mirror of https://github.com/coqui-ai/TTS.git
fix Lint check
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@ -229,14 +229,14 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
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items = []
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meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
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for meta_file in meta_files:
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txt, speaker_id, txt_file = os.path.relpath(meta_file,root_path).split(os.sep)
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_, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
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file_id = txt_file.split('.')[0]
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if isinstance(test_speakers, list): # if is list ignore this speakers ids
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if speaker_id in test_speakers:
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continue
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with open(meta_file) as file_text:
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text = file_text.readlines()[0]
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wav_file = os.path.join(root_path, wavs_path, speaker_id,file_id+'.wav')
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wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id+'.wav')
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items.append([text, wav_file, speaker_id])
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return items
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@ -58,7 +58,7 @@ class Tacotron2(TacotronAbstract):
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else:
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# if speaker_embedding_dim is not None we need use speaker embedding per sample
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self.embeddings_per_sample = True
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# speaker and gst embeddings is concat in decoder input
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if num_speakers > 1:
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decoder_in_features = decoder_in_features + speaker_embedding_dim # add speaker embedding dim
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@ -73,7 +73,7 @@ class Tacotron2(TacotronAbstract):
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if not self.embeddings_per_sample:
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self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
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self.speaker_embedding.weight.data.normal_(0, 0.3)
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# base model layers
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self.encoder = Encoder(encoder_in_features)
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self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
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