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import torch
import hydra
from hydra import compose, initialize
from hydra.utils import instantiate
from lightning import LightningModule
from loguru import logger
from omegaconf import OmegaConf
import sys
sys.path.insert(0,'/workspace/user_code/kuachen/projects/v2s')
from fish_speech.models.v2s_tts.pretrain_model import V2S_TTS_Pretrain_Model
from fish_speech.models.v2s_tts.flow_matching_dit import ConditionalCFM
from fish_speech.models.v2s_tts.model.backbones.dit import DiT_Style
from fish_speech.models.v2s_tts.transformer.encoder import ConformerEncoder
from fish_speech.models.v2s_tts.style_bank import StyleBankExtractor
from omegaconf import DictConfig
class CFMParams:
def __init__(self):
self.sigma_min = 1e-06
self.solver = "euler"
self.t_scheduler = "cosine"
self.training_cfg_rate = 0.2
self.inference_cfg_rate = 0.7
self.reg_loss_type = "l1"
def load_model(config_name, checkpoint_path, device="cpu"):
hydra.core.global_hydra.GlobalHydra.instance().clear()
with initialize(version_base="1.3", config_path="../fish_speech/configs"):
cfg = compose(config_name=config_name)
model: LightningModule = instantiate(cfg.model)
state_dict = torch.load(
checkpoint_path,
map_location=model.device,
)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict, strict=False)
model.eval()
model.to(device)
logger.info("Restored model from checkpoint")
return model
def get_pretrain_model(checkpoint_path):
# εε§ε encoder
encoder = ConformerEncoder(
output_size=512,
attention_heads=8,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.1,
normalize_before=True,
input_layer='linear',
pos_enc_layer_type='rel_pos_espnet',
selfattention_layer_type='rel_selfattn',
input_size=512,
use_cnn_module=False,
macaron_style=False
)
# εε§ε style_qformer
style_qformer = StyleBankExtractor(
dim_in=1024,
n_layers=4,
n_emb=32,
d_model=64,
nhead=4
)
# εε§ε estimator
estimator = DiT_Style(
dim=1024,
depth=22,
heads=16,
ff_mult=2,
conv_layers=4,
mel_dim=80,
style_dim=64
)
cfm_params = CFMParams()
# εε§ε decoder
decoder = ConditionalCFM(
in_channels=160,
n_spks=0,
spk_emb_dim=80,
cfm_params=cfm_params,
estimator=estimator
)
# εε§ε樑ε
model = V2S_TTS_Pretrain_Model(
input_size=512,
output_size=80,
output_type='mel',
vocab_size=500,
spk_dim=192,
sll_checkpoint='checkpoints/wavlm_large.pt',
output_layer=6,
decoder=decoder,
encoder=encoder,
style_qformer=style_qformer
)
state_dict = torch.load(checkpoint_path)['state_dict']
new_params = {}
for k,v in state_dict.items():
if k.startswith('generator'):
new_k = k[10:]
new_params[new_k] = v
# Load the new parameters into the model
model.load_state_dict(new_params, strict=True)
model.eval()
return model
def get_pretrain_model_32_dim32(checkpoint_path):
# εε§ε encoder
encoder = ConformerEncoder(
output_size=512,
attention_heads=8,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.1,
normalize_before=True,
input_layer='linear',
pos_enc_layer_type='rel_pos_espnet',
selfattention_layer_type='rel_selfattn',
input_size=512,
use_cnn_module=False,
macaron_style=False
)
# εε§ε style_qformer
style_qformer = StyleBankExtractor(
dim_in=1024,
n_layers=4,
n_emb=32,
d_model=32,
nhead=4
)
# εε§ε estimator
estimator = DiT_Style(
dim=1024,
depth=22,
heads=16,
ff_mult=2,
conv_layers=4,
mel_dim=80,
style_dim=32
)
cfm_params = CFMParams()
# εε§ε decoder
decoder = ConditionalCFM(
in_channels=160,
n_spks=0,
spk_emb_dim=80,
cfm_params=cfm_params,
estimator=estimator
)
# εε§ε樑ε
model = V2S_TTS_Pretrain_Model(
input_size=512,
output_size=80,
output_type='mel',
vocab_size=500,
spk_dim=192,
sll_checkpoint='checkpoints/wavlm_large.pt',
output_layer=6,
decoder=decoder,
encoder=encoder,
style_qformer=style_qformer
)
state_dict = torch.load(checkpoint_path)['state_dict']
new_params = {}
for k,v in state_dict.items():
if k.startswith('generator'):
new_k = k[10:]
new_params[new_k] = v
# Load the new parameters into the model
model.load_state_dict(new_params, strict=True)
model.eval()
return model
# ckpt_path = '/workspace/user_code/kuachen/projects/v2s/results/v2s_tts_pretrain_32_dim64/step_000336000.ckpt'
# config_name = 'v2s_tts_pretrain_32_dim64.yaml'
# model = load_model(config_name,ckpt_path)
# print(model.generator)
# model = get_pretrain_model(ckpt_path)
# state_dict = torch.load(ckpt_path)['state_dict']
# print(state_dict)
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