Fix unpicklable dataset by removing local lambda

data/ft_dataset.py

@@ -12,27 +12,26 @@
     "max": 30.0,
 }
 # assume single speaker
+def to_mel_fn(wave, mel_fn_args):
+    return mel_spectrogram(wave, **mel_fn_args)
+
 class FT_Dataset(torch.utils.data.Dataset):
-    def __init__(self,
-                 data_path,
-                 spect_params,
-                 sr=22050,
-                 batch_size=1,
-                 ):
+    def __init__(
+        self,
+        data_path,
+        spect_params,
+        sr=22050,
+        batch_size=1,
+    ):
         self.data_path = data_path
-        # recursively find all files in data_path
         self.data = []
         for root, _, files in os.walk(data_path):
             for file in files:
-                if (file.endswith(".wav") or
-                        file.endswith(".mp3") or
-                        file.endswith(".flac") or
-                        file.endswith(".ogg") or
-                        file.endswith(".m4a") or
-                        file.endswith(".opus")):
+                if file.endswith((".wav", ".mp3", ".flac", ".ogg", ".m4a", ".opus")):
                     self.data.append(os.path.join(root, file))
 
-        mel_fn_args = {
+        self.sr = sr
+        self.mel_fn_args = {
             "n_fft": spect_params['n_fft'],
             "win_size": spect_params['win_length'],
             "hop_size": spect_params['hop_length'],
@@ -42,11 +41,8 @@ def __init__(self,
             "fmax": None if spect_params['fmax'] == "None" else spect_params['fmax'],
             "center": False
         }
-        self.to_mel = lambda x: mel_spectrogram(x, **mel_fn_args)
-        self.sr = sr
 
         assert len(self.data) != 0
-        # if dataset length is less than batch size, repeat the dataset
         while len(self.data) < batch_size:
             self.data += self.data
 
@@ -64,17 +60,14 @@ def __getitem__(self, idx):
         if len(speech) < self.sr * duration_setting["min"] or len(speech) > self.sr * duration_setting["max"]:
             print(f"Audio {wav_path} is too short or too long, skipping")
             return self.__getitem__(random.randint(0, len(self)))
-        return_dict = {
-            'audio': speech,
-            'sr': orig_sr
-        }
-        wave, orig_sr = return_dict['audio'], return_dict['sr']
         if orig_sr != self.sr:
-            wave = librosa.resample(wave, orig_sr, self.sr)
-        wave = torch.from_numpy(wave).float()
-        mel = self.to_mel(wave.unsqueeze(0)).squeeze(0)
+            speech = librosa.resample(speech, orig_sr, self.sr)
+
+        wave = torch.from_numpy(speech).float().unsqueeze(0)
+        mel = to_mel_fn(wave, self.mel_fn_args).squeeze(0)
+
+        return wave.squeeze(0), mel
 
-        return wave, mel
 
 def build_ft_dataloader(data_path, spect_params, sr, batch_size=1, num_workers=0):
     dataset = FT_Dataset(data_path, spect_params, sr, batch_size)
@@ -130,4 +123,4 @@ def collate(batch):
         wave, mel, wave_lengths, mel_lengths = batch
         print(wave.shape, mel.shape)
         if idx == 10:
-            break
+            break