style: run black

TTS/bin/collect_env_info.py

@@ -1,4 +1,5 @@
 """Get detailed info about the working environment."""
+
 import json
 import os
 import platform

TTS/bin/find_unique_chars.py

@@ -1,4 +1,5 @@
 """Find all the unique characters in a dataset"""
+
 import argparse
 from argparse import RawTextHelpFormatter
 

TTS/bin/find_unique_phonemes.py

@@ -1,4 +1,5 @@
 """Find all the unique characters in a dataset"""
+
 import argparse
 import multiprocessing
 from argparse import RawTextHelpFormatter

TTS/bin/tune_wavegrad.py

@@ -1,4 +1,5 @@
 """Search a good noise schedule for WaveGrad for a given number of inference iterations"""
+
 import argparse
 from itertools import product as cartesian_product
 

TTS/tts/datasets/dataset.py

@@ -457,9 +457,11 @@ class TTSDataset(Dataset):
 
             # lengths adjusted by the reduction factor
             mel_lengths_adjusted = [
-                m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
+                (
-                if m.shape[1] % self.outputs_per_step
+                    m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
-                else m.shape[1]
+                    if m.shape[1] % self.outputs_per_step
+                    else m.shape[1]
+                )
                 for m in mel
             ]
 

TTS/tts/layers/bark/model.py

@@ -2,6 +2,7 @@
 Much of this code is adapted from Andrej Karpathy's NanoGPT
 (https://github.com/karpathy/nanoGPT)
 """
+
 import math
 from dataclasses import dataclass
 

TTS/tts/layers/bark/model_fine.py

@@ -2,6 +2,7 @@
 Much of this code is adapted from Andrej Karpathy's NanoGPT
 (https://github.com/karpathy/nanoGPT)
 """
+
 import math
 from dataclasses import dataclass
 

TTS/tts/layers/xtts/gpt.py

@@ -187,9 +187,9 @@ class GPT(nn.Module):
     def get_grad_norm_parameter_groups(self):
         return {
             "conditioning_encoder": list(self.conditioning_encoder.parameters()),
-            "conditioning_perceiver": list(self.conditioning_perceiver.parameters())
+            "conditioning_perceiver": (
-            if self.use_perceiver_resampler
+                list(self.conditioning_perceiver.parameters()) if self.use_perceiver_resampler else None
-            else None,
+            ),
             "gpt": list(self.gpt.parameters()),
             "heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()),
         }

TTS/tts/layers/xtts/trainer/dataset.py

@@ -186,9 +186,9 @@ class XTTSDataset(torch.utils.data.Dataset):
             "wav_lengths": torch.tensor(wav.shape[-1], dtype=torch.long),
             "filenames": audiopath,
             "conditioning": cond.unsqueeze(1),
-            "cond_lens": torch.tensor(cond_len, dtype=torch.long)
+            "cond_lens": (
-            if cond_len is not torch.nan
+                torch.tensor(cond_len, dtype=torch.long) if cond_len is not torch.nan else torch.tensor([cond_len])
-            else torch.tensor([cond_len]),
+            ),
             "cond_idxs": torch.tensor(cond_idxs) if cond_idxs is not torch.nan else torch.tensor([cond_idxs]),
         }
         return res

TTS/tts/models/bark.py

@@ -225,14 +225,11 @@ class Bark(BaseTTS):
 
         return return_dict
 
-    def eval_step(self):
+    def eval_step(self): ...
-        ...
 
-    def forward(self):
+    def forward(self): ...
-        ...
 
-    def inference(self):
+    def inference(self): ...
-        ...
 
