Add reproducible evaluation

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

@@ -63,35 +63,48 @@ def load_audio(audiopath, sampling_rate):
     return audio
 
 class XTTSDataset(torch.utils.data.Dataset):
-    def __init__(self, config, samples, tokenizer, sample_rate):
+    def __init__(self, config, samples, tokenizer, sample_rate, is_eval=False):
         self.config = config
         model_args = config.model_args
         self.failed_samples = set()
         self.debug_failures = model_args.debug_loading_failures
         self.max_conditioning_length = model_args.max_conditioning_length
         self.min_conditioning_length = model_args.min_conditioning_length
-
-        # self.samples = []
-        # cache the samples and added type "0" for all samples
-        # ToDo: find a better way to deal with type
-        # for item in samples:
-        #     self.samples.append([item['audio_file'], item["text"], 0])
-        self.samples = samples
-        random.seed(config.training_seed)
-        # random.shuffle(self.samples)
-        random.shuffle(self.samples)
-        # order by language
-        self.samples = key_samples_by_col(self.samples, "language")
-        print(" > Sampling by language:", self.samples.keys())
-
-        # use always the output sampling rate to load in the highest quality
+        self.is_eval = is_eval
+        self.tokenizer = tokenizer
         self.sample_rate = sample_rate
         self.max_wav_len = model_args.max_wav_length
         self.max_text_len = model_args.max_text_length
         assert self.max_wav_len is not None and self.max_text_len is not None
 
-        # load specific vocabulary
-        self.tokenizer = tokenizer
+        self.samples = samples
+        if not is_eval:
+            random.seed(config.training_seed)
+            # random.shuffle(self.samples)
+            random.shuffle(self.samples)
+            # order by language
+            self.samples = key_samples_by_col(self.samples, "language")
+            print(" > Sampling by language:", self.samples.keys())
+        else:
+            # for evaluation load and check samples that are corrupted to ensures the reproducibility
+            self.check_eval_samples()
+
+    def check_eval_samples(self):
+        print("Filtering invalid eval samples!!")
+        new_samples = []
+        for sample in self.samples:
+                try:
+                    tseq, _, wav, _, _, _ = self.load_item(sample)
+                except:
+                    pass
+                # Basically, this audio file is nonexistent or too long to be supported by the dataset.
+                if wav is None or \
+                    (self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \
+                    (self.max_text_len is not None and tseq.shape[0] > self.max_text_len):
+                    continue
+                new_samples.append(sample)
+        self.samples = new_samples
+        print("Total eval samples after filtering:", len(self.samples))
 
     def get_text(self, text, lang):
         tokens = self.tokenizer.encode(text, lang)
@@ -118,13 +131,17 @@ class XTTSDataset(torch.utils.data.Dataset):
         return tseq, audiopath, wav, cond, cond_len, cond_idxs
 
     def __getitem__(self, index):
-        # select a random language
-        lang = random.choice(list(self.samples.keys()))
-        # select random sample
-        index = random.randint(0, len(self.samples[lang]) - 1)
-        sample = self.samples[lang][index]
-        # a unique id for each sampel to deal with fails
-        sample_id = lang+"_"+str(index)
+        if self.is_eval:
+            sample = self.samples[index]
+            sample_id = str(index)
+        else:
+            # select a random language
+            lang = random.choice(list(self.samples.keys()))
+            # select random sample
+            index = random.randint(0, len(self.samples[lang]) - 1)
+            sample = self.samples[lang][index]
+            # a unique id for each sampel to deal with fails
+            sample_id = lang+"_"+str(index)
 
         # ignore samples that we already know that is not valid ones
         if sample_id in self.failed_samples:
@@ -167,11 +184,14 @@ class XTTSDataset(torch.utils.data.Dataset):
         return res
 
     def __len__(self):
+        if self.is_eval:
+            return len(self.samples)
         return sum([len(v) for v in self.samples.values()])
 
     def collate_fn(self, batch):
         # convert list of dicts to dict of lists
         B = len(batch)
+
         batch = {k: [dic[k] for dic in batch] for k in batch[0]}
 
         # stack for features that already have the same shape
@@ -198,5 +218,4 @@ class XTTSDataset(torch.utils.data.Dataset):
 
         batch["wav"] = wav_padded
         batch["padded_text"] = text_padded
-
         return batch

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

@@ -327,7 +327,7 @@ class GPTTrainer(BaseTTS):
         else:
             # Todo: remove the randomness of dataset when it is eval
             # init dataloader
-            dataset = XTTSDataset(self.config, samples, self.tokenizer, config.audio.sample_rate)
+            dataset = XTTSDataset(self.config, samples, self.tokenizer, config.audio.sample_rate, is_eval)
 
             # wait all the DDP process to be ready
             if num_gpus > 1:

recipes/multilingual/xtts_v1/train_xtts.py

@@ -355,7 +355,7 @@ if __name__ == "__main__":
     DASHBOARD_LOGGER = "tensorboard"
     LOGGER_URI = None    
     RESTORE_PATH = None
-    BATCH_SIZE = 2
+    BATCH_SIZE = 10
     GRAD_ACUMM_STEPS = 1
     NUM_LOADERS = 1