diff --git a/TTS/vocoder/configs/shared_configs.py b/TTS/vocoder/configs/shared_configs.py index 6891ce6c..a2b7b866 100644 --- a/TTS/vocoder/configs/shared_configs.py +++ b/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. diff --git a/tests/vocoder_tests/test_vocoder_wavernn.py b/tests/vocoder_tests/test_vocoder_wavernn.py index b5c769ee..d4a7b8dd 100644 --- a/tests/vocoder_tests/test_vocoder_wavernn.py +++ b/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