     @staticmethod
     def init_from_config(config: "BarkConfig", **kwargs):  # pylint: disable=unused-argument

TTS/tts/models/base_tts.py

@@ -369,9 +369,11 @@ class BaseTTS(BaseTrainerModel):
             d_vector = (random.sample(sorted(d_vector), 1),)
 
         aux_inputs = {
-            "speaker_id": None
+            "speaker_id": (
-            if not self.config.use_speaker_embedding
+                None
-            else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
+                if not self.config.use_speaker_embedding
+                else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1)
+            ),
             "d_vector": d_vector,
             "style_wav": None,  # TODO: handle GST style input
         }

TTS/tts/models/tacotron.py

@@ -101,12 +101,16 @@ class Tacotron(BaseTacotron):
                 num_mel=self.decoder_output_dim,
                 encoder_output_dim=self.encoder_in_features,
                 capacitron_VAE_embedding_dim=self.capacitron_vae.capacitron_VAE_embedding_dim,
-                speaker_embedding_dim=self.embedded_speaker_dim
+                speaker_embedding_dim=(
-                if self.use_speaker_embedding and self.capacitron_vae.capacitron_use_speaker_embedding
+                    self.embedded_speaker_dim
-                else None,
+                    if self.use_speaker_embedding and self.capacitron_vae.capacitron_use_speaker_embedding
-                text_summary_embedding_dim=self.capacitron_vae.capacitron_text_summary_embedding_dim
+                    else None
-                if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                ),
-                else None,
+                text_summary_embedding_dim=(
+                    self.capacitron_vae.capacitron_text_summary_embedding_dim
+                    if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    else None
+                ),
             )
 
         # backward pass decoder
@@ -171,9 +175,9 @@ class Tacotron(BaseTacotron):
             encoder_outputs, *capacitron_vae_outputs = self.compute_capacitron_VAE_embedding(
                 encoder_outputs,
                 reference_mel_info=[mel_specs, mel_lengths],
-                text_info=[inputs, text_lengths]
+                text_info=(
-                if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    [inputs, text_lengths] if self.capacitron_vae.capacitron_use_text_summary_embeddings else None
-                else None,
+                ),
                 speaker_embedding=embedded_speakers if self.capacitron_vae.capacitron_use_speaker_embedding else None,
             )
         else:
@@ -237,13 +241,13 @@ class Tacotron(BaseTacotron):
             # B x capacitron_VAE_embedding_dim
             encoder_outputs, *_ = self.compute_capacitron_VAE_embedding(
                 encoder_outputs,
-                reference_mel_info=[aux_input["style_mel"], reference_mel_length]
+                reference_mel_info=(
-                if aux_input["style_mel"] is not None
+                    [aux_input["style_mel"], reference_mel_length] if aux_input["style_mel"] is not None else None
-                else None,
+                ),
                 text_info=[style_text_embedding, style_text_length] if aux_input["style_text"] is not None else None,
-                speaker_embedding=aux_input["d_vectors"]
+                speaker_embedding=(
-                if self.capacitron_vae.capacitron_use_speaker_embedding
+                    aux_input["d_vectors"] if self.capacitron_vae.capacitron_use_speaker_embedding else None
-                else None,
+                ),
             )
         if self.num_speakers > 1:
             if not self.use_d_vector_file:

TTS/tts/models/tacotron2.py

@@ -113,12 +113,14 @@ class Tacotron2(BaseTacotron):
                 num_mel=self.decoder_output_dim,
                 encoder_output_dim=self.encoder_in_features,
                 capacitron_VAE_embedding_dim=self.capacitron_vae.capacitron_VAE_embedding_dim,
-                speaker_embedding_dim=self.embedded_speaker_dim
+                speaker_embedding_dim=(
-                if self.capacitron_vae.capacitron_use_speaker_embedding
+                    self.embedded_speaker_dim if self.capacitron_vae.capacitron_use_speaker_embedding else None
-                else None,
+                ),
-                text_summary_embedding_dim=self.capacitron_vae.capacitron_text_summary_embedding_dim
+                text_summary_embedding_dim=(
-                if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    self.capacitron_vae.capacitron_text_summary_embedding_dim
-                else None,
+                    if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    else None
+                ),
             )
 
