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| import ast | |
| import pprint | |
| class HParams(object): | |
| def __init__(self, **kwargs): self.__dict__.update(kwargs) | |
| def __setitem__(self, key, value): setattr(self, key, value) | |
| def __getitem__(self, key): return getattr(self, key) | |
| def __repr__(self): return pprint.pformat(self.__dict__) | |
| def parse(self, string): | |
| # Overrides hparams from a comma-separated string of name=value pairs | |
| if len(string) > 0: | |
| overrides = [s.split("=") for s in string.split(",")] | |
| keys, values = zip(*overrides) | |
| keys = list(map(str.strip, keys)) | |
| values = list(map(str.strip, values)) | |
| for k in keys: | |
| self.__dict__[k] = ast.literal_eval(values[keys.index(k)]) | |
| return self | |
| hparams = HParams( | |
| ### Signal Processing (used in both synthesizer and vocoder) | |
| sample_rate = 16000, | |
| n_fft = 800, | |
| num_mels = 80, | |
| hop_size = 200, # Tacotron uses 12.5 ms frame shift (set to sample_rate * 0.0125) | |
| win_size = 800, # Tacotron uses 50 ms frame length (set to sample_rate * 0.050) | |
| fmin = 55, | |
| min_level_db = -100, | |
| ref_level_db = 20, | |
| max_abs_value = 4., # Gradient explodes if too big, premature convergence if too small. | |
| preemphasis = 0.97, # Filter coefficient to use if preemphasize is True | |
| preemphasize = True, | |
| ### Tacotron Text-to-Speech (TTS) | |
| tts_embed_dims = 512, # Embedding dimension for the graphemes/phoneme inputs | |
| tts_encoder_dims = 256, | |
| tts_decoder_dims = 128, | |
| tts_postnet_dims = 512, | |
| tts_encoder_K = 5, | |
| tts_lstm_dims = 1024, | |
| tts_postnet_K = 5, | |
| tts_num_highways = 4, | |
| tts_dropout = 0.5, | |
| tts_cleaner_names = ["english_cleaners"], | |
| tts_stop_threshold = -3.4, # Value below which audio generation ends. | |
| # For example, for a range of [-4, 4], this | |
| # will terminate the sequence at the first | |
| # frame that has all values < -3.4 | |
| ### Tacotron Training | |
| tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule | |
| (2, 5e-4, 40_000, 12), # (r, lr, step, batch_size) | |
| (2, 2e-4, 80_000, 12), # | |
| (2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames | |
| (2, 3e-5, 320_000, 12), # synthesized for each decoder iteration) | |
| (2, 1e-5, 640_000, 12)], # lr = learning rate | |
| tts_clip_grad_norm = 1.0, # clips the gradient norm to prevent explosion - set to None if not needed | |
| tts_eval_interval = 500, # Number of steps between model evaluation (sample generation) | |
| # Set to -1 to generate after completing epoch, or 0 to disable | |
| tts_eval_num_samples = 1, # Makes this number of samples | |
| ### Data Preprocessing | |
| max_mel_frames = 900, | |
| rescale = True, | |
| rescaling_max = 0.9, | |
| synthesis_batch_size = 16, # For vocoder preprocessing and inference. | |
| ### Mel Visualization and Griffin-Lim | |
| signal_normalization = True, | |
| power = 1.5, | |
| griffin_lim_iters = 60, | |
| ### Audio processing options | |
| fmax = 7600, # Should not exceed (sample_rate // 2) | |
| allow_clipping_in_normalization = True, # Used when signal_normalization = True | |
| clip_mels_length = True, # If true, discards samples exceeding max_mel_frames | |
| use_lws = False, # "Fast spectrogram phase recovery using local weighted sums" | |
| symmetric_mels = True, # Sets mel range to [-max_abs_value, max_abs_value] if True, | |
| # and [0, max_abs_value] if False | |
| trim_silence = True, # Use with sample_rate of 16000 for best results | |
| ### SV2TTS | |
| speaker_embedding_size = 256, # Dimension for the speaker embedding | |
| silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split | |
| utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded | |
| ) | |
| def hparams_debug_string(): | |
| return str(hparams) | |