Merge pull request #667 from coqui-ai/fix-test-sentences

Fix test runs and wavegrad test_run

TTS/trainer.py

@@ -764,11 +764,11 @@ class Trainer:
         """Run test and log the results. Test run must be defined by the model.
         Model must return figures and audios to be logged by the Tensorboard."""
         if hasattr(self.model, "test_run"):
-            if hasattr(self.eval_loader.load_test_samples):
+            if hasattr(self.eval_loader.dataset, "load_test_samples"):
-                samples = self.eval_loader.load_test_samples(1)
+                samples = self.eval_loader.dataset.load_test_samples(1)
-                figures, audios = self.model.test_run(samples)
+                figures, audios = self.model.test_run(self.ap, samples, None)
             else:
-                figures, audios = self.model.test_run()
+                figures, audios = self.model.test_run(self.ap)
             self.tb_logger.tb_test_audios(self.total_steps_done, audios, self.config.audio["sample_rate"])
             self.tb_logger.tb_test_figures(self.total_steps_done, figures)
 
@@ -790,7 +790,7 @@ class Trainer:
             self.train_epoch()
             if self.config.run_eval:
                 self.eval_epoch()
-            if epoch >= self.config.test_delay_epochs and self.args.rank < 0:
+            if epoch >= self.config.test_delay_epochs and self.args.rank <= 0:
                 self.test_run()
             self.c_logger.print_epoch_end(
                 epoch, self.keep_avg_eval.avg_values if self.config.run_eval else self.keep_avg_train.avg_values

TTS/tts/models/base_tts.py

@@ -70,7 +70,7 @@ class BaseTTS(BaseModel):
 
     def get_aux_input(self, **kwargs) -> Dict:
         """Prepare and return `aux_input` used by `forward()`"""
-        pass
+        return {"speaker_id": None, "style_wav": None, "d_vector": None}
 
     def format_batch(self, batch: Dict) -> Dict:
         """Generic batch formatting for `TTSDataset`.
@@ -200,7 +200,7 @@ class BaseTTS(BaseModel):
             )
         return loader
 
-    def test_run(self) -> Tuple[Dict, Dict]:
+    def test_run(self, ap) -> Tuple[Dict, Dict]:
         """Generic test run for `tts` models used by `Trainer`.
 
         You can override this for a different behaviour.
@@ -212,14 +212,14 @@ class BaseTTS(BaseModel):
         test_audios = {}
         test_figures = {}
         test_sentences = self.config.test_sentences
-        aux_inputs = self._get_aux_inputs()
+        aux_inputs = self.get_aux_input()
         for idx, sen in enumerate(test_sentences):
             wav, alignment, model_outputs, _ = synthesis(
-                self.model,
+                self,
                 sen,
                 self.config,
-                self.use_cuda,
+                "cuda" in str(next(self.parameters()).device),
-                self.ap,
+                ap,
                 speaker_id=aux_inputs["speaker_id"],
                 d_vector=aux_inputs["d_vector"],
                 style_wav=aux_inputs["style_wav"],
@@ -229,6 +229,6 @@ class BaseTTS(BaseModel):
             ).values()
 
             test_audios["{}-audio".format(idx)] = wav
-            test_figures["{}-prediction".format(idx)] = plot_spectrogram(model_outputs, self.ap, output_fig=False)
+            test_figures["{}-prediction".format(idx)] = plot_spectrogram(model_outputs, ap, output_fig=False)
             test_figures["{}-alignment".format(idx)] = plot_alignment(alignment, output_fig=False)
         return test_figures, test_audios

TTS/tts/models/glow_tts.py

@@ -113,7 +113,7 @@ class GlowTTS(BaseTTS):
 
     @staticmethod
     def compute_outputs(attn, o_mean, o_log_scale, x_mask):
-        """ Compute and format the mode outputs with the given alignment map"""
+        """Compute and format the mode outputs with the given alignment map"""
         y_mean = torch.matmul(attn.squeeze(1).transpose(1, 2), o_mean.transpose(1, 2)).transpose(
             1, 2
         )  # [b, t', t], [b, t, d] -> [b, d, t']

TTS/vocoder/datasets/wavegrad_dataset.py

@@ -2,6 +2,7 @@ import glob
 import os
 import random
 from multiprocessing import Manager
+from typing import List, Tuple
 
 import numpy as np
 import torch
@@ -67,7 +68,19 @@ class WaveGradDataset(Dataset):
         item = self.load_item(idx)
         return item
 
