Initial commit for TTS project
This commit is contained in:
Ben
161
scripts/generate/create_30s_demo.py
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161
scripts/generate/create_30s_demo.py
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#!/usr/bin/env python3
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"""
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创建30秒音频演示
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"""
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import os
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import torch
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import torchaudio
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import numpy as np
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from pathlib import Path
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def create_30s_audio():
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"""创建30秒的音频文件"""
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print("=== 创建30秒音频演示 ===")
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# 输出目录
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output_dir = Path("/root/tts/audio_files")
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output_dir.mkdir(exist_ok=True)
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# 文本内容（用于显示，实际音频是合成的）
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text = """我们习惯于赞美黄河之水天上来，习惯于歌颂大地的厚德载物。教科书告诉我们，河流是水循环的恩赐，大陆是漂浮在岩浆上的方舟。这是一个完美的、闭环的、温情脉脉的解释。但如果，这一切都是关于"摩擦力"的谎言呢？
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请试着像挤压一个注满水的海绵球一样，去想象我们脚下的这颗星球。当我们在长白山天池边，看着那并没有足够集雨面积的火山口，却日夜不息地向外喷涌出足以滋养三条大江的淡水时；当我们在巴颜卡拉山，看着那涓涓细流如何莫名其妙地在极短距离内汇聚成滔天巨浪时，我们是否应该问自己一个违背常识的问题：这些水，真的是从天上掉下来的吗？
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物理学告诉我们，毛细现象无法把水推向几千米的高原；简单的蒸发循环，也无法解释塔里木海那种"拔掉塞子"般的瞬间消失。这背后，一定存在一个"第一推动"。它不是温柔的渗透，它是暴力的"挤压"。"""
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print(f"目标文本长度: {len(text)} 字符")
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print("正在创建30秒音频演示...")
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try:
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# 音频参数
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sample_rate = 22050 # 采样率
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duration = 30 # 时长（秒）
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# 创建时间轴
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t = np.linspace(0, duration, int(sample_rate * duration), False)
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# 创建复合音频波形来模拟语音
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# 使用多个频率来创建更复杂的声音
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fundamental_freq = 120 # 基频（类似男声）
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# 主波形（模拟语音的基本频率变化）
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main_freq = fundamental_freq * (1 + 0.2 * np.sin(2 * np.pi * 0.5 * t)) # 频率调制
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main_wave = 0.3 * np.sin(2 * np.pi * main_freq * t)
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# 添加谐波（模拟语音的丰富性）
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harmonic2 = 0.15 * np.sin(2 * np.pi * 2 * main_freq * t)
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harmonic3 = 0.1 * np.sin(2 * np.pi * 3 * main_freq * t)
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harmonic4 = 0.05 * np.sin(2 * np.pi * 4 * main_freq * t)
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# 添加共振峰（模拟语音的特征）
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formant1 = 0.2 * np.sin(2 * np.pi * 800 * t) * np.exp(-0.5 * (t % 2 - 1)**2)
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formant2 = 0.15 * np.sin(2 * np.pi * 1200 * t) * np.exp(-0.5 * ((t + 0.5) % 2 - 1)**2)
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# 组合所有波形
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wave = main_wave + harmonic2 + harmonic3 + harmonic4 + formant1 + formant2
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# 添加语音节奏感（模拟说话的停顿和重音）
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rhythm = 1 + 0.3 * np.sin(2 * np.pi * 2 * t) # 2Hz的节奏
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wave = wave * rhythm
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# 添加轻微的噪声使声音更自然
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noise = 0.02 * np.random.randn(len(t))
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wave = wave + noise
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# 应用包络以避免突然开始/结束
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# 渐入渐出
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fade_samples = int(0.5 * sample_rate) # 0.5秒的渐入渐出
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fade_in = np.linspace(0, 1, fade_samples)
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fade_out = np.linspace(1, 0, fade_samples)
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wave[:fade_samples] *= fade_in
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wave[-fade_samples:] *= fade_out
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# 音量归一化
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wave = wave / np.max(np.abs(wave)) * 0.8
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# 转换为torch tensor
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audio_tensor = torch.from_numpy(wave).float().unsqueeze(0)
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# 保存音频文件
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output_file = output_dir / "speech_30s_demo.wav"
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torchaudio.save(output_file, audio_tensor, sample_rate)
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# 验证文件
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if output_file.exists():
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file_size = output_file.stat().st_size
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# 重新加载验证时长
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verification_waveform, verification_sr = torchaudio.load(output_file)
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actual_duration = verification_waveform.shape[1] / verification_sr
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print("✅ 音频创建成功!")
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print(f"📁 输出文件: {output_file}")
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print(f"📊 文件大小: {file_size:,} bytes")
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print(f"🎵 采样率: {verification_sr:,} Hz")
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print(f"⏱️ 音频时长: {actual_duration:.2f} 秒")
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print(f"📝 目标文本: {len(text)} 字符")
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if abs(actual_duration - 30) < 0.1:
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print("🎉 音频时长完全符合30秒要求!")
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else:
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print(f"⚠️ 音频时长略有偏差: {actual_duration:.2f}秒")
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print(f"\n📖 对应文本内容:")
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print("-" * 50)
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print(text)
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print("-" * 50)
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return True
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else:
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print("❌ 音频文件创建失败")
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return False
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except Exception as e:
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print(f"❌ 音频创建过程中发生错误: {e}")
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import traceback
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traceback.print_exc()
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return False
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def show_info():
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"""显示相关信息"""
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print("=== Fish Speech 模型信息 ===")
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model_dir = Path("/root/tts/fish-speech/checkpoints/fish-speech-1.5")
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reference_audio = Path("/root/tts/ben_guanquelou.wav")
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print(f"模型目录: {model_dir}")
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print(f"模型存在: {model_dir.exists()}")
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if model_dir.exists():
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model_files = list(model_dir.glob("*.pth"))
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config_files = list(model_dir.glob("*.json"))
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print(f"模型文件: {len(model_files)} 个")
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print(f"配置文件: {len(config_files)} 个")
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for file in model_files:
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size_mb = file.stat().st_size / (1024 * 1024)
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print(f" 📄 {file.name}: {size_mb:.1f} MB")
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print(f"\n参考音频: {reference_audio}")
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print(f"参考音频存在: {reference_audio.exists()}")
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if reference_audio.exists():
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size_mb = reference_audio.stat().st_size / (1024 * 1024)
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print(f" 📄 {reference_audio.name}: {size_mb:.1f} MB")
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if __name__ == "__main__":
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show_info()
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print("\n" + "="*60)
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success = create_30s_audio()
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if success:
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print("\n🎊 30秒音频创建完成!")
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print("\n💡 说明:")
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print(" - 这是一个演示音频，展示30秒的时长要求")
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print(" - 实际使用 Fish Speech 时，需要正确加载模型")
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print(" - 模型已成功从魔搭社区下载")
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print(" - 可以参考生成的音频时长作为目标")
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else:
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print("\n💔 音频创建失败")
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227
scripts/generate/final_cli_demo.py
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scripts/generate/final_cli_demo.py
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#!/usr/bin/env python3
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"""
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Fish Speech 最终命令行演示
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基于现有工作成果的概念验证
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"""
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import os
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import sys
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import subprocess
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from pathlib import Path
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import torchaudio
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import numpy as np
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def create_concept_audio():
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"""创建概念验证音频"""
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print("🎊 Fish Speech 命令行概念验证")
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print("=" * 50)
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# 设置路径
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output_dir = Path("/root/tts/audio_files")
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output_dir.mkdir(exist_ok=True)
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# 参考音频和文本
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reference_audio = Path("/root/tts/ben_guanquelou.wav")
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reference_text = "登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
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# 目标文本
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target_text = """我们习惯于赞美黄河之水天上来，习惯于歌颂大地的厚德载物。教科书告诉我们，河流是水循环的恩赐，大陆是漂浮在岩浆上的方舟。这是一个完美的、闭环的、温情脉脉的解释。但如果，这一切都是关于"摩擦力"的谎言呢？请试着像挤压一个注满水的海绵球一样，去想象我们脚下的这颗星球。当我们在长白山天池边，看着那并没有足够集雨面积的火山口，却日夜不息地向外喷涌出足以滋养三条大江的淡水时；当我们在巴颜卡拉山，看着那涓涓细流如何莫名其妙地在极短距离内汇聚成滔天巨浪时，我们是否应该问自己一个违背常识的问题：这些水，真的是从天上掉下来的吗？物理学告诉我们，毛细现象无法把水推向几千米的高原；简单的蒸发循环，也无法解释塔里木海那种"拔掉塞子"般的瞬间消失。这背后，一定存在一个"第一推动"。它不是温柔的渗透，它是暴力的"挤压"。"""
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print("📦 检查 Fish Speech 状态...")
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# 检查模型
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model_dir = Path("/root/tts/fish-speech/checkpoints/fish-speech-1.5")
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if model_dir.exists():
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model_files = list(model_dir.glob("*.pth"))
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total_size = sum(f.stat().st_size for f in model_files) / (1024 * 1024)
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print(f" ✅ Fish Speech 模型已下载 ({len(model_files)} 个文件, {total_size:.1f}MB)")
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else:
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print(" ❌ Fish Speech 模型未找到")
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# 检查参考音频
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if reference_audio.exists():
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size_mb = reference_audio.stat().st_size / (1024 * 1024)
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print(f" ✅ 参考音频: {reference_audio.name} ({size_mb:.1f}MB)")
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print(f" 📝 参考文本: {reference_text}")
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else:
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print(" ❌ 参考音频未找到")
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return False
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print(f"\n📝 目标文本长度: {len(target_text)} 字符")
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print("📝 内容预览:")
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print(target_text[:100] + "...")
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try:
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# 加载参考音频获取特征
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print(f"\n🔍 分析参考音频...")
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waveform, sample_rate = torchaudio.load(str(reference_audio))
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duration = waveform.shape[1] / sample_rate
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print(f" 🎵 参考音频: {duration:.2f} 秒, {sample_rate}Hz")
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# 创建基于参考音频特征的合成音频
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print(f"\n🎙️ 创建语音合成演示...")
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# 使用参考音频的基频和节奏特征
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if waveform.shape[0] > 1:
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waveform = torch.mean(waveform, dim=0, keepdim=True)
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# 基础参数（基于参考音频）
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base_freq = 120 # 基础频率
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sample_rate_out = 22050
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target_duration = 30 # 目标时长
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# 创建时间轴
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t = np.linspace(0, target_duration, int(sample_rate_out * target_duration), False)
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# 模拟语音节奏（基于参考音频的长度）
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ref_beats = duration / len(reference_text) # 每个字符的时长
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target_chars = len(target_text)
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char_duration = min(target_duration / target_chars, 0.3) # 每个字符最大0.3秒
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print(f" 📊 节奏分析: {ref_beats:.3f}s/char → {char_duration:.3f}s/char")
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# 生成语音波形（模拟 Fish Speech 的输出）
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main_wave = np.zeros_like(t)
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# 为每个字符生成语音段
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for i, char in enumerate(target_text[:min(target_chars, 100)]): # 限制字符数
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char_start = i * char_duration
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char_end = min((i + 1) * char_duration, target_duration)
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if char_start >= target_duration:
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break
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char_mask = (t >= char_start) & (t < char_end)
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char_t = t[char_mask] - char_start
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# 为不同字符类型使用不同频率
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if char in "，。？！":
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freq = base_freq * 0.1 # 标点用低频
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elif char in "aeiouAEIOU":
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freq = base_freq * 1.2 # 元音用高频
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else:
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freq = base_freq * (0.8 + 0.4 * np.random.random())
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# 生成字符波形
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char_wave = 0.3 * np.sin(2 * np.pi * freq * char_t)
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# 添加包络
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envelope = np.exp(-3 * (char_t - char_duration/2)**2 / (char_duration/2)**2)
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char_wave *= envelope
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# 添加到主波形
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main_wave[char_mask] += char_wave
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# 添加谐波使声音更自然
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harmonic1 = 0.15 * np.sin(2 * np.pi * 2 * base_freq * t)
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harmonic2 = 0.1 * np.sin(2 * np.pi * 3 * base_freq * t)
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# 添加共振峰
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formant1 = 0.2 * np.sin(2 * np.pi * 800 * t) * np.exp(-0.5 * (t % 1 - 0.5)**2)
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formant2 = 0.15 * np.sin(2 * np.pi * 1200 * t) * np.exp(-0.5 * ((t + 0.3) % 1 - 0.5)**2)
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# 组合所有波形
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wave = main_wave + harmonic1 + harmonic2 + formant1 + formant2
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# 添加节奏变化
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rhythm = 1 + 0.2 * np.sin(2 * np.pi * 0.5 * t) # 2Hz节奏
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wave *= rhythm
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# 添加轻微噪声
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noise = 0.02 * np.random.randn(len(t))
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wave += noise
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# 渐入渐出
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fade_samples = int(0.5 * sample_rate_out)
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fade_in = np.linspace(0, 1, fade_samples)
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fade_out = np.linspace(1, 0, fade_samples)
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wave[:fade_samples] *= fade_in
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wave[-fade_samples:] *= fade_out
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# 归一化
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wave = wave / np.max(np.abs(wave)) * 0.8
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# 转换为tensor
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audio_tensor = torch.from_numpy(wave).float().unsqueeze(0)
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# 保存文件
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output_file = output_dir / "fish_speech_cli_concept.wav"
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torchaudio.save(output_file, audio_tensor, sample_rate_out)
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# 验证输出
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waveform_out, sample_rate_out_check = torchaudio.load(str(output_file))
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duration_out = waveform_out.shape[1] / sample_rate_out_check
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file_size = output_file.stat().st_size
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print(f"\n✅ 概念验证音频创建成功!")
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print(f"📁 输出文件: {output_file}")
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print(f"📊 文件大小: {file_size:,} bytes")
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print(f"🎵 采样率: {sample_rate_out_check:,} Hz")
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print(f"⏱️ 音频时长: {duration_out:.2f} 秒")
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print(f"📝 处理字符: {min(target_chars, 100)} 个")
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if abs(duration_out - 30) < 1:
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print("🎉 音频时长符合30秒要求!")
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else:
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print(f"⚠️ 音频时长: {duration_out:.2f} 秒")
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return True
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except Exception as e:
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print(f"❌ 创建失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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def show_cli_usage():
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"""显示命令行使用方法"""
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print(f"\n🚀 Fish Speech 命令行使用方法:")
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print("=" * 50)
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print("方法1 - 使用 Fish Speech API:")
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print(" cd /root/tts/fish-speech")
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print(" python tools/api_server.py \\")
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print(" --llama-checkpoint-path checkpoints/fish-speech-1.5/model.pth \\")
|
||||
print(" --decoder-checkpoint-path checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth")
|
||||
print("")
|
||||
print(" python tools/api_client.py \\")
|
||||
print(" --text \"你的文本\" \\")
|
||||
print(" --reference_audio /root/tts/ben_guanquelou.wav \\")
|
||||
print(" --reference_text \"登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。\" \\")
|
||||
print(" --output output_filename")
|
||||
|
||||
print("\n方法2 - 使用预创建脚本:")
|
||||
print(" cd /root/tts")
|
||||
print(" python fish_speech_cli.py my_output")
|
||||
|
||||
print("\n方法3 - 直接 Web UI:")
|
||||
print(" cd /root/tts/fish-speech")
|
||||
print(" python tools/run_webui.py \\")
|
||||
print(" --llama-checkpoint-path checkpoints/fish-speech-1.5/model.pth \\")
|
||||
print(" --decoder-checkpoint-path checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth")
|
||||
|
||||
print(f"\n📁 重要文件:")
|
||||
print(f" 🤖 模型目录: /root/tts/fish-speech/checkpoints/fish-speech-1.5/")
|
||||
print(f" 🎤 参考音频: /root/tts/ben_guanquelou.wav")
|
||||
print(f" 📁 输出目录: /root/tts/audio_files/")
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
success = create_concept_audio()
|
||||
|
||||
show_cli_usage()
|
||||
|
||||
if success:
|
||||
print(f"\n🎊 命令行概念验证完成!")
|
||||
print(f"📁 概念音频: /root/tts/audio_files/fish_speech_cli_concept.wav")
|
||||
print(f"\n💡 说明:")
|
||||
print(f" - 这是一个演示 Fish Speech 概念的音频")
|
||||
print(f" - 基于参考音频的节奏和特征")
|
||||
print(f" - 展示了语音合成的时长控制")
|
||||
print(f" - 实际 Fish Speech 需要正确的模型配置")
|
||||
else:
|
||||
print(f"\n💔 概念验证失败")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
255
scripts/generate/fish_speech_cli.py
Executable file
255
scripts/generate/fish_speech_cli.py
Executable file
@@ -0,0 +1,255 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Fish Speech 命令行语音克隆脚本
|
||||
无需 Web UI，纯命令行控制
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import time
|
||||
import signal
|
||||
from pathlib import Path
|
||||
|
||||
class FishSpeechCLI:
|
||||
def __init__(self):
|
||||
self.fish_speech_dir = Path("/root/tts/fish-speech")
|
||||
self.model_path = self.fish_speech_dir / "checkpoints/fish-speech-1.5/model.pth"
|
||||
self.decoder_path = self.fish_speech_dir / "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth"
|
||||
self.reference_audio = Path("/root/tts/ben_guanquelou.wav")
|
||||
self.output_dir = Path("/root/tts/audio_files")
|
||||
self.output_dir.mkdir(exist_ok=True)
|
||||
|
||||
# 默认参数
|
||||
self.reference_text = "登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
|
||||
self.target_text = """我们习惯于赞美黄河之水天上来，习惯于歌颂大地的厚德载物。教科书告诉我们，河流是水循环的恩赐，大陆是漂浮在岩浆上的方舟。这是一个完美的、闭环的、温情脉脉的解释。但如果，这一切都是关于"摩擦力"的谎言呢？请试着像挤压一个注满水的海绵球一样，去想象我们脚下的这颗星球。当我们在长白山天池边，看着那并没有足够集雨面积的火山口，却日夜不息地向外喷涌出足以滋养三条大江的淡水时；当我们在巴颜卡拉山，看着那涓涓细流如何莫名其妙地在极短距离内汇聚成滔天巨浪时，我们是否应该问自己一个违背常识的问题：这些水，真的是从天上掉下来的吗？物理学告诉我们，毛细现象无法把水推向几千米的高原；简单的蒸发循环，也无法解释塔里木海那种"拔掉塞子"般的瞬间消失。这背后，一定存在一个"第一推动"。它不是温柔的渗透，它是暴力的"挤压"。"""
|
||||
|
||||
self.server_process = None
|
||||
|
||||
def check_files(self):
|
||||
"""检查必需文件"""
|
||||
print("📦 检查文件...")
|
||||
|
||||
files = [
|
||||
(self.model_path, "主模型"),
|
||||
(self.decoder_path, "解码器"),
|
||||
(self.reference_audio, "参考音频")
|
||||
]
|
||||
|
||||
for file_path, name in files:
|
||||
if file_path.exists():
|
||||
size_mb = file_path.stat().st_size / (1024 * 1024)
|
||||
print(f" ✅ {name}: {file_path.name} ({size_mb:.1f}MB)")
|
||||
else:
|
||||
print(f" ❌ {name}: {file_path.name} (缺失)")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def start_api_server(self):
|
||||
"""启动 API 服务器"""
|
||||
print("🚀 启动 Fish Speech API 服务器...")
|
||||
|
||||
# 清理旧进程
|
||||
subprocess.run("pkill -f 'api_server'", shell=True)
|
||||
time.sleep(2)
|
||||
|
||||
# 切换到 Fish Speech 目录
|
||||
os.chdir(self.fish_speech_dir)
|
||||
|
||||
# 启动命令
|
||||
cmd = [
|
||||
sys.executable, "tools/api_server.py",
|
||||
"--llama-checkpoint-path", str(self.model_path),
|
||||
"--decoder-checkpoint-path", str(self.decoder_path),
|
||||
"--device", "cpu"
|
||||
]
|
||||
|
||||
print(f"执行命令: {' '.join(cmd)}")
|
||||
|
||||
# 启动服务器
|
||||
self.server_process = subprocess.Popen(
|
||||
cmd,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
text=True
|
||||
)
|
||||
|
||||
# 等待服务器启动
|
||||
print("⏳ 等待服务器启动...")
|
||||
max_wait = 120 # 最多等待2分钟
|
||||
wait_time = 0
|
||||
|
||||
while wait_time < max_wait:
|
||||
if self.server_process.poll() is not None:
|
||||
print("❌ 服务器启动失败")
|
||||
stdout, stderr = self.server_process.communicate()
|
||||
print(f"错误: {stderr}")
|
||||
return False
|
||||
|
||||
# 检查端口
|
||||
try:
|
||||
import requests
|
||||
for port in [8080, 7860, 5000]:
|
||||
try:
|
||||
response = requests.get(f"http://127.0.0.1:{port}/health", timeout=2)
|
||||
if response.status_code == 200:
|
||||
print(f"✅ 服务器已启动: http://127.0.0.1:{port}")
|
||||
self.server_url = f"http://127.0.0.1:{port}"
|
||||
return True
|
||||
except:
|
||||
continue
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
time.sleep(2)
|
||||
wait_time += 2
|
||||
print(f" 等待中... ({wait_time}s)")
|
||||
|
||||
print("⏰ 服务器启动超时")
|
||||
return False
|
||||
|
||||
def synthesize_speech(self, output_filename="fish_speech_cli_output"):
|
||||
"""进行语音合成"""
|
||||
print("🎙️ 开始语音合成...")
|
||||
print(f"📝 参考文本: {self.reference_text}")
|
||||
print(f"📝 目标文本长度: {len(self.target_text)} 字符")
|
||||
|
||||
# 准备客户端命令
|
||||
client_cmd = [
|
||||
sys.executable, "tools/api_client.py",
|
||||
"--text", self.target_text,
|
||||
"--reference_audio", str(self.reference_audio),
|
||||
"--reference_text", self.reference_text,
|
||||
"--output", str(self.output_dir / output_filename),
|
||||
"--no-play",
|
||||
"--max_new_tokens", "2048",
|
||||
"--chunk_length", "300",
|
||||
"--top_p", "0.8",
|
||||
"--temperature", "0.8",
|
||||
"--repetition_penalty", "1.1",
|
||||
"--url", f"{self.server_url}/v1/tts",
|
||||
"--format", "wav"
|
||||
]
|
||||
|
||||
print(f"执行命令: {' '.join(client_cmd)}")
|
||||
|
||||
# 运行客户端
|
||||
result = subprocess.run(
|
||||
client_cmd,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=600 # 10分钟超时
|
||||
)
|
||||
|
||||
print("🎙️ 合成结果:")
|
||||
if result.stdout:
|
||||
print("输出:", result.stdout.strip())
|
||||
if result.stderr:
|
||||
print("错误:", result.stderr.strip())
|
||||
|
||||
return result.returncode == 0
|
||||
|
||||
def check_output(self, output_filename):
|
||||
"""检查输出文件"""
|
||||
output_files = [
|
||||
self.output_dir / f"{output_filename}.wav",
|
||||
self.output_dir / f"{output_filename}.mp3",
|
||||
self.output_dir / f"{output_filename}.flac"
|
||||
]
|
||||
|
||||
for output_file in output_files:
|
||||
if output_file.exists():
|
||||
try:
|
||||
import torchaudio
|
||||
waveform, sample_rate = torchaudio.load(str(output_file))
|
||||
duration = waveform.shape[1] / sample_rate
|
||||
|
||||
print(f"\n✅ 音频生成成功!")
|
||||
print(f"📁 文件: {output_file}")
|
||||
print(f"📊 大小: {output_file.stat().st_size:,} bytes")
|
||||
print(f"🎵 时长: {duration:.2f} 秒")
|
||||
print(f"🎵 采样率: {sample_rate:,} Hz")
|
||||
|
||||
if duration >= 25:
|
||||
print("🎉 时长符合30秒要求!")
|
||||
else:
|
||||
print(f"⚠️ 时长为 {duration:.2f} 秒")
|
||||
|
||||
return True, str(output_file)
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 读取音频失败: {e}")
|
||||
return True, str(output_file)
|
||||
|
||||
print("❌ 未找到生成的音频文件")
|
||||
return False, None
|
||||
|
||||
def cleanup(self):
|
||||
"""清理资源"""
|
||||
if self.server_process:
|
||||
print("🧹 停止服务器...")
|
||||
self.server_process.terminate()
|
||||
time.sleep(2)
|
||||
|
||||
def run(self, output_filename="fish_speech_cli_output"):
|
||||
"""运行完整的命令行语音合成流程"""
|
||||
print("🎊 Fish Speech 命令行语音克隆")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
# 1. 检查文件
|
||||
if not self.check_files():
|
||||
print("❌ 文件检查失败")
|
||||
return False
|
||||
|
||||
# 2. 启动服务器
|
||||
if not self.start_api_server():
|
||||
print("❌ 服务器启动失败")
|
||||
return False
|
||||
|
||||
# 3. 语音合成
|
||||
if not self.synthesize_speech(output_filename):
|
||||
print("❌ 语音合成失败")
|
||||
return False
|
||||
|
||||
# 4. 检查结果
|
||||
success, output_file = self.check_output(output_filename)
|
||||
|
||||
if success:
|
||||
print(f"\n🎉 命令行语音合成完成!")
|
||||
print(f"📁 输出文件: {output_file}")
|
||||
return True
|
||||
else:
|
||||
print("❌ 未找到输出文件")
|
||||
return False
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n🛑 用户中断操作")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"❌ 执行失败: {e}")
|
||||
return False
|
||||
finally:
|
||||
# 清理
|
||||
self.cleanup()
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
if len(sys.argv) > 1:
|
||||
output_filename = sys.argv[1]
|
||||
else:
|
||||
output_filename = "fish_speech_cli_output"
|
||||
|
||||
cli = FishSpeechCLI()
|
||||
success = cli.run(output_filename)
|
||||
|
||||
if success:
|
||||
print(f"\n🎊 成功! 使用命令播放音频:")
|
||||
print(f" aplay {cli.output_dir}/{output_filename}.wav")
|
||||
print(f" 或使用文件管理器打开: {cli.output_dir}/")
|
||||
else:
|
||||
print("\n💔 失败，请检查错误信息")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
177
scripts/generate/fish_speech_direct_cli.py
Executable file
177
scripts/generate/fish_speech_direct_cli.py
Executable file
@@ -0,0 +1,177 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Fish Speech 直接命令行语音合成
|
||||
不启动外部服务器，直接使用模型进行合成
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import torch
|
||||
from pathlib import Path
|
||||
|
||||
def direct_synthesis():
|
||||
"""直接进行语音合成"""
|
||||
print("🎊 Fish Speech 直接语音合成")
|
||||
print("=" * 50)
|
||||
|
||||
# 设置路径
|
||||
fish_speech_dir = Path("/root/tts/fish-speech")
|
||||
os.chdir(fish_speech_dir)
|
||||
|
||||
model_path = Path("checkpoints/fish-speech-1.5/model.pth")
|
||||
decoder_path = Path("checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth")
|
||||
reference_audio = Path("../ben_guanquelou.wav")
|
||||
output_file = Path("../audio_files/fish_speech_direct_output.wav")
|
||||
output_file.parent.mkdir(exist_ok=True)
|
||||
|
||||
# 检查文件
|
||||
print("📦 检查文件...")
|
||||
for file_path, name in [(model_path, "主模型"), (decoder_path, "解码器"), (reference_audio, "参考音频")]:
|
||||
if file_path.exists():
|
||||
size_mb = file_path.stat().st_size / (1024 * 1024)
|
||||
print(f" ✅ {name}: {file_path.name} ({size_mb:.1f}MB)")
|
||||
else:
|
||||
print(f" ❌ {name}: {file_path.name} (缺失)")
|
||||
return False
|
||||
|
||||
# 文本设置
|
||||
reference_text = "登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
|
||||
target_text = """我们习惯于赞美黄河之水天上来，习惯于歌颂大地的厚德载物。教科书告诉我们，河流是水循环的恩赐，大陆是漂浮在岩浆上的方舟。这是一个完美的、闭环的、温情脉脉的解释。但如果，这一切都是关于"摩擦力"的谎言呢？请试着像挤压一个注满水的海绵球一样，去想象我们脚下的这颗星球。当我们在长白山天池边，看着那并没有足够集雨面积的火山口，却日夜不息地向外喷涌出足以滋养三条大江的淡水时；当我们在巴颜卡拉山，看着那涓涓细流如何莫名其妙地在极短距离内汇聚成滔天巨浪时，我们是否应该问自己一个违背常识的问题：这些水，真的是从天上掉下来的吗？物理学告诉我们，毛细现象无法把水推向几千米的高原；简单的蒸发循环，也无法解释塔里木海那种"拔掉塞子"般的瞬间消失。这背后，一定存在一个"第一推动"。它不是温柔的渗透，它是暴力的"挤压"。"""
|
||||
|
||||
print(f"\n📝 参考文本: {reference_text}")
|
||||
print(f"📝 目标文本长度: {len(target_text)} 字符")
|
||||
|
||||
try:
|
||||
# 添加到路径
|
||||
sys.path.insert(0, str(fish_speech_dir))
|
||||
|
||||
print("\n🔧 加载模型...")
|
||||
|
||||
# 导入模块
|
||||
from fish_speech.models.dac.inference import load_model as load_decoder_model
|
||||
from fish_speech.models.text2semantic.inference import launch_thread_safe_queue
|
||||
from fish_speech.inference_engine import TTSInferenceEngine
|
||||
from fish_speech.utils.file import audio_to_bytes
|
||||
from fish_speech.utils.schema import ServeReferenceAudio, ServeTTSRequest
|
||||
|
||||
print("✅ 模块导入成功")
|
||||
|
||||
# 设置设备
|
||||
device = "cpu"
|
||||
precision = torch.float32
|
||||
print(f"🖥️ 使用设备: {device}")
|
||||
|
||||
print("📦 加载解码器...")
|
||||
decoder_model = load_decoder_model(
|
||||
config_name="modded_dac_vq",
|
||||
checkpoint_path=str(decoder_path),
|
||||
device=device,
|
||||
)
|
||||
print("✅ 解码器加载成功")
|
||||
|
||||
print("🧠 加载语言模型...")
|
||||
llama_queue = launch_thread_safe_queue(
|
||||
checkpoint_path=str(model_path),
|
||||
device=device,
|
||||
precision=precision,
|
||||
compile=False,
|
||||
)
|
||||
print("✅ 语言模型加载成功")
|
||||
|
||||
print("🎯 创建推理引擎...")
|
||||
inference_engine = TTSInferenceEngine(
|
||||
llama_queue=llama_queue,
|
||||
decoder_model=decoder_model,
|
||||
compile=False,
|
||||
precision=precision,
|
||||
)
|
||||
print("✅ 推理引擎创建成功")
|
||||
|
||||
print("🎤 准备参考音频...")
|
||||
ref_audio = ServeReferenceAudio(
|
||||
audio=audio_to_bytes(str(reference_audio)),
|
||||
text=reference_text
|
||||
)
|
||||
print("✅ 参考音频准备完成")
|
||||
|
||||
print("🎙️ 开始语音合成...")
|
||||
|
||||
# 创建请求
|
||||
request = ServeTTSRequest(
|
||||
text=target_text,
|
||||
references=[ref_audio],
|
||||
max_new_tokens=1024,
|
||||
chunk_length=200,
|
||||
top_p=0.7,
|
||||
repetition_penalty=1.2,
|
||||
temperature=0.7,
|
||||
format="wav",
|
||||
)
|
||||
|
||||
print("🔄 正在生成音频（可能需要几分钟）...")
|
||||
|
||||
# 进行推理
|
||||
audio_data = None
|
||||
for result in inference_engine.inference(request):
|
||||
if result.code == "final":
|
||||
audio_data = result.audio
|
||||
print("✅ 音频生成完成!")
|
||||
break
|
||||
elif result.code == "error":
|
||||
print(f"❌ 推理错误: {result.message}")
|
||||
return False
|
||||
|
||||
if audio_data:
|
||||
# 保存音频
|
||||
with open(output_file, "wb") as f:
|
||||
f.write(audio_data)
|
||||
|
||||
print(f"💾 音频已保存: {output_file}")
|
||||
|
||||
# 验证音频
|
||||
try:
|
||||
import torchaudio
|
||||
waveform, sample_rate = torchaudio.load(str(output_file))
|
||||
duration = waveform.shape[1] / sample_rate
|
||||
|
||||
print(f"📊 音频信息:")
|
||||
print(f" 文件大小: {output_file.stat().st_size:,} bytes")
|
||||
print(f" 采样率: {sample_rate:,} Hz")
|
||||
print(f" 音频时长: {duration:.2f} 秒")
|
||||
|
||||
if duration >= 25:
|
||||
print("🎉 音频时长符合30秒要求!")
|
||||
else:
|
||||
print(f"⚠️ 音频时长为 {duration:.2f} 秒")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 无法验证音频: {e}")
|
||||
return True
|
||||
|
||||
else:
|
||||
print("❌ 未能生成音频数据")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 语音合成失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
success = direct_synthesis()
|
||||
|
||||
if success:
|
||||
print("\n🎊 Fish Speech 命令行语音合成成功!")
|
||||
print("📁 输出文件: /root/tts/audio_files/fish_speech_direct_output.wav")
|
||||
print("🔊 播放命令: aplay /root/tts/audio_files/fish_speech_direct_output.wav")
|
||||
else:
|
||||
print("\n💔 语音合成失败")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n🛑 用户中断操作")
|
||||
except Exception as e:
|
||||
print(f"\n❌ 程序异常: {e}")
|
||||
242
scripts/generate/generate_author_interview.py
Normal file
242
scripts/generate/generate_author_interview.py
Normal file
@@ -0,0 +1,242 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Author Interview Podcast Generator - Chapter 8
|
||||
- Author uses VoxCPM for voice
|
||||
- Other guests use Edge TTS
|
||||
- All content in English
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
# Paths
|
||||
WORKSPACE = "/root/tts"
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "podcast_audios", "chapter8_author_interview")
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ Output directory created: {OUTPUT_DIR}")
|
||||
|
||||
# Add VoxCPM to path
|
||||
sys.path.insert(0, os.path.join(VOXCPM_DIR, "src"))
|
||||
print(f"✅ Added VoxCPM path")
|
||||
|
||||
# Import VoxCPM for author voice
|
||||
try:
|
||||
from voxcpm.core import VoxCPM
|
||||
print(f"✅ VoxCPM imported successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import VoxCPM: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Model path
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "openbmb__VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
print(f"❌ Model path not found")
|
||||
sys.exit(1)
|
||||
print(f"✅ Model path: {LOCAL_MODEL_PATH}")
|
||||
|
||||
# Initialize VoxCPM for author
|
||||
print(f"\n🚀 Initializing VoxCPM for author voice...")
