fix Lint checks

TTS/bin/compute_embeddings.py

@@ -1,16 +1,16 @@
 import argparse
-import glob
 import os
 
 import torch
 import numpy as np
 from tqdm import tqdm
+from TTS.config import load_config
 
 from TTS.speaker_encoder.utils.generic_utils import setup_model
 from TTS.tts.datasets.preprocess import load_meta_data
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.audio import AudioProcessor
-from TTS.config import load_config
+
 
 parser = argparse.ArgumentParser(
     description='Compute embedding vectors for each wav file in a dataset.'

TTS/bin/find_unique_chars.py

@@ -1,6 +1,5 @@
 """Find all the unique characters in a dataset"""
 import argparse
-import os
 from argparse import RawTextHelpFormatter
 from TTS.tts.datasets.preprocess import load_meta_data
 from TTS.config import load_config
@@ -31,7 +30,8 @@ def main():
     texts = "".join(item[0] for item in items)
     chars = set(texts)
     lower_chars = filter(lambda c: c.islower(), chars)
-    chars_force_lower = set([c.lower() for c in chars])
+    chars_force_lower = [c.lower() for c in chars])
+    chars_force_lower = set(chars_force_lower)
 
     print(f" > Number of unique characters: {len(chars)}")
     print(f" > Unique characters: {''.join(sorted(chars))}")

TTS/tts/datasets/preprocess.py

@@ -365,12 +365,11 @@ def mls(root_path, meta_files=None):
     """http://www.openslr.org/94/"""
     items = []
     with open(os.path.join(root_path, meta_files), "r") as meta:
-        isTrain = "train" in meta_files
         for line in meta:
             file, text = line.split('\t')
             text = text[:-1]
-            speaker, book, no = file.split('_')
-            wav_file = os.path.join(root_path, "train" if isTrain else "dev", 'audio', speaker, book, file + ".wav")
+            speaker, book, *_ = file.split('_')
+            wav_file = os.path.join(root_path, os.path.dirname(meta_files), 'audio', speaker, book, file + ".wav")
             items.append([text, wav_file, "MLS_" + speaker])
     return items
 

tests/test_speaker_encoder.py

@@ -34,7 +34,7 @@ class LSTMSpeakerEncoderTests(unittest.TestCase):
         assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
         # compute d for a given batch
         dummy_input = T.rand(1, 240, 80)  # B x T x D
-        output = model.compute_embedding(dummy_input, num_frames=160, overlap=0.5)
+        output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
         assert output.shape[0] == 1
         assert output.shape[1] == 256
         assert len(output.shape) == 2