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from dataclasses import dataclass | |
class HParams: | |
## Mel-filterbank | |
mel_window_length = 25 # In milliseconds | |
mel_window_step = 10 # In milliseconds | |
mel_n_channels = 40 | |
## Audio | |
sampling_rate = 16000 | |
# Number of spectrogram frames in a partial utterance | |
partials_n_frames = 160 # 1600 ms | |
# Number of spectrogram frames at inference | |
inference_n_frames = 80 # 800 ms | |
## Voice Activation Detection | |
# Window size of the VAD. Must be either 10, 20 or 30 milliseconds. | |
# This sets the granularity of the VAD. Should not need to be changed. | |
vad_window_length = 30 # In milliseconds | |
# Number of frames to average together when performing the moving average smoothing. | |
# The larger this value, the larger the VAD variations must be to not get smoothed out. | |
vad_moving_average_width = 8 | |
# Maximum number of consecutive silent frames a segment can have. | |
vad_max_silence_length = 6 | |
## Audio volume normalization | |
audio_norm_target_dBFS = -30 | |
## Model parameters | |
model_hidden_size = 256 | |
model_embedding_size = 256 | |
model_num_layers = 3 | |
## Training parameters | |
learning_rate_init = 1e-4 | |
speakers_per_batch = 64 | |
utterances_per_speaker = 10 | |
hparams = HParams() | |