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import soundfile as sf
import numpy as np
from pathlib import Path
from voxcpm.model import VoxCPMModel
from voxcpm.model.voxcpm import LoRAConfig
from voxcpm.training.config import load_yaml_config
import argparse
import torch
import os
import re
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--lora_ckpt", type=str, required=True)
parser.add_argument("--lora_config_path", type=str, required=True)
parser.add_argument("--text", type=str)
parser.add_argument("--text_file", type=str)
parser.add_argument("--output_dir", type=str, default="outputs")
parser.add_argument("--cfg_value", type=float, default=2.0)
parser.add_argument("--inference_timesteps", type=int, default=10)
args = parser.parse_args()
assert args.text or args.text_file, "Please provide either text or text_file"
# 1. 读取 YAML 配置
cfg = load_yaml_config(args.lora_config_path)
pretrained_path = cfg["pretrained_path"]
lora_cfg_dict = cfg.get("lora", {}) or {}
lora_cfg = LoRAConfig(**lora_cfg_dict) if lora_cfg_dict else None
# 2. 加载基础模型（包含 LoRA 结构，并执行 torch.compile）
print(f"[1/3] 加载基础模型：{pretrained_path}")
model = VoxCPMModel.from_local(
pretrained_path,
optimize=True, # 先 compile，load_lora_weights 使用 named_parameters 兼容
training=False,
lora_config=lora_cfg,
)
from src.voxcpm.utils.text_normalize import TextNormalizer
text_normalizer = TextNormalizer()
# 3. 加载 LoRA 权重（在 compile 后也能正常工作）
ckpt_dir = Path(args.lora_ckpt)
if not ckpt_dir.exists():
raise FileNotFoundError(f"找不到 LoRA checkpoint: {ckpt_dir}")
print(f"[2/3] 加载 LoRA 权重：{ckpt_dir}")
loaded, skipped = model.load_lora_weights(str(ckpt_dir))
print(f" 已加载 {len(loaded)} 个参数")
if skipped:
print(f"[WARNING] 跳过 {len(skipped)} 个参数")
print(f" 跳过的 key (前5个): {skipped[:5]}")
print(f"\n[3/3] 开始推理...")
if args.text:
with torch.inference_mode():
target_text = args.text.replace("\n", " ")
target_text = re.sub(r'\s+', ' ', target_text)
target_text = text_normalizer.normalize(target_text)
wav = model.generate(
target_text=target_text,
cfg_value=args.cfg_value, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
inference_timesteps=args.inference_timesteps, # LocDiT inference timesteps, higher for better result, lower for fast speed
retry_badcase=True, # enable retrying mode for some bad cases (unstoppable)
retry_badcase_max_times=3, # maximum retrying times
retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
)
audio_np = wav.squeeze(0).cpu().numpy() if wav.dim() > 1 else wav.cpu().numpy()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
sf.write(f"{args.output_dir}/output_lora.wav", audio_np, 16000)
print(f"saved: {args.output_dir}/output_lora.wav")
elif args.text_file:
texts = []
with open(args.text_file, "r") as f:
lines = f.readlines()
for line in lines:
line = line.strip().split("||")
wav_id = line[0]
text = " ".join(line[1:])
texts.append((wav_id, text))
for wav_id, text in texts:
with torch.inference_mode():
target_text = text.replace("\n", " ")
target_text = re.sub(r'\s+', ' ', target_text)
target_text = text_normalizer.normalize(target_text)
wav = model.generate(
target_text=target_text,
cfg_value=args.cfg_value, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
inference_timesteps=args.inference_timesteps, # LocDiT inference timesteps, higher for better result, lower for fast speed
retry_badcase=True, # enable retrying mode for some bad cases (unstoppable)
retry_badcase_max_times=3, # maximum retrying times
retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
)
audio_np = wav.squeeze(0).cpu().numpy() if wav.dim() > 1 else wav.cpu().numpy()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
sf.write(f"{args.output_dir}/{wav_id}.wav", audio_np, 16000)
print(f"saved: {args.output_dir}/{wav_id}.wav")
if __name__ == "__main__":
main()
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