68 lines
2.8 KiB
Python
68 lines
2.8 KiB
Python
from voxcpm import VoxCPM
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import soundfile as sf
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import torch
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from pathlib import Path
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import sys
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BASE_DIR = Path(__file__).resolve().parent
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DEVICE = "cuda"
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PROMPT_WAV = BASE_DIR / "lzl.wav"
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CACHE_PATH = BASE_DIR / "lzl_prompt_cache.pt"
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VOICE_DESIGN_PATH = BASE_DIR / "voice_design.wav"
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HIFI_CLONE_PATH = BASE_DIR / "hifi_clone.wav"
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PROMPT_TEXT = "亲爱的，今天的你要出发，挣钱喽，都没有系好安全带。 乖乖仔，我要到了，奇怪还没有分开就开始想你了，注意一下，闯红灯拍照，我可不喜欢明知孤犯的小坏蛋，安全带系一好，我们这边要出发喽。 小坏蛋，前方有限速拍照，姐姐给你盯着呢，车速太快了啊，慢一点慢一点降下来，不要让我害怕好吗？ 过最堵的路段，千万不要着急，姐姐会一直陪着你。 今日导航就先到这里了，哥哥注意安全停车哦。"
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TARGET_TEXT = "慢慢来吧，额度还在就好～"
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if DEVICE != "cuda":
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raise RuntimeError(f"Unsupported device: {DEVICE}. This script only supports CUDA.")
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if not torch.cuda.is_available():
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raise RuntimeError(
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"This script requires CUDA. "
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f"Current python: {sys.executable}, torch: {torch.__version__}. "
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"Please run it with a CUDA-enabled Python environment."
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)
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model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
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if model.tts_model.device != DEVICE:
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raise RuntimeError(f"Expected VoxCPM to run on {DEVICE}, got {model.tts_model.device}")
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print(f"VoxCPM loaded on {model.tts_model.device} with torch {torch.__version__}")
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wav = model.generate(
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text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM!",
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cfg_value=2.0,
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inference_timesteps=10,
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)
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sf.write(str(VOICE_DESIGN_PATH), wav, model.tts_model.sample_rate)
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print(f"Saved voice design to {VOICE_DESIGN_PATH}")
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# Build and persist the prompt cache so later runs can skip prompt encoding.
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if CACHE_PATH.exists():
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prompt_cache = torch.load(CACHE_PATH, map_location="cpu")
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print(f"Loaded prompt cache from {CACHE_PATH}")
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else:
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prompt_cache = model.tts_model.build_prompt_cache(
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prompt_wav_path=str(PROMPT_WAV),
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prompt_text=PROMPT_TEXT,
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reference_wav_path=str(PROMPT_WAV),
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)
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torch.save(prompt_cache, CACHE_PATH)
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print(f"Built and saved prompt cache to {CACHE_PATH}")
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cache_devices = {
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key: str(value.device)
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for key, value in prompt_cache.items()
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if isinstance(value, torch.Tensor)
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}
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print(f"Prompt cache tensor devices: {cache_devices}")
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wav, _, _ = model.tts_model.generate_with_prompt_cache(
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target_text=TARGET_TEXT,
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prompt_cache=prompt_cache,
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cfg_value=2.0,
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inference_timesteps=10,
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)
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wav = wav.detach().cpu().float().squeeze(0).numpy()
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sf.write(str(HIFI_CLONE_PATH), wav, model.tts_model.sample_rate)
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print(f"Saved cloned audio to {HIFI_CLONE_PATH}")
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