Fix WaveRNN config and test

TTS/vocoder/configs/shared_configs.py

@@ -17,11 +17,11 @@ class BaseVocoderConfig(BaseTrainingConfig):
             Number of instances used for evaluation. Defaults to 10.
         data_path (str):
             Root path of the training data. All the audio files found recursively from this root path are used for
-            training. Defaults to MISSING.
+            training. Defaults to `""`.
         feature_path (str):
             Root path to the precomputed feature files. Defaults to None.
         seq_len (int):
-            Length of the waveform segments used for training. Defaults to MISSING.
+            Length of the waveform segments used for training. Defaults to 1000.
         pad_short (int):
             Extra padding for the waveforms shorter than `seq_len`. Defaults to 0.
         conv_path (int):
@@ -45,9 +45,9 @@ class BaseVocoderConfig(BaseTrainingConfig):
     use_noise_augment: bool = False  # enable/disable random noise augmentation in spectrograms.
     eval_split_size: int = 10  # number of samples used for evaluation.
     # dataset
-    data_path: str = MISSING  # root data path. It finds all wav files recursively from there.
+    data_path: str = ""  # root data path. It finds all wav files recursively from there.
     feature_path: str = None  # if you use precomputed features
-    seq_len: int = MISSING  # signal length used in training.
+    seq_len: int = 1000  # signal length used in training.
     pad_short: int = 0  # additional padding for short wavs
     conv_pad: int = 0  # additional padding against convolutions applied to spectrograms
     use_cache: bool = False  # use in memory cache to keep the computed features. This might cause OOM.

tests/vocoder_tests/test_vocoder_wavernn.py

@@ -12,7 +12,7 @@ def test_wavernn():
     config.model_args = WavernnArgs(
         rnn_dims=512,
         fc_dims=512,
-        mode=10,
+        mode="mold",
         mulaw=False,
         pad=2,
         use_aux_net=True,
@@ -37,13 +37,13 @@ def test_wavernn():
     assert np.all(output.shape == (2, 1280, 30)), output.shape
 
     # mode: gauss
-    config.model_params.mode = "gauss"
+    config.model_args.mode = "gauss"
     model = Wavernn(config)
     output = model(dummy_x, dummy_m)
     assert np.all(output.shape == (2, 1280, 2)), output.shape
 
     # mode: quantized
-    config.model_params.mode = 4
+    config.model_args.mode = 4
     model = Wavernn(config)
     output = model(dummy_x, dummy_m)
     assert np.all(output.shape == (2, 1280, 2 ** 4)), output.shape