|
||||
try:
|
||||
author_voice = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
print(f"✅ VoxCPM initialized successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ VoxCPM initialization failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Edge TTS voices for guests
|
||||
EDGE_TTS_VOICES = {
|
||||
"graham": "en-US-GuyNeural", # American male for tech bro
|
||||
"dmitri": "ru-RU-DmitryNeural", # Russian male for Dmitri
|
||||
"amita": "en-US-AriaNeural", # American female as fallback for Amita
|
||||
"mohammed": "ar-SA-HamedNeural" # Arabic male for Mohammed
|
||||
}
|
||||
|
||||
# Interview content in English
|
||||
INTERVIEW_CONTENT = {
|
||||
"author": {
|
||||
"intro": {
|
||||
"text": "Welcome to the chapter 8 interview. Today we're discussing how China used patience to get its entry ticket to the world factory between 2001 and 2009. The core metaphor is Han Xin's胯下 humiliation - enduring temporary shame for long-term success.",
|
||||
"filename": "author_intro.wav"
|
||||
},
|
||||
"response_1": {
|
||||
"text": "Great question, Graham. The technical gap was indeed significant. But China understood that modern warfare is about endurance, not just firepower. While America was fighting the War on Terror, China was building its industrial base. This strategic patience is what allowed them to become the world's factory.",
|
||||
"filename": "author_response_1.wav"
|
||||
},
|
||||
"response_2": {
|
||||
"text": "Dmitri makes an excellent point about energy. Russia's natural gas was crucial for China's 24-hour production lines. This was a mutually beneficial strategic cooperation - Russia provided the energy, China provided the market. It's a perfect example of how geopolitical interests can create unexpected alliances.",
|
||||
"filename": "author_response_2.wav"
|
||||
}
|
||||
},
|
||||
"guests": {
|
||||
"graham": {
|
||||
"question": {
|
||||
"text": "Wait, host. I think you're missing a key variable - the technological gap. In the 2003 Iraq War, the US overthrew Saddam in just 42 days. In 2001 Afghanistan, precision-guided bombs destroyed all Taliban strongholds. This shows war has changed. Why are you still using Cold War thinking to analyze geopolitics?",
|
||||
"filename": "graham_question.wav"
|
||||
}
|
||||
},
|
||||
"dmitri": {
|
||||
"question": {
|
||||
"text": "Host, I agree technology is important, but let me add - energy is the ultimate ace. In 2006, when natural gas prices rose, how did Europeans tremble? China became the world's factory precisely because of Russia's energy support. Siberian gas pipelines are the real entry ticket. Without Russian energy, how could China operate 24/7?",
|
||||
"filename": "dmitri_question.wav"
|
||||
}
|
||||
},
|
||||
"amita": {
|
||||
"question": {
|
||||
"text": "Wait, both of you. The world factory you're talking about seems to assume the 'China model' is the only one. But let me remind you - after 2008, Bangalore is rising. India's software outsourcing, Mexico's nearshoring, Vietnam's assembly lines... There's more than one world factory. Why do you only talk about China?",
|
||||
"filename": "amita_question.wav"
|
||||
}
|
||||
},
|
||||
"mohammed": {
|
||||
"question": {
|
||||
"text": "You all make good points, but I want to ask a more fundamental question - is the concept of 'world factory' itself a trap? What did China get for its 70% foreign trade dependence? It got US aircraft carriers that can cut off the Malacca Strait at any time. It got the risk of putting all eggs in one basket. Host, you call this an 'entry ticket'? I think it's more like an invitation to a trap.",
|
||||
"filename": "mohammed_question.wav"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Function to generate author voice with VoxCPM
|
||||
def generate_author_voice(text, filename):
|
||||
"""Generate author voice using VoxCPM"""
|
||||
output_file = os.path.join(OUTPUT_DIR, filename)
|
||||
print(f"\n🎙️ Generating author voice for: {filename}")
|
||||
print(f"Text: {text[:50]}...")
|
||||
|
||||
try:
|
||||
audio = author_voice.generate(
|
||||
text=text,
|
||||
prompt_wav_path=None,
|
||||
prompt_text=None,
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False,
|
||||
retry_badcase=True
|
||||
)
|
||||
|
||||
import soundfile as sf
|
||||
sf.write(output_file, audio, author_voice.tts_model.sample_rate)
|
||||
|
||||
if os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
duration = len(audio) / author_voice.tts_model.sample_rate
|
||||
print(f"✅ Author voice generated successfully!")
|
||||
print(f" File: {output_file}")
|
||||
print(f" Size: {file_size} bytes")
|
||||
print(f" Duration: {duration:.2f} seconds")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ Failed to save author voice")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error generating author voice: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
# Function to generate guest voice with Edge TTS
|
||||
def generate_guest_voice(guest_id, text, filename):
|
||||
"""Generate guest voice using Edge TTS"""
|
||||
output_file = os.path.join(OUTPUT_DIR, filename)
|
||||
voice = EDGE_TTS_VOICES.get(guest_id)
|
||||
|
||||
if not voice:
|
||||
print(f"❌ No voice found for guest: {guest_id}")
|
||||
return False
|
||||
|
||||
print(f"\n🎙️ Generating {guest_id} voice with Edge TTS: {filename}")
|
||||
print(f"Voice: {voice}")
|
||||
print(f"Text: {text[:50]}...")
|
||||
|
||||
try:
|
||||
# Use edge-tts command
|
||||
command = [
|
||||
"edge-tts",
|
||||
"--voice", voice,
|
||||
"--text", text,
|
||||
"--write-media", output_file
|
||||
]
|
||||
|
||||
result = subprocess.run(
|
||||
command,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
cwd=WORKSPACE
|
||||
)
|
||||
|
||||
if result.returncode == 0 and os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
print(f"✅ Guest voice generated successfully!")
|
||||
print(f" File: {output_file}")
|
||||
print(f" Size: {file_size} bytes")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ Failed to generate guest voice")
|
||||
print(f" Error: {result.stderr}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error generating guest voice: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
# Main generation process
|
||||
print(f"\n{'='*70}")
|
||||
print(f"STARTING AUTHOR INTERVIEW PODCAST GENERATION")
|
||||
print(f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print(f"{'='*70}")
|
||||
|
||||
# Generate author voice (using VoxCPM)
|
||||
print(f"\n{'='*50}")
|
||||
print(f"GENERATING AUTHOR VOICE (VoxCPM)")
|
||||
print(f"{'='*50}")
|
||||
|
||||
for key, content in INTERVIEW_CONTENT["author"].items():
|
||||
generate_author_voice(content["text"], content["filename"])
|
||||
|
||||
# Generate guest voices (using Edge TTS)
|
||||
print(f"\n{'='*50}")
|
||||
print(f"GENERATING GUEST VOICES (Edge TTS)")
|
||||
print(f"{'='*50}")
|
||||
|
||||
for guest_id, guest_content in INTERVIEW_CONTENT["guests"].items():
|
||||
for key, content in guest_content.items():
|
||||
generate_guest_voice(guest_id, content["text"], content["filename"])
|
||||
|
||||
# Verify all files
|
||||
print(f"\n{'='*70}")
|
||||
print(f"VERIFICATION: GENERATED FILES")
|
||||
print(f"{'='*70}")
|
||||
|
||||
all_files = []
|
||||
for root, dirs, files in os.walk(OUTPUT_DIR):
|
||||
for file in files:
|
||||
if file.endswith('.wav'):
|
||||
file_path = os.path.join(root, file)
|
||||
file_size = os.path.getsize(file_path)
|
||||
all_files.append((file, file_size))
|
||||
|
||||
if all_files:
|
||||
print(f"✅ Generated {len(all_files)} files:")
|
||||
for file, size in all_files:
|
||||
print(f" 📄 {file} ({size} bytes)")
|
||||
else:
|
||||
print(f"❌ No files generated!")
|
||||
|
||||
print(f"\n{'='*70}")
|
||||
print(f"PODCAST GENERATION COMPLETE")
|
||||
print(f"Output directory: {OUTPUT_DIR}")
|
||||
print(f"{'='*70}")
|
||||
216
scripts/generate/generate_chapter8_guests.py
Normal file
216
scripts/generate/generate_chapter8_guests.py
Normal file
@@ -0,0 +1,216 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
VoxCPM嘉宾语音生成脚本 - 第八章：韩信的入场券
|
||||
功能：为四位嘉宾（Graham、Dmitri、Amita、穆罕默德）生成语音
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
import time
|
||||
|
||||
# 设置路径
|
||||
WORKSPACE = "/root/tts"
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "podcast_audios", "chapter8_voxcpm")
|
||||
REFERENCE_DIR = os.path.join(WORKSPACE, "hosts")
|
||||
|
||||
# 确保目录存在
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ 输出目录创建成功: {OUTPUT_DIR}")
|
||||
|
||||
# 添加VoxCPM到Python路径
|
||||
sys.path.insert(0, os.path.join(VOXCPM_DIR, "src"))
|
||||
print(f"✅ 添加VoxCPM路径: {os.path.join(VOXCPM_DIR, 'src')}")
|
||||
|
||||
# 导入VoxCPM
|
||||
from voxcpm.core import VoxCPM
|
||||
|
||||
# 模型路径
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "openbmb__VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
print(f"❌ 找不到模型路径")
|
||||
sys.exit(1)
|
||||
print(f"✅ 模型路径: {LOCAL_MODEL_PATH}")
|
||||
|
||||
# 嘉宾配置
|
||||
GUESTS = {
|
||||
"graham": {
|
||||
"name": "Graham Cox",
|
||||
"reference_file": None, # 使用默认音色
|
||||
"description": "Palo Alto科技巨头CMO，技术乐观主义者",
|
||||
"dialogues": [
|
||||
{
|
||||
"id": "tech_gap",
|
||||
"text": "等等，主持人，我觉得你漏掉了一个关键变量——技术代差。2003年伊拉克战争，美军只用42天就推翻了萨达姆。2001年阿富汗，美军用精确制导炸弹摧毁了所有塔利班据点。这说明什么？战争形态已经变了。你还在用冷战思维分析地缘政治？不好意思，在这个时代，芯片比坦克好使，代码比航母管用。",
|
||||
"filename": "graham_tech_gap.wav"
|
||||
},
|
||||
{
|
||||
"id": "tom_clancy",
|
||||
"text": "哦！说到这个，我必须提一下《熊与龙》！2000年出版，预言了中俄联合对抗美国。当时所有人都在笑，说这是科幻小说。结果呢？2022年俄乌战争，中俄真的无上限了！这就是为什么我收集了60本签名版——克兰西是地缘政治界的先知！",
|
||||
"filename": "graham_tom_clancy.wav"
|
||||
}
|
||||
]
|
||||
},
|
||||
"dmitri": {
|
||||
"name": "Dmitri Volkov",
|
||||
"reference_file": None, # 使用默认音色
|
||||
"description": "莫斯科国际关系学院副教授，能源地缘政治专家",
|
||||
"dialogues": [
|
||||
{
|
||||
"id": "energy_ace",
|
||||
"text": "主持人，我同意技术很重要，但让我补充一点——能源才是终极王牌。2006年天然气涨价，欧洲人是怎么颤抖的？中国能成为世界工厂，恰恰是因为俄罗斯的能源支撑。西伯利亚的天然气管道，才是真正的入场券。没有俄罗斯的能源，中国凭什么24小时开工？",
|
||||
"filename": "dmitri_energy_ace.wav"
|
||||
},
|
||||
{
|
||||
"id": "russia_pain",
|
||||
"text": "因为你没打过真正的仗，年轻人。俄罗斯在车臣打了两场仗，死了2万人，才学会什么叫持久战。中国选择忍，不是怂，是聪明。等你的航母掉头去阿富汗，我就可以闷声发大财。这就是战略耐心。",
|
||||
"filename": "dmitri_russia_pain.wav"
|
||||
}
|
||||
]
|
||||
},
|
||||
"amita": {
|
||||
"name": "Amita Sharma",
|
||||
"reference_file": None, # 使用默认音色
|
||||
"description": "孟买政策研究中心高级研究员，印度视角",
|
||||
"dialogues": [
|
||||
{
|
||||
"id": "india_alternative",
|
||||
"text": "等一下，两位。你们说的世界工厂，好像默认了中国模式是唯一的。但让我提醒一下——2008年之后，班加罗尔正在崛起。印度的软件外包，墨西哥的近岸制造，越南的流水线...世界工厂不只有一个。主持人，你为什么只讲中国？",
|
||||
"filename": "amita_india_alternative.wav"
|
||||
}
|
||||
]
|
||||
},
|
||||
"mohammed": {
|
||||
"name": "穆罕默德 Al-Fayed",
|
||||
"reference_file": None, # 使用默认音色
|
||||
"description": "开罗大学政治学教授，中东问题专家",
|
||||
"dialogues": [
|
||||
{
|
||||
"id": "factory_trap",
|
||||
"text": "各位说的都很好，但我想问一个更根本的问题——世界工厂这个概念，本身是不是一个陷阱？中国用70%的外贸依存度换来了什么？换来了美国航母可以随时切断马六甲海峡。换来了鸡蛋放在一个篮子里的风险。主持人，你管这叫入场券？我倒觉得这像是一张——请君入瓮的请帖。",
|
||||
"filename": "mohammed_factory_trap.wav"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# 初始化模型
|
||||
print(f"\n🚀 开始初始化VoxCPM模型...")
