Add option to sort input sequnce by audio len

TTS/bin/compute_attention_masks.py

@@ -97,7 +97,7 @@ Example run:
         enable_eos_bos=C.enable_eos_bos_chars,
     )
 
-    dataset.sort_items()
+    dataset.sort_and_filter_items(C.get("sort_by_audio_len", default=False))
     loader = DataLoader(
         dataset,
         batch_size=args.batch_size,

TTS/bin/extract_tts_spectrograms.py

@@ -46,7 +46,7 @@ def setup_loader(ap, r, verbose=False):
     if c.use_phonemes and c.compute_input_seq_cache:
         # precompute phonemes to have a better estimate of sequence lengths.
         dataset.compute_input_seq(c.num_loader_workers)
-    dataset.sort_items()
+    dataset.sort_and_filter_items(c.get("sort_by_audio_len", default=False))
 
     loader = DataLoader(
         dataset,

TTS/trainer.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 
 import importlib
-import logging
 import multiprocessing
 import os
 import platform

TTS/tts/configs/vits_config.py

@@ -120,8 +120,9 @@ class VitsConfig(BaseTTSConfig):
     compute_linear_spec: bool = True
 
     # overrides
-    min_seq_len: int = 32
+    sort_by_audio_len: bool = True
-    max_seq_len: int = 1000
+    min_seq_len: int = 0
+    max_seq_len: int = 500000
     r: int = 1  # DO NOT CHANGE
     add_blank: bool = True
 

TTS/tts/datasets/TTSDataset.py

@@ -69,7 +69,7 @@ class TTSDataset(Dataset):
                 batch. Set 0 to disable. Defaults to 0.
 
             min_seq_len (int): Minimum input sequence length to be processed
-                by the loader. Filter out input sequences that are shorter than this. Some models have a
+                by sort_inputs`. Filter out input sequences that are shorter than this. Some models have a
                 minimum input length due to its architecture. Defaults to 0.
 
             max_seq_len (int): Maximum input sequence length. Filter out input sequences that are longer than this.
@@ -302,10 +302,23 @@ class TTSDataset(Dataset):
                     for idx, p in enumerate(phonemes):
                         self.items[idx][0] = p
 
-    def sort_items(self):
+    def sort_and_filter_items(self, by_audio_len=False):
-        r"""Sort instances based on text length in ascending order"""
+        r"""Sort `items` based on text length or audio length in ascending order. Filter out samples out or the length
-        lengths = np.array([len(ins[0]) for ins in self.items])
+        range.
 
+        Args:
+            by_audio_len (bool): if True, sort by audio length else by text length.
+        """
+        # compute the target sequence length
+        if by_audio_len:
+            lengths = []
+            for item in self.items:
+                lengths.append(os.path.getsize(item[1]))
+            lengths = np.array(lengths)
+        else:
+            lengths = np.array([len(ins[0]) for ins in self.items])
+
+        # sort items based on the sequence length in ascending order
         idxs = np.argsort(lengths)
         new_items = []
         ignored = []
@@ -315,7 +328,10 @@ class TTSDataset(Dataset):
                 ignored.append(idx)
             else:
                 new_items.append(self.items[idx])
+
         # shuffle batch groups
+        # create batches with similar length items
+        # the larger the `batch_group_size`, the higher the length variety in a batch.
         if self.batch_group_size > 0:
             for i in range(len(new_items) // self.batch_group_size):
                 offset = i * self.batch_group_size
@@ -325,6 +341,7 @@ class TTSDataset(Dataset):
                 new_items[offset:end_offset] = temp_items
         self.items = new_items
 
+        # logging
         if self.verbose:
             print(" | > Max length sequence: {}".format(np.max(lengths)))
             print(" | > Min length sequence: {}".format(np.min(lengths)))

TTS/tts/models/base_tts.py

@@ -243,7 +243,7 @@ class BaseTTS(BaseModel):
                 dist.barrier()
 
             # sort input sequences from short to long
-            dataset.sort_items()
+            dataset.sort_and_filter_items(config.get("sort_by_audio_len", default=False))
 
             # sampler for DDP
             sampler = DistributedSampler(dataset) if num_gpus > 1 else None

recipes/ljspeech/vits_tts/train_vits.py

@@ -43,7 +43,7 @@ config = VitsConfig(
     print_step=25,
     print_eval=True,
     mixed_precision=True,
-    max_seq_len=5000,
+    max_seq_len=500000,
     output_path=output_path,
     datasets=[dataset_config],
 )

tests/data_tests/test_loader.py

@@ -124,7 +124,7 @@ class TestTTSDataset(unittest.TestCase):
 
                 avg_length = mel_lengths.numpy().mean()
                 assert avg_length >= last_length
-            dataloader.dataset.sort_items()
+            dataloader.dataset.sort_and_filter_items()
             is_items_reordered = False
             for idx, item in enumerate(dataloader.dataset.items):
                 if item != frames[idx]: