Initial commit
This commit is contained in:
commit
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import torch
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print("Torch 是否使用 GPU:", torch.cuda.is_available())
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print("当前使用的 GPU:", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
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import torch
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from TTS.tts.models.xtts import XttsAudioConfig # 允许 XttsAudioConfig
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from TTS.tts.configs.xtts_config import XttsConfig # 允许 XttsConfig
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from TTS.config.shared_configs import BaseDatasetConfig # 允许 BaseDatasetConfig
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from TTS.tts.models.xtts import XttsArgs # 允许 XttsArgs
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# **添加 `XttsConfig`、`XttsAudioConfig`、`BaseDatasetConfig` 和 `XttsArgs` 到 PyTorch 安全全局对象**
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torch.serialization.add_safe_globals([XttsConfig, XttsAudioConfig, BaseDatasetConfig, XttsArgs])
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# **强制 `weights_only=False`**
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def load_fsspec_fixed(*args, **kwargs):
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kwargs["weights_only"] = False # 关键修正
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return torch.load(*args, **kwargs)
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# **覆盖 Coqui TTS 的 `load_fsspec` 方法**
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import TTS.utils.io
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TTS.utils.io.load_fsspec = load_fsspec_fixed
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from TTS.api import TTS
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import os
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from pydub import AudioSegment # 用于合并音频
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# 选择 XTTS v2 多语言模型
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MODEL_NAME = "tts_models/multilingual/multi-dataset/xtts_v2"
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# 加载 TTS 模型
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print("正在加载 TTS 预训练模型，请稍候...")
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tts = TTS(MODEL_NAME)
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# **指定参考音频**
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speaker_wav_zh = "example_speaker_zh.wav" # 你必须提供这个音频
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speaker_wav_en = "example_speaker_en.wav" # 你必须提供这个音频
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# **确保 `speaker_wav` 存在**
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if not os.path.exists(speaker_wav_zh):
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raise FileNotFoundError(f"错误：找不到 {speaker_wav_zh}，XTTS v2 需要一个参考音频！")
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# **拆分文本**
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text_cn1 = "你好，欢迎使用"
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text_en = "Coqui TTS"
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text_cn2 = "进行中文语音合成！"
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test_all = "Not long ago, my colleague Carlos Fenollosa made a bold claim in his book ‘La singularidad’: ChatGPT is AGI. My initial reaction was immediate dismissal. But knowing his careful reasoning, I kept reading. As I followed his argument, something unexpected happened — I found myself agreeing, though from an entirely different perspective. What I discovered challenges both those waiting for AGI’s arrival and those claiming it’s nowhere near: we might be asking the wrong question altogether."
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# 生成临时音频文件
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tts.tts_to_file(text=text_cn1, file_path="part1.wav", speaker_wav=speaker_wav_zh, language="zh-cn")
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tts.tts_to_file(text=text_en, file_path="part2.wav", speaker_wav=speaker_wav_en, language="en")
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tts.tts_to_file(text=text_cn2, file_path="part3.wav", speaker_wav=speaker_wav_zh, language="zh-cn")
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# tts.tts_to_file(text=test_all, file_path="all.wav", speaker_wav=speaker_wav_en, language="en")
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# # **合并音频**
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audio1 = AudioSegment.from_wav("part1.wav")
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audio2 = AudioSegment.from_wav("part2.wav")
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audio3 = AudioSegment.from_wav("part3.wav")
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final_audio = audio1 + audio2 + audio3
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final_audio.export("output.wav", format="wav")
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print(f"✅ 语音合成完成！已保存到 output.wav")
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import sounddevice as sd
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print("🎤 正在查询可用的音频设备...\n")
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devices = sd.query_devices()
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for i, device in enumerate(devices):
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print(f"设备 ID {i}: {device['name']} - 输入通道: {device['max_input_channels']}")
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# 只显示支持 WASAPI 环回录音的设备
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wasapi_devices = [d for d in devices if "loopback" in d["name"].lower()]
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if wasapi_devices:
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print("\n✅ 发现 WASAPI '环回录音' 设备：")
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for d in wasapi_devices:
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print(f" - {d['name']}")
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else:
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print("\n❌ 没有找到 'WASAPI 环回录音' 设备，请尝试手动启用或安装 Virtual Cable。")
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import pyaudio
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import wave
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import numpy as np
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import time
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from pydub import AudioSegment
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# 录音参数
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FORMAT = pyaudio.paFloat32 # 32-bit 浮点格式
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CHANNELS = 1
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RATE = 44100