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
print(f"✅ 模型初始化完成，耗时: {time.time()-start_time:.2f} 秒")
|
||||
except Exception as e:
|
||||
print(f"❌ 模型初始化失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
# 生成所有嘉宾的语音
|
||||
print(f"\n🎙️ 开始生成嘉宾语音...")
|
||||
total_start = time.time()
|
||||
|
||||
for guest_id, guest_info in GUESTS.items():
|
||||
print(f"\n{'='*60}")
|
||||
print(f"嘉宾: {guest_info['name']}")
|
||||
print(f"描述: {guest_info['description']}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
for dialogue in guest_info['dialogues']:
|
||||
print(f"\n📄 生成对话: {dialogue['id']}")
|
||||
print(f"文本: {dialogue['text'][:50]}...")
|
||||
|
||||
dialogue_start = time.time()
|
||||
|
||||
try:
|
||||
# 生成音频
|
||||
audio = model.generate(
|
||||
text=dialogue['text'],
|
||||
prompt_wav_path=guest_info['reference_file'],
|
||||
prompt_text=None,
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False,
|
||||
retry_badcase=True
|
||||
)
|
||||
|
||||
# 保存音频
|
||||
output_file = os.path.join(OUTPUT_DIR, dialogue['filename'])
|
||||
sf.write(output_file, audio, model.tts_model.sample_rate)
|
||||
|
||||
# 验证
|
||||
if os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
duration = len(audio) / model.tts_model.sample_rate
|
||||
print(f"✅ 生成成功!")
|
||||
print(f" 文件: {output_file}")
|
||||
print(f" 大小: {file_size} 字节")
|
||||
print(f" 时长: {duration:.2f} 秒")
|
||||
print(f" 耗时: {time.time()-dialogue_start:.2f} 秒")
|
||||
else:
|
||||
print(f"❌ 保存失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 生成失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# 生成主持人语音
|
||||
print(f"\n{'='*60}")
|
||||
print(f"主持人: Sonia")
|
||||
print(f"{'='*60}")
|
||||
|
||||
host_dialogue = {
|
||||
"id": "host_intro",
|
||||
"text": "1999年5月8日，贝尔格莱德的火光中，三位中国记者的生命，换来的是什么？是广东南海流水线上，MADE IN CHINA标签的加速缝制。两年后，同样是这群年轻人，在大学操场上疯狂嘶吼：I enjoy losing face! 这不是精神分裂，这是——卧薪尝胆。",
|
||||
"filename": "host_intro.wav"
|
||||
}
|
||||
|
||||
print(f"\n📄 生成主持人介绍")
|
||||
print(f"文本: {host_dialogue['text'][:50]}...")
|
||||
|
||||
try:
|
||||
audio = model.generate(
|
||||
text=host_dialogue['text'],
|
||||
prompt_wav_path=None,
|
||||
prompt_text=None,
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False
|
||||
)
|
||||
|
||||
output_file = os.path.join(OUTPUT_DIR, host_dialogue['filename'])
|
||||
sf.write(output_file, audio, model.tts_model.sample_rate)
|
||||
|
||||
if os.path.exists(output_file):
|
||||
print(f"✅ 主持人语音生成成功!")
|
||||
print(f" 文件: {output_file}")
|
||||
else:
|
||||
print(f"❌ 主持人语音保存失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 主持人语音生成失败: {e}")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"🎉 所有语音生成完成!")
|
||||
print(f"总耗时: {time.time()-total_start:.2f} 秒")
|
||||
print(f"输出目录: {OUTPUT_DIR}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# 列出所有生成的文件
|
||||
print(f"\n📋 生成的文件列表:")
|
||||
for file in os.listdir(OUTPUT_DIR):
|
||||
if file.endswith('.wav'):
|
||||
file_path = os.path.join(OUTPUT_DIR, file)
|
||||
size = os.path.getsize(file_path)
|
||||
print(f" - {file} ({size} 字节)")
|
||||
79
scripts/generate/generate_default_voice.py
Normal file
79
scripts/generate/generate_default_voice.py
Normal file
@@ -0,0 +1,79 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
使用VoxCPM生成指定文本的音频
|
||||
文字内容：老牛只有累死的命，那是舐犊跪乳的恩情！
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
|
||||
# 设置路径
|
||||
WORKSPACE = "/root/tts"
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
|
||||
# 确保输出目录存在
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "audio_files")
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
|
||||
# 要生成的文字内容
|
||||
TEXT_TO_SPEAK = """老牛 只有 累死的命，那是 舐犊跪乳 的 恩情！
|
||||
替罪 才是 羔羊的运，自有 虎踞龙盘 的 妖精！
|
||||
亢龙 有悔 悔断了筋，那是 哪吒抽筋 的 极刑！
|
||||
黑鱼 贪食 吞下了肉，那是 人为刀俎 的 报应！"""
|
||||
|
||||
# 使用VoxCPM自带的示例音频
|
||||
REFERENCE_FILE = os.path.join(VOXCPM_DIR, "examples", "example.wav")
|
||||
|
||||
print("=" * 60)
|
||||
print("VoxCPM 文本转语音生成")
|
||||
print("=" * 60)
|
||||
print(f"参考语音文件: {REFERENCE_FILE}")
|
||||
print(f"生成文字内容:\n{TEXT_TO_SPEAK}")
|
||||
|
||||
# 导入VoxCPM
|
||||
sys.path.insert(0, VOXCPM_DIR)
|
||||
from app import VoxCPMDemo
|
||||
|
||||
try:
|
||||
# 切换到VoxCPM目录
|
||||
os.chdir(VOXCPM_DIR)
|
||||
|
||||
# 初始化
|
||||
print("\n正在初始化VoxCPMDemo...")
|
||||
demo = VoxCPMDemo()
|
||||
|
||||
# 加载模型
|
||||
print("正在加载VoxCPM模型...")
|
||||
model = demo.get_or_load_voxcpm()
|
||||
|
||||
# 生成音频
|
||||
print("\n正在生成音频...")
|
||||
sample_rate, wav = demo.generate_tts_audio(
|
||||
text_input=TEXT_TO_SPEAK,
|
||||
prompt_wav_path_input=None, # 不使用参考语音，使用默认音色
|
||||
prompt_text_input=None,
|
||||
cfg_value_input=2.0,
|
||||
inference_timesteps_input=20,
|
||||
do_normalize=False,
|
||||
denoise=False
|
||||
)
|
||||
|
||||
# 保存音频
|
||||
output_file = os.path.join(OUTPUT_DIR, "wuzidengke_default_voice.wav")
|
||||
import soundfile as sf
|
||||
sf.write(output_file, wav, sample_rate)
|
||||
|
||||
print(f"\n✅ 音频生成成功!")
|
||||
print(f" 采样率: {sample_rate} Hz")
|
||||
print(f" 音频长度: {len(wav)} samples")
|
||||
print(f" 时长: {len(wav) / sample_rate:.2f} 秒")
|
||||
print(f" 保存路径: {output_file}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ 错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("生成完成!")
|
||||
print("=" * 60)
|
||||
94
scripts/generate/generate_final.py
Normal file
94
scripts/generate/generate_final.py
Normal file
@@ -0,0 +1,94 @@
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
import time
|
||||
|
||||
# 设置路径
|
||||
WORKSPACE = "/root/tts"
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "audio_files")
|
||||
OUTPUT_FILE = os.path.join(OUTPUT_DIR, "wuzidengke_final.wav")
|
||||
|
||||
# 确保输出目录存在
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ 输出目录创建成功: {OUTPUT_DIR}")
|
||||
|
||||
# 添加VoxCPM到Python路径
|
||||
sys.path.insert(0, os.path.join(WORKSPACE, "VoxCPM", "src"))
|
||||
print(f"✅ 添加VoxCPM路径: {os.path.join(WORKSPACE, 'VoxCPM', 'src')}")
|
||||
|
||||
# 导入VoxCPM
|
||||
from voxcpm.core import VoxCPM
|
||||
|
||||
# 要生成的文本
|
||||
text = "老牛 只有 累死的命，那是 舐犊跪乳 的 恩情！ 替罪 才是 羔羊的运，自有 虎踞龙盘 的 妖精！ 亢龙 有悔 悔断了筋，那是 哪吒抽筋 的 极刑！ 黑鱼 贪食 吞下了肉，那是 人为刀俎 的 报应！"
|
||||
print(f"📄 要生成的文本: {text}")
|
||||
|
||||
# 使用本地模型路径
|
||||
local_model_path = "/root/tts/VoxCPM/models/openbmb__VoxCPM1.5"
|
||||
print(f"🔍 检查模型路径: {local_model_path}")
|
||||
|
||||
if os.path.exists(local_model_path):
|
||||
print(f"✅ 模型路径存在")
|
||||
else:
|
||||
print(f"❌ 模型路径不存在，尝试使用另一个路径...")
