153,7 @@ class UnivnetConfig(BaseGANVocoderConfig):
     # lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
     lr_scheduler_disc: str = None  # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
     # lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
-    optimizer_params: dict = field(default_factory=lambda: {"betas": [0.5, 0.9], "weight_decay": 0.0})
+    optimizer_params: Dict = field(default_factory=lambda: {"betas": [0.5, 0.9], "weight_decay": 0.0})
     steps_to_start_discriminator: int = 200000
 
     def __post_init__(self):