Fix tune wavegrad (#1844)

* fix imports in tune_wavegrad

* load_config returns Coqpit object instead None

* set action (store true) for flag "--use_cuda"; start to tune if module is running as the main program

* fix var order in the result of batch collating

* make style

* make style with black and isort

TTS/bin/tune_wavegrad.py

@@ -1,4 +1,4 @@
-"""Search a good noise schedule for WaveGrad for a given number of inferece iterations"""
+"""Search a good noise schedule for WaveGrad for a given number of inference iterations"""
 import argparse
 from itertools import product as cartesian_product
 
@@ -7,94 +7,97 @@
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
+from TTS.config import load_config
 from TTS.utils.audio import AudioProcessor
-from TTS.utils.io import load_config
 from TTS.vocoder.datasets.preprocess import load_wav_data
 from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
-from TTS.vocoder.utils.generic_utils import setup_generator
+from TTS.vocoder.models import setup_model
 
-parser = argparse.ArgumentParser()
-parser.add_argument("--model_path", type=str, help="Path to model checkpoint.")
-parser.add_argument("--config_path", type=str, help="Path to model config file.")
-parser.add_argument("--data_path", type=str, help="Path to data directory.")
-parser.add_argument("--output_path", type=str, help="path for output file including file name and extension.")
-parser.add_argument(
-    "--num_iter", type=int, help="Number of model inference iterations that you like to optimize noise schedule for."
-)
-parser.add_argument("--use_cuda", type=bool, help="enable/disable CUDA.")
-parser.add_argument("--num_samples", type=int, default=1, help="Number of datasamples used for inference.")
-parser.add_argument(
-    "--search_depth",
-    type=int,
-    default=3,
-    help="Search granularity. Increasing this increases the run-time exponentially.",
-)
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", type=str, help="Path to model checkpoint.")
+    parser.add_argument("--config_path", type=str, help="Path to model config file.")
+    parser.add_argument("--data_path", type=str, help="Path to data directory.")
+    parser.add_argument("--output_path", type=str, help="path for output file including file name and extension.")
+    parser.add_argument(
+        "--num_iter",
+        type=int,
+        help="Number of model inference iterations that you like to optimize noise schedule for.",
+    )
+    parser.add_argument("--use_cuda", action="store_true", help="enable CUDA.")
+    parser.add_argument("--num_samples", type=int, default=1, help="Number of datasamples used for inference.")
+    parser.add_argument(
+        "--search_depth",
+        type=int,
+        default=3,
+        help="Search granularity. Increasing this increases the run-time exponentially.",
+    )
 
-# load config
-args = parser.parse_args()
-config = load_config(args.config_path)
+    # load config
+    args = parser.parse_args()
+    config = load_config(args.config_path)
 
-# setup audio processor
-ap = AudioProcessor(**config.audio)
+    # setup audio processor
+    ap = AudioProcessor(**config.audio)
 
-# load dataset
-_, train_data = load_wav_data(args.data_path, 0)
-train_data = train_data[: args.num_samples]
-dataset = WaveGradDataset(
-    ap=ap,
-    items=train_data,
-    seq_len=-1,
-    hop_len=ap.hop_length,
-    pad_short=config.pad_short,
-    conv_pad=config.conv_pad,
-    is_training=True,
-    return_segments=False,
-    use_noise_augment=False,
-    use_cache=False,
-    verbose=True,
-)
-loader = DataLoader(
-    dataset,
-    batch_size=1,
-    shuffle=False,
-    collate_fn=dataset.collate_full_clips,
-    drop_last=False,
-    num_workers=config.num_loader_workers,
-    pin_memory=False,
-)
+    # load dataset
+    _, train_data = load_wav_data(args.data_path, 0)
+    train_data = train_data[: args.num_samples]
+    dataset = WaveGradDataset(
+        ap=ap,
+        items=train_data,
+        seq_len=-1,
+        hop_len=ap.hop_length,
+        pad_short=config.pad_short,
+        conv_pad=config.conv_pad,
+        is_training=True,
+        return_segments=False,
+        use_noise_augment=False,
+        use_cache=False,
+        verbose=True,
+    )
+    loader = DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        collate_fn=dataset.collate_full_clips,
+        drop_last=False,
+        num_workers=config.num_loader_workers,
+        pin_memory=False,
+    )
 