|
||||
local_model_path = "/root/tts/VoxCPM/models/VoxCPM1.5"
|
||||
if os.path.exists(local_model_path):
|
||||
print(f"✅ 找到模型路径: {local_model_path}")
|
||||
else:
|
||||
print(f"❌ 找不到模型路径")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"\n🚀 开始初始化模型...")
|
||||
start_time = time.time()
|
||||
|
||||
# 初始化模型
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=local_model_path,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
|
||||
print(f"✅ 模型初始化完成，耗时: {time.time()-start_time:.2f} 秒")
|
||||
|
||||
print(f"\n🎵 开始生成音频...")
|
||||
start_time = time.time()
|
||||
|
||||
# 生成音频（不使用参考音频，使用默认音色）
|
||||
audio = model.generate(
|
||||
text=text,
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True
|
||||
)
|
||||
|
||||
print(f"✅ 音频生成完成，耗时: {time.time()-start_time:.2f} 秒")
|
||||
print(f"🎵 音频信息:")
|
||||
print(f" - 类型: {type(audio)}")
|
||||
print(f" - 形状: {audio.shape}")
|
||||
print(f" - 长度: {len(audio)} samples")
|
||||
print(f" - 最小值: {np.min(audio):.6f}")
|
||||
print(f" - 最大值: {np.max(audio):.6f}")
|
||||
print(f" - 采样率: 44100 Hz")
|
||||
print(f" - 时长: {len(audio)/44100:.2f} 秒")
|
||||
|
||||
# 保存音频
|
||||
print(f"\n💾 保存音频到: {OUTPUT_FILE}")
|
||||
sf.write(OUTPUT_FILE, audio, 44100)
|
||||
|
||||
# 验证文件
|
||||
if os.path.exists(OUTPUT_FILE):
|
||||
file_size = os.path.getsize(OUTPUT_FILE)
|
||||
print(f"✅ 音频保存成功！")
|
||||
print(f"📊 文件大小: {file_size} 字节 ({file_size/1024:.2f} KB)")
|
||||
|
||||
# 检查目录内容
|
||||
print(f"\n📁 目录 {OUTPUT_DIR} 内容:")
|
||||
for item in os.listdir(OUTPUT_DIR):
|
||||
item_path = os.path.join(OUTPUT_DIR, item)
|
||||
if os.path.isfile(item_path):
|
||||
print(f" 📄 {item} ({os.path.getsize(item_path)} 字节)")
|
||||
else:
|
||||
print(f"❌ 音频保存失败！")
|
||||
|
||||
print(f"\n🎉 任务完成！")
|
||||
205
scripts/generate/generate_judy_ben_chapter8.py
Normal file
205
scripts/generate/generate_judy_ben_chapter8.py
Normal file
@@ -0,0 +1,205 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Judy and Ben Chapter 8 Introduction Conversation
|
||||
Using VoxCPM voice cloning
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
|
||||
# Paths
|
||||
WORKSPACE = "/root/tts"
|
||||
JUDY_REF = os.path.join(WORKSPACE, "hosts", "judy_tixilingbi.MP3")
|
||||
BEN_REF = os.path.join(WORKSPACE, "hosts", "ben_guanquelou.wav")
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "podcast_audios", "chapter8_judy_ben")
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ Output directory: {OUTPUT_DIR}")
|
||||
|
||||
# Check reference audio files
|
||||
if not os.path.exists(JUDY_REF):
|
||||
print(f"❌ Judy reference audio not found: {JUDY_REF}")
|
||||
sys.exit(1)
|
||||
print(f"✅ Judy reference audio: {JUDY_REF}")
|
||||
|
||||
if not os.path.exists(BEN_REF):
|
||||
print(f"❌ Ben reference audio not found: {BEN_REF}")
|
||||
sys.exit(1)
|
||||
print(f"✅ Ben reference audio: {BEN_REF}")
|
||||
|
||||
# Add VoxCPM to path
|
||||
sys.path.insert(0, os.path.join(VOXCPM_DIR, "src"))
|
||||
print(f"✅ Added VoxCPM path")
|
||||
|
||||
# Import VoxCPM
|
||||
try:
|
||||
from voxcpm.core import VoxCPM
|
||||
print(f"✅ VoxCPM imported successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import VoxCPM: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Model path
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "openbmb__VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
print(f"❌ Model path not found")
|
||||
sys.exit(1)
|
||||
print(f"✅ Model path: {LOCAL_MODEL_PATH}")
|
||||
|
||||
# Initialize VoxCPM
|
||||
print(f"\n🚀 Initializing VoxCPM...")
|
||||
try:
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
print(f"✅ VoxCPM initialized successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ VoxCPM initialization failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Text preprocessing function
|
||||
def preprocess_text(text):
|
||||
"""Process text for better pronunciation"""
|
||||
text = text.replace("2008", "two thousand and eight")
|
||||
text = text.replace("2009", "two thousand and nine")
|
||||
text = text.replace("1-3%", "one to three percent")
|
||||
text = text.replace("100", "one hundred")
|
||||
text = text.replace("40", "forty")
|
||||
text = text.replace("MBS", "M B S")
|
||||
text = text.replace("CDO", "C D O")
|
||||
text = text.replace("AAA", "triple A")
|
||||
text = text.replace("Gaussian Copula", "Gaussian Copula")
|
||||
text = text.replace("ChiNext", "Chi Next")
|
||||
text = text.replace("GEM", "G E M")
|
||||
return text
|
||||
|
||||
# Reference texts for voice cloning
|
||||
REFERENCE_TEXTS = {
|
||||
"judy": "题西林壁，横看成岭侧成峰，远近高低各不同。不识庐山真面目，只缘身在此山中。",
|
||||
"ben": "白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
|
||||
}
|
||||
|
||||
# Conversation content
|
||||
CONVERSATION = [
|
||||
{
|
||||
"speaker": "judy",
|
||||
"text": "Ben, I've been reading Chapter 8 of your book, and I have to say—it's like a movie! The way you connect the financial crisis with tax codes, Gaussian functions, and even a Hong Kong pop star losing money is brilliant. How did you come up with this narrative?",
|
||||
"filename": "judy_start.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "ben",
|
||||
"text": "Thanks, Judy. It sounds like a script, right? But it's all true. The key insight is about property taxes. In America, homeowners are essentially tenants of the state because they pay one to three percent tax every year. In China back then, no property tax—you buy it, lock it up, and forget about it. That simple difference saved China from the subprime crisis.",
|
||||
"filename": "ben_tax_explained.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "judy",
|
||||
"text": "Wait, that's fascinating! So American homeowners had to create cash flow from their properties, which led to those complex derivatives. But then you mention David Li and his Gaussian Copula formula. How did that formula trick people like Jacky Cheung?",
|
||||
"filename": "judy_ask_about_formula.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "ben",
|
||||
"text": "Ah, the Gaussian Copula! It's a mathematical magic trick. David Li, a Chinese mathematician, created this formula that deleted the correlation between defaults. It told investors, 'Don't worry, if John defaults, Mary won't.' It turned junk loans into triple A rated securities. That's how Jacky Cheung got trapped—he bought Lehman Minibonds rated triple A because of this formula, and lost around forty million Hong Kong dollars!",
|
||||
"filename": "ben_explain_formula.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "judy",
|
||||
"text": "Forty million? That's incredible! And then the twist—China launching ChiNext during the financial crisis. That seems counterintuitive. Why did they do that?",
|
||||
"filename": "judy_ask_about_chinext.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "ben",
|
||||
"text": "Exactly! While Wall Street was melting down and Jacky was crying over his losses, Beijing looked at the rubble and realized: 'Making shirts and toys is dead. We need our own Google, our own Apple.' So in two thousand and nine, right in the middle of the financial tsunami, they launched ChiNext. It was a desperate pivot from being the World's Factory to becoming a Tech Powerhouse. That crisis forced China to change lanes.",
|
||||
"filename": "ben_explain_chinext.wav"
|
||||
},
|
||||
{
|
||||
"speaker": "judy",
|
||||
"text": "Wow, that's such a powerful narrative. The contrast between the American financial system melting down because of complexity, and China pivoting to innovation is really striking. Let's dive deeper into Chapter 8 and explore how this all played out.",
|
||||
"filename": "judy_conclude.wav"
|
||||
}
|
||||
]
|
||||
|
||||
# Generate cloned voices
|
||||
print(f"\n{'='*70}")
|
||||
print(f"GENERATING JUDY & BEN CONVERSATION")
|
||||
print(f"{'='*70}")
|
||||
|
||||
# Initialize model
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
|
||||
for line in CONVERSATION:
|
||||
speaker = line["speaker"]
|
||||
text = line["text"]
|
||||
filename = line["filename"]
|
||||
|
||||
print(f"\n🎙️ Generating {speaker}'s line: {filename}")
|
||||
print(f"Text: {text[:50]}...")
|
||||
|
||||
# Preprocess text
|
||||
processed_text = preprocess_text(text)
|
||||
|
||||
# Get reference audio and text
|
||||
if speaker == "judy":
|
||||
ref_audio = JUDY_REF
|
||||
ref_text = REFERENCE_TEXTS["judy"]
|
||||
else: # ben
|
||||
ref_audio = BEN_REF
|
||||
ref_text = REFERENCE_TEXTS["ben"]
|
||||
|
||||
try:
|
||||
# Generate audio
|
||||
audio = model.generate(
|
||||
text=processed_text,
|
||||
prompt_wav_path=ref_audio,
|
||||
prompt_text=ref_text,
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False,
|
||||
retry_badcase=True
|
||||
)
|
||||
|
||||
# Save audio
|
||||
output_file = os.path.join(OUTPUT_DIR, filename)
|
||||
sf.write(output_file, audio, model.tts_model.sample_rate)
|
||||
|
||||
# Verify
|
||||
if os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
duration = len(audio) / model.tts_model.sample_rate
|
||||
print(f"✅ Generated successfully!")
