PortaSpeech / egs /egs_bases /tts /dataset_params.yaml
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audio_num_mel_bins: 80
audio_sample_rate: 22050
hop_size: 256 # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
win_size: 1024 # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
fft_size: 1024 # Extra window size is filled with 0 paddings to match this parameter
fmin: 80 # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
fmax: 7600 # To be increased/reduced depending on data.
f0_min: 80
f0_max: 800
griffin_lim_iters: 30
pitch_extractor: parselmouth
num_spk: 1
mel_vmin: -6
mel_vmax: 1.5
loud_norm: false
raw_data_dir: ''
processed_data_dir: ''
binary_data_dir: ''
preprocess_cls: ''
binarizer_cls: data_gen.tts.base_binarizer.BaseBinarizer
preprocess_args:
nsample_per_mfa_group: 1000
# text process
txt_processor: en
use_mfa: true
with_phsep: true
reset_phone_dict: true
reset_word_dict: true
add_eos_bos: true
# mfa
mfa_group_shuffle: false
mfa_offset: 0.02
# wav processors
wav_processors: [ ]
save_sil_mask: true
vad_max_silence_length: 12
binarization_args:
shuffle: false
with_wav: false
with_align: true
with_spk_embed: false
with_f0: true
with_f0cwt: false
with_linear: false
trim_eos_bos: false
min_sil_duration: 0.1
train_range: [ 200, -1 ]
test_range: [ 0, 100 ]
valid_range: [ 100, 200 ]
word_dict_size: 10000
pitch_key: pitch