         # backward pass decoder
@@ -191,9 +193,11 @@ class Tacotron2(BaseTacotron):
             encoder_outputs, *capacitron_vae_outputs = self.compute_capacitron_VAE_embedding(
                 encoder_outputs,
                 reference_mel_info=[mel_specs, mel_lengths],
-                text_info=[embedded_inputs.transpose(1, 2), text_lengths]
+                text_info=(
-                if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    [embedded_inputs.transpose(1, 2), text_lengths]
-                else None,
+                    if self.capacitron_vae.capacitron_use_text_summary_embeddings
+                    else None
+                ),
                 speaker_embedding=embedded_speakers if self.capacitron_vae.capacitron_use_speaker_embedding else None,
             )
         else:
@@ -265,13 +269,13 @@ class Tacotron2(BaseTacotron):
             # B x capacitron_VAE_embedding_dim
             encoder_outputs, *_ = self.compute_capacitron_VAE_embedding(
                 encoder_outputs,
-                reference_mel_info=[aux_input["style_mel"], reference_mel_length]
+                reference_mel_info=(
-                if aux_input["style_mel"] is not None
+                    [aux_input["style_mel"], reference_mel_length] if aux_input["style_mel"] is not None else None
-                else None,
+                ),
                 text_info=[style_text_embedding, style_text_length] if aux_input["style_text"] is not None else None,
-                speaker_embedding=aux_input["d_vectors"]
+                speaker_embedding=(
-                if self.capacitron_vae.capacitron_use_speaker_embedding
+                    aux_input["d_vectors"] if self.capacitron_vae.capacitron_use_speaker_embedding else None
-                else None,
+                ),
             )
 
         if self.num_speakers > 1:

TTS/tts/models/tortoise.py

@@ -715,8 +715,9 @@ class Tortoise(BaseTTS):
             self.autoregressive = self.autoregressive.to(self.device)
             if verbose:
                 print("Generating autoregressive samples..")
-            with self.temporary_cuda(self.autoregressive) as autoregressive, torch.autocast(
+            with (
-                device_type="cuda", dtype=torch.float16, enabled=half
+                self.temporary_cuda(self.autoregressive) as autoregressive,
+                torch.autocast(device_type="cuda", dtype=torch.float16, enabled=half),
             ):
                 for b in tqdm(range(num_batches), disable=not verbose):
                     codes = autoregressive.inference_speech(
@@ -737,8 +738,9 @@ class Tortoise(BaseTTS):
             self.autoregressive_batch_size = orig_batch_size  # in the case of single_sample
 
             clip_results = []
-            with self.temporary_cuda(self.clvp) as clvp, torch.autocast(
+            with (
-                device_type="cuda", dtype=torch.float16, enabled=half
+                self.temporary_cuda(self.clvp) as clvp,
+                torch.autocast(device_type="cuda", dtype=torch.float16, enabled=half),
             ):
                 for batch in tqdm(samples, disable=not verbose):
                     for i in range(batch.shape[0]):

TTS/tts/models/vits.py

@@ -1887,9 +1887,11 @@ class Vits(BaseTTS):
         import onnxruntime as ort
 
         providers = [
-            "CPUExecutionProvider"
+            (
-            if cuda is False
+                "CPUExecutionProvider"
-            else ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"})
+                if cuda is False
+                else ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"})
+            )
         ]
         sess_options = ort.SessionOptions()
         self.onnx_sess = ort.InferenceSession(

TTS/tts/utils/ssim.py

@@ -207,6 +207,7 @@ class SSIMLoss(_Loss):
         https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
         DOI:`10.1109/TIP.2003.819861`
     """
+
     __constants__ = ["kernel_size", "k1", "k2", "sigma", "kernel", "reduction"]
 
     def __init__(

TTS/tts/utils/text/cleaners.py

@@ -1,4 +1,5 @@
 """Set of default text cleaners"""
+
 # TODO: pick the cleaner for languages dynamically
 
 import re

TTS/utils/download.py

@@ -36,13 +36,16 @@ def stream_url(
     if start_byte:
         req.headers["Range"] = "bytes={}-".format(start_byte)
 
-    with urllib.request.urlopen(req) as upointer, tqdm(
+    with (
-        unit="B",
+        urllib.request.urlopen(req) as upointer,
-        unit_scale=True,
+        tqdm(
-        unit_divisor=1024,
+            unit="B",
-        total=url_size,
+            unit_scale=True,
-        disable=not progress_bar,
+            unit_divisor=1024,
-    ) as pbar:
+            total=url_size,
+            disable=not progress_bar,
+        ) as pbar,
+    ):
         num_bytes = 0
         while True:
             chunk = upointer.read(block_size)