-# setup the model
-model = setup_generator(config)
-if args.use_cuda:
-    model.cuda()
+    # setup the model
+    model = setup_model(config)
+    if args.use_cuda:
+        model.cuda()
 
-# setup optimization parameters
-base_values = sorted(10 * np.random.uniform(size=args.search_depth))
-print(base_values)
-exponents = 10 ** np.linspace(-6, -1, num=args.num_iter)
-best_error = float("inf")
-best_schedule = None
-total_search_iter = len(base_values) ** args.num_iter
-for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=total_search_iter):
-    beta = exponents * base
-    model.compute_noise_level(beta)
-    for data in loader:
-        mel, audio = data
-        y_hat = model.inference(mel.cuda() if args.use_cuda else mel)
+    # setup optimization parameters
+    base_values = sorted(10 * np.random.uniform(size=args.search_depth))
+    print(f" > base values: {base_values}")
+    exponents = 10 ** np.linspace(-6, -1, num=args.num_iter)
+    best_error = float("inf")
+    best_schedule = None  # pylint: disable=C0103
+    total_search_iter = len(base_values) ** args.num_iter
+    for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=total_search_iter):
+        beta = exponents * base
+        model.compute_noise_level(beta)
+        for data in loader:
+            mel, audio = data
+            y_hat = model.inference(mel.cuda() if args.use_cuda else mel)
 
-        if args.use_cuda:
-            y_hat = y_hat.cpu()
-        y_hat = y_hat.numpy()
+            if args.use_cuda:
+                y_hat = y_hat.cpu()
+            y_hat = y_hat.numpy()
 
-        mel_hat = []
-        for i in range(y_hat.shape[0]):
-            m = ap.melspectrogram(y_hat[i, 0])[:, :-1]
-            mel_hat.append(torch.from_numpy(m))
+            mel_hat = []
+            for i in range(y_hat.shape[0]):
+                m = ap.melspectrogram(y_hat[i, 0])[:, :-1]
+                mel_hat.append(torch.from_numpy(m))
 
-        mel_hat = torch.stack(mel_hat)
-        mse = torch.sum((mel - mel_hat) ** 2).mean()
-        if mse.item() < best_error:
-            best_error = mse.item()
-            best_schedule = {"beta": beta}
-            print(f" > Found a better schedule. - MSE: {mse.item()}")
-            np.save(args.output_path, best_schedule)
+            mel_hat = torch.stack(mel_hat)
+            mse = torch.sum((mel - mel_hat) ** 2).mean()
+            if mse.item() < best_error:
+                best_error = mse.item()
+                best_schedule = {"beta": beta}
+                print(f" > Found a better schedule. - MSE: {mse.item()}")
+                np.save(args.output_path, best_schedule)

TTS/config/__init__.py

@@ -62,7 +62,7 @@ def _process_model_name(config_dict: Dict) -> str:
     return model_name
 
 
-def load_config(config_path: str) -> None:
+def load_config(config_path: str) -> Coqpit:
     """Import `json` or `yaml` files as TTS configs. First, load the input file as a `dict` and check the model name
     to find the corresponding Config class. Then initialize the Config.
 

TTS/vocoder/datasets/wavegrad_dataset.py

@@ -149,4 +149,4 @@ def collate_full_clips(batch):
             mels[idx, :, : mel.shape[1]] = mel
             audios[idx, : audio.shape[0]] = audio
 
-        return audios, mels
+        return mels, audios