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CHUNK = 1024
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OUTPUT_FILENAME = "output.wav"
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THRESHOLD = 0.008 # 适配 paFloat32（范围是 [-1.0, 1.0]）
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SILENCE_DURATION = 2 # 静音时间（秒）
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# **🔍 列出所有可用设备并解释用途**
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def list_audio_devices():
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audio = pyaudio.PyAudio()
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device_info = []
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print("\n🎤 **可用的录音设备列表**\n")
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print(f"{'ID':<5}{'设备名称':<35}{'输入通道数':<15}{'设备类型'}")
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print("="*80)
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for i in range(audio.get_device_count()):
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dev = audio.get_device_info_by_index(i)
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name = dev['name'].lower()
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channels = dev['maxInputChannels']
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if channels > 0:
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# 设备类型判断
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if "stereo mix" in name or "what you hear" in name:
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device_type = "✅ 立体声混音（推荐）"
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elif "loopback" in name:
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device_type = "🔄 环回录音（可选）"
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elif "mic" in name or "microphone" in name:
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device_type = "🎤 麦克风（不推荐）"
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else:
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device_type = "🎧 其他音频设备"
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print(f"{i:<5}{dev['name']:<35}{channels:<15}{device_type}")
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device_info.append((i, dev['name'], device_type))
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audio.terminate()
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print("\n✅ **如果 '立体声混音' 存在，优先使用它**，否则尝试 '环回' 或手动选择。\n")
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return device_info
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# **🔍 自动选择最佳录音设备**
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def get_best_device():
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audio = pyaudio.PyAudio()
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best_device = None
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fallback_device = None
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print("\n🔍 **正在尝试自动选择最佳录音设备...**")
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for i in range(audio.get_device_count()):
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dev = audio.get_device_info_by_index(i)
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name = dev['name'].lower()
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channels = dev['maxInputChannels']
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if channels > 0:
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# **优先选择“立体声混音”**
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if "stereo mix" in name or "what you hear" in name:
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print(f"✅ 选择设备: {dev['name']} (ID: {i}) - 立体声混音（最佳）")
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best_device = i
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break
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# **其次选择“环回（loopback）”**
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if "loopback" in name and best_device is None:
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print(f"🔄 选择设备: {dev['name']} (ID: {i}) - 环回录音")
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best_device = i
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# **如果没有立体声混音或环回，选择一个非麦克风的可用设备**
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if "mic" not in name and "microphone" not in name and fallback_device is None:
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fallback_device = i
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audio.terminate()
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if best_device is not None:
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return best_device
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elif fallback_device is not None:
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print(f"⚠️ 没有找到 '立体声混音'，使用默认设备 (ID: {fallback_device})")
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return fallback_device
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else:
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print("\n❌ 没有检测到合适的设备，请手动选择一个设备 ID：")
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device_list = list_audio_devices()
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selected_device = input("🔹 请输入你要使用的设备 ID（数字）: ")
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try:
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selected_device = int(selected_device)
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return selected_device
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except ValueError:
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raise RuntimeError("❌ 设备 ID 无效，程序终止！")
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# **🎤 运行设备列表**
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device_list = list_audio_devices()
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# **🔧 选择最佳设备**
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device_index = get_best_device()
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# 初始化 PyAudio
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audio = pyaudio.PyAudio()
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try:
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if device_index is not None:
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stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True,
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input_device_index=device_index,
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frames_per_buffer=CHUNK)
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else:
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stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True,
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frames_per_buffer=CHUNK)
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print("🎤 只录制系统声音，麦克风已禁用！")
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print("🎤 等待声音触发录音...")