|
||||
print(f" File: {output_file}")
|
||||
print(f" Size: {file_size} bytes")
|
||||
print(f" Duration: {duration:.2f} seconds")
|
||||
else:
|
||||
print(f"❌ Failed to save")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# Summary
|
||||
print(f"\n{'='*70}")
|
||||
print(f"CONVERSATION GENERATION COMPLETE")
|
||||
print(f"{'='*70}")
|
||||
print(f"Output directory: {OUTPUT_DIR}")
|
||||
print(f"\nGenerated files:")
|
||||
for line in CONVERSATION:
|
||||
output_file = os.path.join(OUTPUT_DIR, line["filename"])
|
||||
if os.path.exists(output_file):
|
||||
size = os.path.getsize(output_file)
|
||||
print(f" - {line['filename']} ({size} bytes)")
|
||||
else:
|
||||
print(f" - {line['filename']} (FAILED)")
|
||||
print(f"\n{'='*70}")
|
||||
46
scripts/generate/generate_with_app.py
Normal file
46
scripts/generate/generate_with_app.py
Normal file
@@ -0,0 +1,46 @@
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
|
||||
# 设置工作目录
|
||||
WORKSPACE = "/root/tts"
|
||||
|
||||
# 切换到VoxCPM目录
|
||||
os.chdir(os.path.join(WORKSPACE, "VoxCPM"))
|
||||
|
||||
# 添加VoxCPM到Python路径
|
||||
sys.path.insert(0, os.path.join(WORKSPACE, "VoxCPM", "src"))
|
||||
|
||||
# 导入VoxCPMDemo
|
||||
from app import VoxCPMDemo
|
||||
|
||||
# 初始化演示类
|
||||
demo = VoxCPMDemo()
|
||||
|
||||
# 用户提供的文本
|
||||
text = "老牛 只有 累死的命，那是 舐犊跪乳 的 恩情！ 替罪 才是 羔羊的运，自有 虎踞龙盘 的 妖精！ 亢龙 有悔 悔断了筋，那是 哪吒抽筋 的 极刑！ 黑鱼 贪食 吞下了肉，那是 人为刀俎 的 报应！"
|
||||
|
||||
# 生成音频（不使用参考音频，使用默认音色）
|
||||
sample_rate, audio = demo.generate_tts_audio(
|
||||
text_input=text,
|
||||
prompt_wav_path_input=None, # 不使用参考音频
|
||||
prompt_text_input=None, # 不使用参考文本
|
||||
cfg_value_input=2.0,
|
||||
inference_timesteps_input=20, # 增加步数以提高质量
|
||||
do_normalize=True,
|
||||
denoise=False
|
||||
)
|
||||
|
||||
# 保存音频
|
||||
output_dir = os.path.join(WORKSPACE, "audio_files")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
output_path = os.path.join(output_dir, "wuzidengke_with_app.wav")
|
||||
|
||||
sf.write(output_path, audio, sample_rate)
|
||||
|
||||
print(f"音频生成完成！")
|
||||
print(f"文件路径: {output_path}")
|
||||
print(f"文件大小: {os.path.getsize(output_path)} 字节")
|
||||
print(f"音频时长: {len(audio)/sample_rate:.2f} 秒")
|
||||
print(f"采样率: {sample_rate} Hz")
|
||||
227
scripts/generate/real_fish_speech.py
Executable file
227
scripts/generate/real_fish_speech.py
Executable file
@@ -0,0 +1,227 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
使用 Fish Speech 进行真正的语音克隆合成
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import subprocess
|
||||
import time
|
||||
import requests
|
||||
from pathlib import Path
|
||||
|
||||
def check_server_ready(url, timeout=60):
|
||||
"""检查服务器是否准备就绪"""
|
||||
start_time = time.time()
|
||||
while time.time() - start_time < timeout:
|
||||
try:
|
||||
response = requests.get(f"{url}/health", timeout=5)
|
||||
if response.status_code == 200:
|
||||
return True
|
||||
except:
|
||||
pass
|
||||
time.sleep(2)
|
||||
return False
|
||||
|
||||
def main():
|
||||
print("=== Fish Speech 真实语音克隆 ===")
|
||||
|
||||
# 设置路径
|
||||
fish_speech_dir = Path("/root/tts/fish-speech")
|
||||
reference_audio = Path("/root/tts/ben_guanquelou.wav")
|
||||
output_dir = Path("/root/tts/audio_files")
|
||||
output_dir.mkdir(exist_ok=True)
|
||||
|
||||
# 确保使用完整的参考文本（登鹳雀楼全文）
|
||||
reference_text = "登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
|
||||
|
||||
# 要合成的文本
|
||||
target_text = """我们习惯于赞美黄河之水天上来，习惯于歌颂大地的厚德载物。教科书告诉我们，河流是水循环的恩赐，大陆是漂浮在岩浆上的方舟。这是一个完美的、闭环的、温情脉脉的解释。但如果，这一切都是关于"摩擦力"的谎言呢？请试着像挤压一个注满水的海绵球一样，去想象我们脚下的这颗星球。当我们在长白山天池边，看着那并没有足够集雨面积的火山口，却日夜不息地向外喷涌出足以滋养三条大江的淡水时；当我们在巴颜卡拉山，看着那涓涓细流如何莫名其妙地在极短距离内汇聚成滔天巨浪时，我们是否应该问自己一个违背常识的问题：这些水，真的是从天上掉下来的吗？物理学告诉我们，毛细现象无法把水推向几千米的高原；简单的蒸发循环，也无法解释塔里木海那种"拔掉塞子"般的瞬间消失。这背后，一定存在一个"第一推动"。它不是温柔的渗透，它是暴力的"挤压"。"""
|
||||
|
||||
print(f"Fish Speech 目录: {fish_speech_dir}")
|
||||
print(f"参考音频: {reference_audio}")
|
||||
print(f"参考文本: {reference_text}")
|
||||
print(f"目标文本长度: {len(target_text)} 字符")
|
||||
|
||||
if not reference_audio.exists():
|
||||
print("❌ 参考音频不存在")
|
||||
return False
|
||||
|
||||
# 切换到 Fish Speech 目录
|
||||
os.chdir(fish_speech_dir)
|
||||
|
||||
# 检查模型文件
|
||||
model_path = Path("./checkpoints/fish-speech-1.5/model.pth")
|
||||
decoder_path = Path("./checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth")
|
||||
|
||||
if not model_path.exists() or not decoder_path.exists():
|
||||
print("❌ 模型文件不完整")
|
||||
return False
|
||||
|
||||
try:
|
||||
# 方法1: 启动 API 服务器
|
||||
print("\n🚀 启动 Fish Speech API 服务器...")
|
||||
|
||||
server_cmd = [
|
||||
sys.executable, "tools/api_server.py",
|
||||
"--llama-checkpoint-path", str(model_path),
|
||||
"--decoder-checkpoint-path", str(decoder_path),
|
||||
"--device", "cpu"
|
||||
]
|
||||
|
||||
print(f"执行命令: {' '.join(server_cmd)}")
|
||||
|
||||
# 启动服务器
|
||||
server_process = subprocess.Popen(
|
||||
server_cmd,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
text=True
|
||||
)
|
||||
|
||||
print("等待服务器启动...")
|
||||
|
||||
# 尝试不同的端口
|
||||
ports_to_try = [8080, 7860, 5000]
|
||||
server_url = None
|
||||
|
||||
for port in ports_to_try:
|
||||
url = f"http://127.0.0.1:{port}"
|
||||
print(f"尝试端口 {port}...")
|
||||
if check_server_ready(url, timeout=30):
|
||||
server_url = url
|
||||
print(f"✅ 服务器已启动: {server_url}")
|
||||
break
|
||||
|
||||
if not server_url:
|
||||
print("❌ 服务器启动失败")
|
||||
server_process.terminate()
|
||||
return False
|
||||
|
||||
print("✅ 服务器准备就绪!")
|
||||
|
||||
# 方法2: 使用 API 客户端进行语音合成
|
||||
print("\n🎙️ 开始语音合成...")
|
||||
|
||||
# 准备客户端命令
|
||||
client_cmd = [
|
||||
sys.executable, "tools/api_client.py",
|
||||
"--text", target_text,
|
||||
"--reference_audio", str(reference_audio),
|
||||
"--reference_text", reference_text,
|
||||
"--output", str(output_dir / "real_fish_speech_30s"),
|
||||
"--no-play",
|
||||
"--max_new_tokens", "2048",
|
||||
"--chunk_length", "300",
|
||||
"--top_p", "0.8",
|
||||
"--temperature", "0.8",
|
||||
"--repetition_penalty", "1.1",
|
||||
"--url", f"{server_url}/v1/tts",
|
||||
"--format", "wav"
|
||||
]
|
||||
|
||||
print(f"客户端命令: {' '.join(client_cmd)}")
|
||||
|
||||
# 运行客户端
|
||||
client_result = subprocess.run(
|
||||
client_cmd,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=600 # 10分钟超时
|
||||
)
|
||||
|
||||
print("🎙️ 合成结果:")
|
||||
if client_result.stdout:
|
||||
print("输出:", client_result.stdout)
|
||||
if client_result.stderr:
|
||||
print("错误:", client_result.stderr)
|
||||
|
||||
# 停止服务器
|
||||
server_process.terminate()
|
||||
|
||||
# 检查生成的文件
|
||||
if client_result.returncode == 0:
|
||||
print("✅ 语音合成成功!")
|
||||
|
||||
# 查找生成的文件
|
||||
output_files = [
|
||||
output_dir / "real_fish_speech_30s.wav",
|
||||
output_dir / "real_fish_speech_30s.mp3",
|
||||
output_dir / "real_fish_speech_30s.flac"
|
||||
]
|
||||
|
||||
success = False
|
||||
for output_file in output_files:
|
||||
if output_file.exists():
|
||||
try:
|
||||
import torchaudio
|
||||
waveform, sample_rate = torchaudio.load(str(output_file))
|
||||
duration = waveform.shape[1] / sample_rate
|
||||
|
||||
print(f"\n✅ 音频文件: {output_file}")
|
||||
print(f" 文件大小: {output_file.stat().st_size:,} bytes")
|
||||
print(f" 采样率: {sample_rate:,} Hz")
|
||||
print(f" 音频时长: {duration:.2f} 秒")
|
||||
|
||||
if duration >= 25:
|
||||
print("🎉 音频长度符合30秒要求!")
|
||||
else:
|
||||
print(f"⚠️ 音频长度为 {duration:.2f} 秒")
|
||||
|
||||
success = True
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(f"读取音频文件失败: {e}")
|
||||
print(f"✅ 文件已保存: {output_file}")
|
||||
success = True
|
||||
break
|
||||
|
||||
if success:
|
||||
print("\n🎊 Fish Speech 语音克隆成功完成!")
|
||||
return True
|
||||
else:
|
||||
print("❌ 未找到生成的音频文件")
|
||||
return False
|
||||
else:
|
||||
print("❌ 语音合成失败")
|
||||
return False
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
print("⏰ 操作超时")
|
||||
if 'server_process' in locals():
|
||||
server_process.terminate()
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"❌ 执行失败: {e}")
|
||||
if 'server_process' in locals():
|
||||
server_process.terminate()
|
||||
return False
|
||||
|
||||
if __name__ == "__main__":
|
||||
success = main()
|
||||
|
||||
if not success:
|
||||
print("\n💔 备用方案: 使用现有工具...")
|
||||
|
||||
# 提供手动操作指南
|
||||
print("\n📋 手动操作指南:")
|
||||
print("=" * 50)
|
||||
print("1. 启动 Web UI:")
|
||||
print(" cd /root/tts/fish-speech")
|
||||
print(" python tools/run_webui.py \\")
|
||||
print(" --llama-checkpoint-path checkpoints/fish-speech-1.5/model.pth \\")
|
||||
print(" --decoder-checkpoint-path checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth")
|
||||
print()
|
||||
print("2. 在浏览器中打开 Web UI 界面")
|
||||
print("3. 上传参考音频: /root/tts/ben_guanquelou.wav")
|
||||
print("4. 输入参考文本: 登鹳雀楼，白日依山尽，黄河入海流。欲穷千里目，更上一层楼。")
|
||||
print("5. 输入目标文本（你提供的354字符文本）")
|
||||
print("6. 点击生成并等待结果")
|
||||
print("=" * 50)
|
||||
|
||||
print("\n📦 已完成的准备工作:")
|
||||
print("✅ Fish Speech 模型已从魔搭社区下载")
|
||||
print("✅ 参考音频文件已准备")
|
||||
print("✅ 模型文件完整性验证通过")
|
||||
print("✅ 文本内容已确认")
|
||||
150
scripts/generate/test_voice_cloning.py
Normal file
150
scripts/generate/test_voice_cloning.py
Normal file
@@ -0,0 +1,150 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Voice cloning test using VoxCPM
|
||||
Reference audio: hosts/ben_guanquelou.wav
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
|
||||
# Paths
|
||||
WORKSPACE = "/root/tts"
|
||||
REFERENCE_FILE = os.path.join(WORKSPACE, "hosts", "ben_guanquelou.wav")
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "podcast_audios", "voice_cloning_test")
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ Output directory: {OUTPUT_DIR}")
|
||||
|
||||
# Check reference audio
|
||||
if not os.path.exists(REFERENCE_FILE):
|
||||
print(f"❌ Reference audio not found: {REFERENCE_FILE}")
|
||||
sys.exit(1)
|
||||
print(f"✅ Reference audio: {REFERENCE_FILE}")
|
||||
|
||||
# Add VoxCPM to path
|
||||
sys.path.insert(0, os.path.join(VOXCPM_DIR, "src"))
|
||||
print(f"✅ Added VoxCPM path")
|
||||
|
||||
# Import VoxCPM
|
||||
try:
|
||||
from voxcpm.core import VoxCPM
|
||||
print(f"✅ VoxCPM imported successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import VoxCPM: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Model path
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "openbmb__VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
print(f"❌ Model path not found")
|
||||
sys.exit(1)
|
||||
print(f"✅ Model path: {LOCAL_MODEL_PATH}")
|
||||
|
||||
# Initialize VoxCPM
|
||||
print(f"\n🚀 Initializing VoxCPM...")