-    def load_test_samples(self, num_samples):
+    def load_test_samples(self, num_samples: int) -> List[Tuple]:
+        """Return test samples.
+
+        Args:
+            num_samples (int): Number of samples to return.
+
+        Returns:
+            List[Tuple]: melspectorgram and audio.
+
+        Shapes:
+            - melspectrogram (Tensor): :math:`[C, T]`
+            - audio (Tensor): :math:`[T_audio]`
+        """
         samples = []
         return_segments = self.return_segments
         self.return_segments = False

TTS/vocoder/models/__init__.py

@@ -31,7 +31,7 @@ def setup_model(config: Coqpit):
 
 
 def setup_generator(c):
-    """ TODO: use config object as arguments"""
+    """TODO: use config object as arguments"""
     print(" > Generator Model: {}".format(c.generator_model))
     MyModel = importlib.import_module("TTS.vocoder.models." + c.generator_model.lower())
     MyModel = getattr(MyModel, to_camel(c.generator_model))
@@ -94,7 +94,7 @@ def setup_generator(c):
 
 
 def setup_discriminator(c):
-    """ TODO: use config objekt as arguments"""
+    """TODO: use config objekt as arguments"""
     print(" > Discriminator Model: {}".format(c.discriminator_model))
     if "parallel_wavegan" in c.discriminator_model:
         MyModel = importlib.import_module("TTS.vocoder.models.parallel_wavegan_discriminator")

TTS/vocoder/models/wavegrad.py

@@ -124,11 +124,16 @@ class Wavegrad(BaseModel):
 
     @torch.no_grad()
     def inference(self, x, y_n=None):
-        """x: B x D X T"""
+        """
+        Shapes:
+            x: :math:`[B, C , T]`
+            y_n: :math:`[B, 1, T]`
+        """
         if y_n is None:
-            y_n = torch.randn(x.shape[0], 1, self.hop_len * x.shape[-1], dtype=torch.float32).to(x)
+            y_n = torch.randn(x.shape[0], 1, self.hop_len * x.shape[-1])
         else:
-            y_n = torch.FloatTensor(y_n).unsqueeze(0).unsqueeze(0).to(x)
+            y_n = torch.FloatTensor(y_n).unsqueeze(0).unsqueeze(0)
+        y_n = y_n.type_as(x)
         sqrt_alpha_hat = self.noise_level.to(x)
         for n in range(len(self.alpha) - 1, -1, -1):
             y_n = self.c1[n] * (y_n - self.c2[n] * self.forward(y_n, x, sqrt_alpha_hat[n].repeat(x.shape[0])))
@@ -267,8 +272,10 @@ class Wavegrad(BaseModel):
         betas = np.linspace(noise_schedule["min_val"], noise_schedule["max_val"], noise_schedule["num_steps"])
         self.compute_noise_level(betas)
         for sample in samples:
-            x = sample["input"]
+            x = sample[0]
-            y = sample["waveform"]
+            x = x[None, :, :].to(next(self.parameters()).device)
+            y = sample[1]
+            y = y[None, :]
             # compute voice
             y_pred = self.inference(x)
             # compute spectrograms

TTS/vocoder/models/wavernn.py

@@ -322,7 +322,7 @@ class Wavernn(BaseVocoder):
 
         with torch.no_grad():
             if isinstance(mels, np.ndarray):
-                mels = torch.FloatTensor(mels).type_as(mels)
+                mels = torch.FloatTensor(mels).to(str(next(self.parameters()).device))
 
             if mels.ndim == 2:
                 mels = mels.unsqueeze(0)
@@ -576,7 +576,8 @@ class Wavernn(BaseVocoder):
         figures = {}
         audios = {}
         for idx, sample in enumerate(samples):
-            x = sample["input"]
+            x = torch.FloatTensor(sample[0])
+            x = x.to(next(self.parameters()).device)
             y_hat = self.inference(x, self.config.batched, self.config.target_samples, self.config.overlap_samples)
             x_hat = ap.melspectrogram(y_hat)
             figures.update(
@@ -585,7 +586,7 @@ class Wavernn(BaseVocoder):
                     f"test_{idx}/prediction": plot_spectrogram(x_hat.T),
                 }
             )
-            audios.update({f"test_{idx}/audio", y_hat})
+            audios.update({f"test_{idx}/audio": y_hat})
         return figures, audios
 
     @staticmethod

tests/test_speaker_encoder_train.py

@@ -6,6 +6,7 @@ from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.config.shared_configs import BaseAudioConfig
 from TTS.speaker_encoder.speaker_encoder_config import SpeakerEncoderConfig
 
+
 def run_test_train():
     command = (
         f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
@@ -17,6 +18,7 @@ def run_test_train():
     )
     run_cli(command)
 
+
 config_path = os.path.join(get_tests_output_path(), "test_speaker_encoder_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")