Modify SpeechSynthesisDataset class, make it return text

lhotse/dataset/speech_synthesis.py

@@ -4,7 +4,7 @@
 
 from lhotse import validate
 from lhotse.cut import CutSet
-from lhotse.dataset.collation import TokenCollater, collate_audio
+from lhotse.dataset.collation import collate_audio
 from lhotse.dataset.input_strategies import BatchIO, PrecomputedFeatures
 from lhotse.utils import ifnone
 
@@ -19,31 +19,34 @@
         {
             'audio': (B x NumSamples) float tensor
             'features': (B x NumFrames x NumFeatures) float tensor
-            'tokens': (B x NumTokens) long tensor
             'audio_lens': (B, ) int tensor
             'features_lens': (B, ) int tensor
-            'tokens_lens': (B, ) int tensor
-            'speakers': List[str] of len B (optional) # if return_spk_ids is True
+            'text': List[str] of len B  # when return_text=True
+            'tokens': List[List[str]]  # when return_tokens=True
+            'speakers': List[str] of len B  # when return_spk_ids=True
+            'cut': List of Cuts  # when return_cuts=True
         }
     """
 
     def __init__(
         self,
-        cuts: CutSet,
         cut_transforms: List[Callable[[CutSet], CutSet]] = None,
         feature_input_strategy: BatchIO = PrecomputedFeatures(),
         feature_transforms: Union[Sequence[Callable], Callable] = None,
-        add_eos: bool = True,
-        add_bos: bool = True,
+        return_text: bool = True,
+        return_tokens: bool = False,
         return_spk_ids: bool = False,
+        return_cuts: bool = False,
     ) -> None:
         super().__init__()
 
-        self.cuts = cuts
-        self.token_collater = TokenCollater(cuts, add_eos=add_eos, add_bos=add_bos)
         self.cut_transforms = ifnone(cut_transforms, [])
         self.feature_input_strategy = feature_input_strategy
+
+        self.return_text = return_text
+        self.return_tokens = return_tokens
         self.return_spk_ids = return_spk_ids
+        self.return_cuts = return_cuts
 
         if feature_transforms is None:
             feature_transforms = []
@@ -67,18 +70,28 @@
         for transform in self.feature_transforms:
             features = transform(features)
 
-        tokens, tokens_lens = self.token_collater(cuts)
         batch = {
             "audio": audio,
             "features": features,
-            "tokens": tokens,
             "audio_lens": audio_lens,
             "features_lens": features_lens,
-            "tokens_lens": tokens_lens,
         }
+
+        if self.return_text:
+            # use normalized text
+            text = [cut.supervisions[0].normalized_text for cut in cuts]
+            batch["text"] = text
+
+        if self.return_tokens:
+            tokens = [cut.tokens for cut in cuts]
+            batch["tokens"] = tokens
+
         if self.return_spk_ids:
             batch["speakers"] = [cut.supervisions[0].speaker for cut in cuts]
 
+        if self.return_cuts:
+            batch["cut"] = [cut for cut in cuts]
+
         return batch
 
 

lhotse/recipes/ljspeech.py

@@ -72,7 +72,7 @@ def prepare_ljspeech(
     supervisions = []
     with open(metadata_csv_path) as f:
         for line in f:
-            recording_id, text, normalized = line.split("|")
+            recording_id, text, normalized = line.strip().split("|")
             audio_path = corpus_dir / "wavs" / f"{recording_id}.wav"
             if not audio_path.is_file():
                 logging.warning(f"No such file: {audio_path}")

test/dataset/test_speech_synthesis_dataset.py

@@ -20,20 +20,18 @@ def test_speech_synthesis_dataset(cut_set, transform):
     else:
         transform = None
 
-    dataset = SpeechSynthesisDataset(cut_set, feature_transforms=transform)
+    dataset = SpeechSynthesisDataset(feature_transforms=transform)
     example = dataset[cut_set]
     assert example["audio"].shape[1] > 0
     assert example["features"].shape[1] > 0
-    assert example["tokens"].shape[1] > 0
+    assert len(example["text"]) > 0
+    assert len(example["text"][0]) > 0
 
     assert example["audio"].ndim == 2
     assert example["features"].ndim == 3
-    assert example["tokens"].ndim == 2
 
     assert isinstance(example["audio_lens"], torch.IntTensor)
     assert isinstance(example["features_lens"], torch.IntTensor)
-    assert isinstance(example["tokens_lens"], torch.IntTensor)
 
     assert example["audio_lens"].ndim == 1
     assert example["features_lens"].ndim == 1
-    assert example["tokens_lens"].ndim == 1

test/fixtures/ljspeech/cuts.json

@@ -12,6 +12,7 @@
         "duration": 1.5396371882086168,
         "channel": 0,
         "text": "IN EIGHTEEN THIRTEEN",
+        "custom":{"normalized_text": "IN EIGHTEEN THIRTEEN"},
         "language": "English",
         "gender": "female"
       }
@@ -59,6 +60,7 @@
         "duration": 1.5976870748299319,
         "channel": 0,
         "text": "EIGHT THE PRESS YARD",
+        "custom":{"normalized_text": "EIGHT THE PRESS YARD"},
         "language": "English",
         "gender": "female"
       }
@@ -93,4 +95,4 @@
     },
     "type": "MonoCut"
   }
-]
+]