TTS/vc/models/base_vc.py

@@ -357,9 +357,11 @@ class BaseVC(BaseTrainerModel):
             d_vector = (random.sample(sorted(d_vector), 1),)
 
         aux_inputs = {
-            "speaker_id": None
+            "speaker_id": (
-            if not self.config.use_speaker_embedding
+                None
-            else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
+                if not self.config.use_speaker_embedding
+                else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1)
+            ),
             "d_vector": d_vector,
             "style_wav": None,  # TODO: handle GST style input
         }

TTS/vc/models/freevc.py

@@ -544,8 +544,7 @@ class FreeVC(BaseVC):
         audio = audio[0][0].data.cpu().float().numpy()
         return audio
 
-    def eval_step():
+    def eval_step(): ...
-        ...
 
     @staticmethod
     def init_from_config(config: FreeVCConfig, samples: Union[List[List], List[Dict]] = None, verbose=True):
@@ -558,5 +557,4 @@ class FreeVC(BaseVC):
         if eval:
             self.eval()
 
-    def train_step():
+    def train_step(): ...
-        ...

TTS/vc/modules/freevc/wavlm/wavlm.py

@@ -155,7 +155,9 @@ def compute_mask_indices(
 
 class WavLMConfig:
     def __init__(self, cfg=None):
-        self.extractor_mode: str = "default"  # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
+        self.extractor_mode: str = (
+            "default"  # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True)
+        )
         self.encoder_layers: int = 12  # num encoder layers in the transformer
 
         self.encoder_embed_dim: int = 768  # encoder embedding dimension
@@ -164,7 +166,9 @@ class WavLMConfig:
         self.activation_fn: str = "gelu"  # activation function to use
 
         self.layer_norm_first: bool = False  # apply layernorm first in the transformer
-        self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"  # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
+        self.conv_feature_layers: str = (
+            "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2"  # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]
+        )
         self.conv_bias: bool = False  # include bias in conv encoder
         self.feature_grad_mult: float = 1.0  # multiply feature extractor var grads by this
 

tests/text_tests/test_phonemizer.py

@@ -234,8 +234,12 @@ class TestZH_CN_Phonemizer(unittest.TestCase):
 class TestBN_Phonemizer(unittest.TestCase):
     def setUp(self):
         self.phonemizer = BN_Phonemizer()
-        self._TEST_CASES = "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে, কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয়, তখনও যেন"
+        self._TEST_CASES = (
-        self._EXPECTED = "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয় তখনও যেন।"
+            "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে, কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয়, তখনও যেন"
+        )
+        self._EXPECTED = (
+            "রাসূলুল্লাহ সাল্লাল্লাহু আলাইহি ওয়া সাল্লাম শিক্ষা দিয়েছেন যে কেউ যদি কোন খারাপ কিছুর সম্মুখীন হয় তখনও যেন।"
+        )
 
     def test_phonemize(self):
         self.assertEqual(self.phonemizer.phonemize(self._TEST_CASES, separator=""), self._EXPECTED)

tests/vc_tests/test_freevc.py

@@ -115,20 +115,14 @@ class TestFreeVC(unittest.TestCase):
             output_wav.shape[0] + config.audio.hop_length == source_wav.shape[0]
         ), f"{output_wav.shape} != {source_wav.shape}"
 
-    def test_train_step(self):
+    def test_train_step(self): ...
-        ...
 
-    def test_train_eval_log(self):
+    def test_train_eval_log(self): ...
-        ...
 
-    def test_test_run(self):
+    def test_test_run(self): ...
-        ...
 
-    def test_load_checkpoint(self):
+    def test_load_checkpoint(self): ...
-        ...
 
-    def test_get_criterion(self):
+    def test_get_criterion(self): ...
-        ...
 
-    def test_init_from_config(self):
+    def test_init_from_config(self): ...
-        ...