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import soundfile as sf
import numpy as np
from voxcpm import VoxCPM
import argparse
import os
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model_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)
parser.add_argument("--prompt_wav_path", type=str)
parser.add_argument("--prompt_text", type=str)
args = parser.parse_args()
assert args.text or args.text_file, "Please provide either text or text_file"
# validate prompt_wav_path and prompt_text 必须同时提供
if args.prompt_wav_path or args.prompt_text:
assert args.prompt_wav_path and args.prompt_text, "Please provide both prompt_wav_path and prompt_text"
model = VoxCPM.from_pretrained(args.model_path, load_denoiser=False)
if args.text:
wav = model.generate(
text=args.text,
prompt_wav_path=args.prompt_wav_path, # optional: path to a prompt speech for voice cloning
prompt_text=args.prompt_text, # optional: reference 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
normalize=True, # enable external TN tool
denoise=False, # enable external Denoise tool
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
)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
sf.write(f"{args.output_dir}/output.wav", wav, 16000)
print(f"saved: {args.output_dir}/output.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:
wav = model.generate(
text=text,
prompt_wav_path=args.prompt_wav_path, # optional: path to a prompt speech for voice cloning
prompt_text=args.prompt_text, # optional: reference 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
normalize=True, # enable external TN tool
denoise=False, # enable external Denoise tool
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
)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
sf.write(f"{args.output_dir}/{wav_id}.wav", wav, 16000)
print(f"saved: {args.output_dir}/{wav_id}.wav")
if __name__ == "__main__":
main()
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