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import sys, os | |
sys.path.append(os.getcwd()) | |
from model import M2_TTS, UNetT, DiT, MMDiT | |
import torch | |
import thop | |
''' ~155M ''' | |
# transformer = UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4) | |
# transformer = UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4, text_dim = 512, conv_layers = 4) | |
# transformer = DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2) | |
# transformer = DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4) | |
# transformer = DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4, long_skip_connection = True) | |
# transformer = MMDiT(dim = 512, depth = 16, heads = 16, ff_mult = 2) | |
''' ~335M ''' | |
# FLOPs: 622.1 G, Params: 333.2 M | |
# transformer = UNetT(dim = 1024, depth = 24, heads = 16, ff_mult = 4) | |
# FLOPs: 363.4 G, Params: 335.8 M | |
transformer = DiT(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4) | |
model = M2_TTS(transformer=transformer) | |
target_sample_rate = 24000 | |
n_mel_channels = 100 | |
hop_length = 256 | |
duration = 20 | |
frame_length = int(duration * target_sample_rate / hop_length) | |
text_length = 150 | |
flops, params = thop.profile(model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long))) | |
print(f"FLOPs: {flops / 1e9} G") | |
print(f"Params: {params / 1e6} M") | |