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from dataclasses import dataclass
@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()
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