79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
"""节拍行者 — 语音对白生成（Qwen3-TTS CustomVoice）。
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用法（必须 flock，GPU 与其他 agent 共享）：
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flock /home/xsl/SoundGame/.gpu.lock \
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/home/xsl/SoundGame/.venv-tts/bin/python gen_voice.py
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产出 web/rhythm/audio/voice/*.wav（24kHz），随后由 gen_convert.sh 转 ogg/m4a。
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"""
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import os
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os.environ.setdefault("HF_HOME", "/home/xsl/SoundGame/models/hf")
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os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
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import numpy as np
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import soundfile as sf
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import torch
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from qwen_tts import Qwen3TTSModel
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OUT = "/home/xsl/SoundGame/web/rhythm/audio/voice"
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os.makedirs(OUT, exist_ok=True)
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# (id, 文本, 情感指令)
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LINES = [
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("welcome",
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"欢迎来到节拍行者！规则很简单：仔细听提示音，在下一个节拍，做出对应的动作。"
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"建议用有线耳机或扬声器游玩，蓝牙耳机会有延迟。",
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"热情活力的游戏解说语气"),
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("rules",
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"听好了：上行音，向上滑；下行音，向下滑；低音，向左滑；高音，向右滑；"
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"一声轻响，单击；两声轻响，快速双击。",
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"清晰利落的教学语气，节奏感强"),
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("tut_start",
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"先来四小节练习。跟着节拍器，听到提示音，就在下一拍做动作。大胆做！",
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"鼓励的语气"),
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("tut_done",
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"练习结束！正式挑战开始，一共四十个指令。预备！",
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"兴奋、充满干劲的语气"),
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("grade_s",
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"评级，S！完美无缺，你就是节拍大师！",
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"极度兴奋、欢呼的语气"),
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("grade_a",
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"评级，A！非常出色，差一点就完美了！",
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"开心赞许的语气"),
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("grade_b",
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"评级，B。不错不错，继续加油！",
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"温和鼓励的语气"),
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("grade_c",
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"评级，C。别灰心，多听节拍，你一定行！",
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"安慰打气的语气"),
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("restart",
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"单击屏幕，再来一局。",
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"轻松愉快的语气"),
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]
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model = Qwen3TTSModel.from_pretrained(
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"Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
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device_map="cuda:0",
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dtype=torch.bfloat16,
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)
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wavs, sr = model.generate_custom_voice(
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text=[t for _, t, _ in LINES],
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language=["Chinese"] * len(LINES),
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speaker=["Vivian"] * len(LINES),
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instruct=[i for _, _, i in LINES],
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)
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for (vid, text, _), wav in zip(LINES, wavs):
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wav = np.asarray(wav, dtype=np.float32)
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dur = len(wav) / sr
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peak = float(np.max(np.abs(wav))) if len(wav) else 0.0
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assert dur > 0.3, f"{vid}: 时长异常 {dur:.2f}s"
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assert peak > 0.05, f"{vid}: 峰值异常 {peak:.3f}"
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path = os.path.join(OUT, vid + ".wav")
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sf.write(path, wav, sr)
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print(f"{vid}: {dur:.2f}s peak={peak:.3f} -> {path}")
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print("ALL OK")
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