Add fullband-melgan DE vocoder

TTS/.models.json

@@ -230,6 +230,11 @@
                     "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.0.11/vocoder_models--de--thorsten--wavegrad.zip",
                     "author": "@thorstenMueller",
                     "commit": "unknown"
+                },
+                "fullband-melgan":{
+                    "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.1.3/vocoder_models--de--thorsten--fullband-melgan.zip",
+                    "author": "@thorstenMueller",
+                    "commit": "unknown"
                 }
             }
         }

TTS/bin/compute_embeddings.py

@@ -1,21 +1,20 @@
-
 import argparse
 import os
+from argparse import RawTextHelpFormatter
 
 from tqdm import tqdm
 
-from argparse import RawTextHelpFormatter
 from TTS.config import load_config
 from TTS.tts.datasets import load_meta_data
 from TTS.tts.utils.speakers import SpeakerManager
 
 parser = argparse.ArgumentParser(
     description="""Compute embedding vectors for each wav file in a dataset.\n\n"""
-        """
+    """
     Example runs:
     python TTS/bin/compute_embeddings.py speaker_encoder_model.pth.tar speaker_encoder_config.json  dataset_config.json embeddings_output_path/
     """,
-        formatter_class=RawTextHelpFormatter,
+    formatter_class=RawTextHelpFormatter,
 )
 parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
 parser.add_argument(
@@ -40,7 +39,9 @@ c_dataset = load_config(args.config_dataset_path)
 meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval)
 wav_files = meta_data_train + meta_data_eval
 
-speaker_manager = SpeakerManager(encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda)
+speaker_manager = SpeakerManager(
+    encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda
+)
 
 # compute speaker embeddings
 speaker_mapping = {}
@@ -62,7 +63,7 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
 
 if speaker_mapping:
     # save speaker_mapping if target dataset is defined
-    if '.json' not in args.output_path:
+    if ".json" not in args.output_path:
         mapping_file_path = os.path.join(args.output_path, "speakers.json")
     else:
         mapping_file_path = args.output_path

TTS/bin/find_unique_chars.py

@@ -1,8 +1,9 @@
 """Find all the unique characters in a dataset"""
 import argparse
 from argparse import RawTextHelpFormatter
-from TTS.tts.datasets import load_meta_data
+
 from TTS.config import load_config
+from TTS.tts.datasets import load_meta_data
 
 
 def main():
@@ -16,9 +17,7 @@ def main():
     """,
         formatter_class=RawTextHelpFormatter,
     )
-    parser.add_argument(
-        "--config_path", type=str, help="Path to dataset config file.", required=True
-    )
+    parser.add_argument("--config_path", type=str, help="Path to dataset config file.", required=True)
     args = parser.parse_args()
 
     c = load_config(args.config_path)
@@ -38,5 +37,6 @@ def main():
     print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
     print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
 
+
 if __name__ == "__main__":
     main()

TTS/speaker_encoder/models/lstm.py

@@ -1,5 +1,5 @@
-import torch
 import numpy as np
+import torch
 from torch import nn
 
 
@@ -81,12 +81,12 @@ class LSTMSpeakerEncoder(nn.Module):
         if max_len < num_frames:
             num_frames = max_len
 
-        offsets = np.linspace(0, max_len-num_frames, num=num_eval)
+        offsets = np.linspace(0, max_len - num_frames, num=num_eval)
 
         frames_batch = []
         for offset in offsets:
             offset = int(offset)
-            end_offset = int(offset+num_frames)
+            end_offset = int(offset + num_frames)
             frames = x[:, offset:end_offset]
             frames_batch.append(frames)
 

TTS/speaker_encoder/models/resnet.py

@@ -207,4 +207,4 @@ class ResNetSpeakerEncoder(nn.Module):
             self.cuda()
         if eval:
             self.eval()
-            assert not self.training
+            assert not self.training

TTS/tts/datasets/formatters.py

@@ -291,18 +291,20 @@ def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
 
     return items
 
+
 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:
         for line in meta:
-            file, text = line.split('\t')
+            file, text = line.split("\t")
             text = text[:-1]
-            speaker, book, *_ = file.split('_')
-            wav_file = os.path.join(root_path, os.path.dirname(meta_files), '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
 
+
 # ======================================== VOX CELEB ===========================================
 def voxceleb2(root_path, meta_file=None):
     """