Update WaveGrad

TTS/vocoder/models/wavegrad.py

@@ -58,7 +58,7 @@ class Wavegrad(BaseVocoder):
 
     # pylint: disable=dangerous-default-value
     def __init__(self, config: Coqpit):
-        super().__init__()
+        super().__init__(config)
         self.config = config
         self.use_weight_norm = config.model_params.use_weight_norm
         self.hop_len = np.prod(config.model_params.upsample_factors)
@@ -258,21 +258,22 @@ class Wavegrad(BaseVocoder):
         return {"model_output": noise_hat}, {"loss": loss}
 
     def train_log(  # pylint: disable=no-self-use
-        self, ap: AudioProcessor, batch: Dict, outputs: Dict  # pylint: disable=unused-argument
+        self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int  # pylint: disable=unused-argument
     ) -> Tuple[Dict, np.ndarray]:
-        return None, None
+        pass
 
     @torch.no_grad()
     def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]:
         return self.train_step(batch, criterion)
 
     def eval_log(  # pylint: disable=no-self-use
-        self, ap: AudioProcessor, batch: Dict, outputs: Dict  # pylint: disable=unused-argument
-    ) -> Tuple[Dict, np.ndarray]:
-        return None, None
+        self, batch: Dict, outputs: Dict, logger: "Logger", assets: Dict, steps: int  # pylint: disable=unused-argument
+    ) -> None:
+        pass
 
-    def test_run(self, ap: AudioProcessor, samples: List[Dict], ouputs: Dict):  # pylint: disable=unused-argument
+    def test_run(self, assets: Dict, samples: List[Dict], outputs: Dict):  # pylint: disable=unused-argument
         # setup noise schedule and inference
+        ap = assets["audio_processor"]
         noise_schedule = self.config["test_noise_schedule"]
         betas = np.linspace(noise_schedule["min_val"], noise_schedule["max_val"], noise_schedule["num_steps"])
         self.compute_noise_level(betas)
@@ -307,8 +308,9 @@ class Wavegrad(BaseVocoder):
         return {"input": m, "waveform": y}
 
     def get_data_loader(
-        self, config: Coqpit, ap: AudioProcessor, is_eval: True, data_items: List, verbose: bool, num_gpus: int
+        self, config: Coqpit, assets: Dict, is_eval: True, data_items: List, verbose: bool, num_gpus: int
     ):
+        ap = assets["audio_processor"]
         dataset = WaveGradDataset(
             ap=ap,
             items=data_items,

recipes/ljspeech/wavegrad/train_wavegrad.py

@@ -1,7 +1,11 @@
 import os
 
-from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.utils.audio import AudioProcessor
 from TTS.vocoder.configs import WavegradConfig
+from TTS.vocoder.models.wavegrad import Wavegrad
+from TTS.vocoder.datasets.preprocess import load_wav_data
+
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 config = WavegradConfig(
@@ -22,6 +26,24 @@ config = WavegradConfig(
     data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
     output_path=output_path,
 )
-args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
+
+# init model
+model = Wavegrad(config)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainingArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
 trainer.fit()