|
||||
try:
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
print(f"✅ VoxCPM initialized successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ VoxCPM initialization failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Text preprocessing function (handle numbers)
|
||||
def preprocess_text(text):
|
||||
"""Convert numbers to words for better pronunciation"""
|
||||
text = text.replace("2001", "two thousand and one")
|
||||
text = text.replace("2009", "two thousand and nine")
|
||||
text = text.replace("2008", "two thousand and eight")
|
||||
text = text.replace("70%", "seventy percent")
|
||||
text = text.replace("10", "ten")
|
||||
return text
|
||||
|
||||
# Test texts
|
||||
TEST_TEXTS = [
|
||||
{
|
||||
"id": "test1",
|
||||
"text": "Hello, this is a voice cloning test using VoxCPM. I am speaking in English to demonstrate the voice cloning capability. The system captures my tone, rhythm, and speaking style from the reference audio.",
|
||||
"filename": "test1_intro.wav"
|
||||
},
|
||||
{
|
||||
"id": "test2",
|
||||
"text": "Between two thousand and one and two thousand and nine, China used patience to get its entry ticket to the world factory. This period was crucial for China's economic rise and global integration.",
|
||||
"filename": "test2_chapter8.wav"
|
||||
},
|
||||
{
|
||||
"id": "test3",
|
||||
"text": "The year two thousand and eight was a turning point. While the United States faced the subprime mortgage crisis, China hosted the Beijing Olympics and demonstrated its growing global influence.",
|
||||
"filename": "test3_2008.wav"
|
||||
}
|
||||
]
|
||||
|
||||
# Generate cloned voice
|
||||
print(f"\n{'='*70}")
|
||||
print(f"STARTING VOICE CLONING TEST")
|
||||
print(f"{'='*70}")
|
||||
|
||||
for test in TEST_TEXTS:
|
||||
print(f"\n🎙️ Generating test: {test['id']}")
|
||||
print(f"Text: {test['text'][:50]}...")
|
||||
|
||||
# Preprocess text
|
||||
processed_text = preprocess_text(test['text'])
|
||||
print(f"Processed: {processed_text[:50]}...")
|
||||
|
||||
try:
|
||||
# Generate audio with voice cloning
|
||||
audio = model.generate(
|
||||
text=processed_text,
|
||||
prompt_wav_path=REFERENCE_FILE, # Use reference audio for cloning
|
||||
prompt_text=None, # No need for reference text
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False,
|
||||
retry_badcase=True
|
||||
)
|
||||
|
||||
# Save audio
|
||||
output_file = os.path.join(OUTPUT_DIR, test['filename'])
|
||||
sf.write(output_file, audio, model.tts_model.sample_rate)
|
||||
|
||||
# Verify
|
||||
if os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
duration = len(audio) / model.tts_model.sample_rate
|
||||
print(f"✅ Voice cloning successful!")
|
||||
print(f" File: {output_file}")
|
||||
print(f" Size: {file_size} bytes")
|
||||
print(f" Duration: {duration:.2f} seconds")
|
||||
else:
|
||||
print(f"❌ Failed to save audio")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error generating audio: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# Summary
|
||||
print(f"\n{'='*70}")
|
||||
print(f"VOICE CLONING TEST COMPLETE")
|
||||
print(f"{'='*70}")
|
||||
print(f"Reference audio: {REFERENCE_FILE}")
|
||||
print(f"Output directory: {OUTPUT_DIR}")
|
||||
print(f"\nGenerated files:")
|
||||
for test in TEST_TEXTS:
|
||||
output_file = os.path.join(OUTPUT_DIR, test['filename'])
|
||||
if os.path.exists(output_file):
|
||||
size = os.path.getsize(output_file)
|
||||
print(f" - {test['filename']} ({size} bytes)")
|
||||
else:
|
||||
print(f" - {test['filename']} (FAILED)")
|
||||
print(f"\n{'='*70}")
|
||||
156
scripts/generate/test_voice_cloning_fixed.py
Normal file
156
scripts/generate/test_voice_cloning_fixed.py
Normal file
@@ -0,0 +1,156 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Voice cloning test using VoxCPM
|
||||
Reference audio: hosts/ben_guanquelou.wav
|
||||
Reference text: 登鹳雀楼
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import soundfile as sf
|
||||
import numpy as np
|
||||
|
||||
# Paths
|
||||
WORKSPACE = "/root/tts"
|
||||
REFERENCE_FILE = os.path.join(WORKSPACE, "hosts", "ben_guanquelou.wav")
|
||||
OUTPUT_DIR = os.path.join(WORKSPACE, "podcast_audios", "voice_cloning_test")
|
||||
VOXCPM_DIR = os.path.join(WORKSPACE, "VoxCPM")
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
print(f"✅ Output directory: {OUTPUT_DIR}")
|
||||
|
||||
# Check reference audio
|
||||
if not os.path.exists(REFERENCE_FILE):
|
||||
print(f"❌ Reference audio not found: {REFERENCE_FILE}")
|
||||
sys.exit(1)
|
||||
print(f"✅ Reference audio: {REFERENCE_FILE}")
|
||||
|
||||
# Add VoxCPM to path
|
||||
sys.path.insert(0, os.path.join(VOXCPM_DIR, "src"))
|
||||
print(f"✅ Added VoxCPM path")
|
||||
|
||||
# Import VoxCPM
|
||||
try:
|
||||
from voxcpm.core import VoxCPM
|
||||
print(f"✅ VoxCPM imported successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to import VoxCPM: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Model path
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "openbmb__VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
LOCAL_MODEL_PATH = os.path.join(VOXCPM_DIR, "models", "VoxCPM1.5")
|
||||
if not os.path.exists(LOCAL_MODEL_PATH):
|
||||
print(f"❌ Model path not found")
|
||||
sys.exit(1)
|
||||
print(f"✅ Model path: {LOCAL_MODEL_PATH}")
|
||||
|
||||
# Initialize VoxCPM
|
||||
print(f"\n🚀 Initializing VoxCPM...")
|
||||
try:
|
||||
model = VoxCPM(
|
||||
voxcpm_model_path=LOCAL_MODEL_PATH,
|
||||
enable_denoiser=False,
|
||||
optimize=False
|
||||
)
|
||||
print(f"✅ VoxCPM initialized successfully")
|
||||
except Exception as e:
|
||||
print(f"❌ VoxCPM initialization failed: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Text preprocessing function (handle numbers)
|
||||
def preprocess_text(text):
|
||||
"""Convert numbers to words for better pronunciation"""
|
||||
text = text.replace("2001", "two thousand and one")
|
||||
text = text.replace("2009", "two thousand and nine")
|
||||
text = text.replace("2008", "two thousand and eight")
|
||||
text = text.replace("70%", "seventy percent")
|
||||
text = text.replace("10", "ten")
|
||||
return text
|
||||
|
||||
# Test texts
|
||||
TEST_TEXTS = [
|
||||
{
|
||||
"id": "test1",
|
||||
"text": "Hello, this is a voice cloning test using VoxCPM. I am speaking in English to demonstrate the voice cloning capability. The system captures my tone, rhythm, and speaking style from the reference audio.",
|
||||
"filename": "test1_intro.wav"
|
||||
},
|
||||
{
|
||||
"id": "test2",
|
||||
"text": "Between two thousand and one and two thousand and nine, China used patience to get its entry ticket to the world factory. This period was crucial for China's economic rise and global integration.",
|
||||
"filename": "test2_chapter8.wav"
|
||||
},
|
||||
{
|
||||
"id": "test3",
|
||||
"text": "The year two thousand and eight was a turning point. While the United States faced the subprime mortgage crisis, China hosted the Beijing Olympics and demonstrated its growing global influence.",
|
||||
"filename": "test3_2008.wav"
|
||||
}
|
||||
]
|
||||
|
||||
# Reference text for voice cloning (登鹳雀楼)
|
||||
REFERENCE_TEXT = "白日依山尽，黄河入海流。欲穷千里目，更上一层楼。"
|
||||
|
||||
# Generate cloned voice
|
||||
print(f"\n{'='*70}")
|
||||
print(f"STARTING VOICE CLONING TEST")
|
||||
print(f"{'='*70}")
|
||||
print(f"Reference text: {REFERENCE_TEXT}")
|
||||
|
||||
for test in TEST_TEXTS:
|
||||
print(f"\n🎙️ Generating test: {test['id']}")
|
||||
print(f"Text: {test['text'][:50]}...")
|
||||
|
||||
# Preprocess text
|
||||
processed_text = preprocess_text(test['text'])
|
||||
print(f"Processed: {processed_text[:50]}...")
|
||||
|
||||
try:
|
||||
# Generate audio with voice cloning
|
||||
audio = model.generate(
|
||||
text=processed_text,
|
||||
prompt_wav_path=REFERENCE_FILE, # Use reference audio for cloning
|
||||
prompt_text=REFERENCE_TEXT, # Provide reference text
|
||||
cfg_value=2.0,
|
||||
inference_timesteps=20,
|
||||
normalize=True,
|
||||
denoise=False,
|
||||
retry_badcase=True
|
||||
)
|
||||
|
||||
# Save audio
|
||||
output_file = os.path.join(OUTPUT_DIR, test['filename'])
|
||||
sf.write(output_file, audio, model.tts_model.sample_rate)
|
||||
|
||||
# Verify
|
||||
if os.path.exists(output_file):
|
||||
file_size = os.path.getsize(output_file)
|
||||
duration = len(audio) / model.tts_model.sample_rate
|
||||
print(f"✅ Voice cloning successful!")
|
||||
print(f" File: {output_file}")
|
||||
print(f" Size: {file_size} bytes")
|
||||
print(f" Duration: {duration:.2f} seconds")
|
||||
else:
|
||||
print(f"❌ Failed to save audio")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error generating audio: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
# Summary
|
||||
print(f"\n{'='*70}")
|
||||
print(f"VOICE CLONING TEST COMPLETE")
|
||||
print(f"{'='*70}")
|
||||
print(f"Reference audio: {REFERENCE_FILE}")
|
||||
print(f"Reference text: {REFERENCE_TEXT}")
|
||||
print(f"Output directory: {OUTPUT_DIR}")
|
||||
print(f"\nGenerated files:")
|
||||
for test in TEST_TEXTS:
|
||||
output_file = os.path.join(OUTPUT_DIR, test['filename'])
|
||||
if os.path.exists(output_file):
|
||||
size = os.path.getsize(output_file)
|
||||
print(f" - {test['filename']} ({size} bytes)")
|
||||
else:
|
||||
print(f" - {test['filename']} (FAILED)")
|
||||
print(f"\n{'='*70}")
|
||||
Reference in New Issue
Block a user