Implement most similar ref training approach

TTS/tts/layers/xtts/trainer/dataset.py

@@ -88,6 +88,7 @@ class XTTSDataset(torch.utils.data.Dataset):
         self.sample_rate = sample_rate
         self.max_wav_len = model_args.max_wav_length
         self.max_text_len = model_args.max_text_length
+        self.use_masking_gt_as_prompt = model_args.use_masking_gt_as_prompt
         assert self.max_wav_len is not None and self.max_text_len is not None
 
         self.samples = samples
@@ -109,7 +110,7 @@ class XTTSDataset(torch.utils.data.Dataset):
             try:
                 tseq, _, wav, _, _, _ = self.load_item(sample)
             except:
-                pass
+                continue
             # Basically, this audio file is nonexistent or too long to be supported by the dataset.
             if (
                 wav is None
@@ -140,10 +141,18 @@ class XTTSDataset(torch.utils.data.Dataset):
             # Ultra short clips are also useless (and can cause problems within some models).
             raise ValueError
 
-        # get a slice from GT to condition the model
-        cond, cond_len, cond_idxs = get_prompt_slice(
-            audiopath, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval
-        )
+        if self.use_masking_gt_as_prompt:
+            # get a slice from GT to condition the model
+            cond, cond_len, cond_idxs = get_prompt_slice(
+                audiopath, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval
+            )
+        else:
+            ref_sample = sample["reference_path"] if "reference_path" in sample and sample["reference_path"] is not None else audiopath
+            cond, cond_len, cond_idxs = get_prompt_slice(
+                ref_sample, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval
+            )
+            cond_idxs = torch.nan
+            cond_len = torch.nan
 
         return tseq, audiopath, wav, cond, cond_len, cond_idxs
 
@@ -199,8 +208,8 @@ class XTTSDataset(torch.utils.data.Dataset):
             "wav_lengths": torch.tensor(wav.shape[-1], dtype=torch.long),
             "filenames": audiopath,
             "conditioning": cond.unsqueeze(1),
-            "cond_lens": torch.tensor(cond_len, dtype=torch.long),
-            "cond_idxs": torch.tensor(cond_idxs),
+            "cond_lens": torch.tensor(cond_len, dtype=torch.long) if cond_len is not torch.nan else torch.tensor([cond_len]),
+            "cond_idxs": torch.tensor(cond_idxs) if cond_idxs is not torch.nan else torch.tensor([cond_len]),
         }
         return res
 
@@ -221,6 +230,10 @@ class XTTSDataset(torch.utils.data.Dataset):
         batch["conditioning"] = torch.stack(batch["conditioning"])
         batch["cond_lens"] = torch.stack(batch["cond_lens"])
         batch["cond_idxs"] = torch.stack(batch["cond_idxs"])
+        if torch.any(batch["cond_idxs"].isnan()):
+            batch["cond_lens"] = None
+            batch["cond_idxs"] = None
+
         max_text_len = batch["text_lengths"].max()
         max_wav_len = batch["wav_lengths"].max()
 

TTS/tts/layers/xtts/trainer/gpt_trainer.py

@@ -52,6 +52,7 @@ class GPTArgs(XttsArgs):
     xtts_checkpoint: str = ""
     gpt_checkpoint: str = ""  # if defined it will replace the gpt weights on xtts model
     vocoder: str = ""  # overide vocoder key on the config to avoid json write issues
+    use_masking_gt_as_prompt: bool = True
 
 
 def callback_clearml_load_save(operation_type, model_info):