REBASED: Add support for the speaker encoder training using torch spectrograms

TTS/bin/train_encoder.py

@@ -46,6 +46,7 @@ def setup_loader(ap: AudioProcessor, is_val: bool = False, verbose: bool = False
             sample_from_storage_p=c.storage["sample_from_storage_p"],
             verbose=verbose,
             augmentation_config=c.audio_augmentation,
+            use_torch_spec=c.model_params.get("use_torch_spec", False),
         )
 
         # sampler = DistributedSampler(dataset) if num_gpus > 1 else None

TTS/speaker_encoder/dataset.py

@@ -20,6 +20,7 @@ def __init__(
         skip_speakers=False,
         verbose=False,
         augmentation_config=None,
+        use_torch_spec=None,
     ):
         """
         Args:
@@ -37,6 +38,7 @@ def __init__(
         self.skip_speakers = skip_speakers
         self.ap = ap
         self.verbose = verbose
+        self.use_torch_spec = use_torch_spec
         self.__parse_items()
         storage_max_size = storage_size * num_speakers_in_batch
         self.storage = Storage(
@@ -72,22 +74,6 @@ def load_wav(self, filename):
         audio = self.ap.load_wav(filename, sr=self.ap.sample_rate)
         return audio
 
-    def load_data(self, idx):
-        text, wav_file, speaker_name = self.items[idx]
-        wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)
-        mel = self.ap.melspectrogram(wav).astype("float32")
-        # sample seq_len
-
-        assert text.size > 0, self.items[idx]["audio_file"]
-        assert wav.size > 0, self.items[idx]["audio_file"]
-
-        sample = {
-            "mel": mel,
-            "item_idx": self.items[idx]["audio_file"],
-            "speaker_name": speaker_name,
-        }
-        return sample
-
     def __parse_items(self):
         self.speaker_to_utters = {}
         for i in self.items:
@@ -241,8 +227,12 @@ def collate_fn(self, batch):
                             self.gaussian_augmentation_config["max_amplitude"],
                             size=len(wav),
                         )
-                mel = self.ap.melspectrogram(wav)
-                feats_.append(torch.FloatTensor(mel))
+
+                if not self.use_torch_spec:
+                    mel = self.ap.melspectrogram(wav)
+                    feats_.append(torch.FloatTensor(mel))
+                else:
+                    feats_.append(torch.FloatTensor(wav))
 
             labels.append(torch.LongTensor(labels_))
             feats.extend(feats_)

TTS/tts/datasets/formatters.py

@@ -334,21 +334,22 @@ def vctk(root_path, meta_files=None, wavs_path="wav48_silence_trimmed", mic="mic
     return items
 
 
-def vctk_old(root_path, meta_files=None, wavs_path="wav48"):
+def vctk_old(root_path, meta_files=None, wavs_path="wav48", ignored_speakers=None):
     """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
-    test_speakers = meta_files
     items = []
     meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
     for meta_file in meta_files:
         _, speaker_id, txt_file = os.path.relpath(meta_file, root_path).split(os.sep)
         file_id = txt_file.split(".")[0]
-        if isinstance(test_speakers, list):  # if is list ignore this speakers ids
-            if speaker_id in test_speakers:
+        # ignore speakers
+        if isinstance(ignored_speakers, list):
+            if speaker_id in ignored_speakers:
                 continue
         with open(meta_file, "r", encoding="utf-8") as file_text:
             text = file_text.readlines()[0]
         wav_file = os.path.join(root_path, wavs_path, speaker_id, file_id + ".wav")
-        items.append([text, wav_file, "VCTK_old_" + speaker_id])
+        items.append([text, wav_file, "VCTK_" + speaker_id])
+
     return items