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log_dir: "./runs"
save_freq: 1
log_interval: 10
save_interval: 1000
device: "cuda"
epochs: 1000 # number of epochs for first stage training (pre-training)
batch_size: 2
batch_length: 100 # maximum duration of audio in a batch (in seconds)
max_len: 80 # maximum number of frames
pretrained_model: ""
pretrained_encoder: "./temp_ckpt.pth"
load_only_params: False # set to true if do not want to load epoch numbers and optimizer parameters
preprocess_params:
sr: 22050
spect_params:
n_fft: 1024
win_length: 1024
hop_length: 256
n_mels: 80
fmin: 0
fmax: "None"
model_params:
dit_type: "DiT" # uDiT or DiT
reg_loss_type: "l1" # l1 or l2
speech_tokenizer:
type: 'whisper'
whisper_name: "openai/whisper-small"
path: "speech_tokenizer_v1.onnx"
cosyvoice:
path: "../CosyVoice/pretrained_models/CosyVoice-300M"
style_encoder:
dim: 192
campplus_path: "campplus_cn_common.bin"
DAC:
encoder_dim: 64
encoder_rates: [2, 5, 5, 6]
decoder_dim: 1536
decoder_rates: [ 6, 5, 5, 2 ]
sr: 24000
length_regulator:
channels: 512
is_discrete: false
in_channels: 768
content_codebook_size: 2048
sampling_ratios: [1, 1, 1, 1]
vector_quantize: false
n_codebooks: 1
quantizer_dropout: 0.0
f0_condition: false
n_f0_bins: 512
DiT:
hidden_dim: 512
num_heads: 8
depth: 13
class_dropout_prob: 0.1
block_size: 8192
in_channels: 80
style_condition: true
final_layer_type: 'wavenet'
target: 'mel' # mel or codec
content_dim: 512
content_codebook_size: 1024
content_type: 'discrete'
f0_condition: false
n_f0_bins: 512
content_codebooks: 1
is_causal: false
long_skip_connection: true
zero_prompt_speech_token: false # for prompt component, do not input corresponding speech token
time_as_token: false
style_as_token: false
uvit_skip_connection: true
add_resblock_in_transformer: false
wavenet:
hidden_dim: 512
num_layers: 8
kernel_size: 5
dilation_rate: 1
p_dropout: 0.2
style_condition: true
loss_params:
base_lr: 0.0001
lambda_mel: 45
lambda_kl: 1.0 |