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# 等待声音触发
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while True:
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data = stream.read(CHUNK, exception_on_overflow=False)
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audio_data = np.frombuffer(data, dtype=np.float32) # 读取 32-bit 浮点数据
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volume = np.max(np.abs(audio_data)) # 计算音量
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if volume > THRESHOLD:
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print("🎙 检测到声音，开始录音...")
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break
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frames = [data]
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silent_start = None
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# 开始录音，直到检测到 2 秒以上静音
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while True:
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data = stream.read(CHUNK, exception_on_overflow=False)
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audio_data = np.frombuffer(data, dtype=np.float32)
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# **放大音量**
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volume_boost = 5.0 # 放大 5 倍
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audio_data = np.clip(audio_data * volume_boost, -1.0, 1.0) # 避免溢出
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frames.append(audio_data.tobytes())
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volume = np.max(np.abs(audio_data)) # 计算当前音量
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if volume < THRESHOLD:
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if silent_start is None:
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silent_start = time.time()
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elif time.time() - silent_start >= SILENCE_DURATION:
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print("🤫 检测到静音超过 2 秒，停止录音...")
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break
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else:
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silent_start = None
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print("🎼 录音结束，正在保存文件...")
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# 关闭流
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stream.stop_stream()
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stream.close()
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audio.terminate()
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# **转换 `paFloat32` 录音数据为 `paInt16`（标准 WAV 格式）**
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int_frames = []
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for frame in frames:
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float_data = np.frombuffer(frame, dtype=np.float32) # 读取浮点数据
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int_data = np.int16(float_data * 32767) # 转换为 16-bit PCM
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int_frames.append(int_data.tobytes())
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# 保存为 WAV
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wf = wave.open(OUTPUT_FILENAME, 'wb')
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(2) # 16-bit PCM
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wf.setframerate(RATE)
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wf.writeframes(b''.join(int_frames))
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wf.close()
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print(f"✅ 录音已保存为 {OUTPUT_FILENAME}")
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# **自动归一化音量**
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audio = AudioSegment.from_wav(OUTPUT_FILENAME)
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normalized_audio = audio.apply_gain(-audio.dBFS)
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normalized_audio.export("output_loud.wav", format="wav")
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print("✅ 录音音量已调整，保存为 output_loud.wav")
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except Exception as e:
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print(f"❌ 录音失败: {e}")
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audio.terminate()
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import torch
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from TTS.tts.models.xtts import XttsAudioConfig
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from TTS.tts.configs.xtts_config import XttsConfig
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from TTS.config.shared_configs import BaseDatasetConfig
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from TTS.tts.models.xtts import XttsArgs
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# **添加 `XttsConfig` 到 PyTorch 安全全局对象**
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torch.serialization.add_safe_globals([XttsConfig, XttsAudioConfig, BaseDatasetConfig, XttsArgs])
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# **强制 `weights_only=False` 解决 UnpicklingError**
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# def load_fsspec_fixed(*args, **kwargs):
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# kwargs["weights_only"] = False # 关键修正
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# return torch.load(*args, **kwargs)
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def load_fsspec_fixed(*args, **kwargs):
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kwargs.pop("cache", None) # 移除 cache 参数，避免报错
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kwargs["weights_only"] = False # 关键修正
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return torch.load(*args, **kwargs)
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# **覆盖 Coqui TTS 的 `load_fsspec` 方法**
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import TTS.utils.io
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TTS.utils.io.load_fsspec = load_fsspec_fixed
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from TTS.api import TTS
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# **XTTS v2 高质量多语言模型**
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XTTS_MODEL = "tts_models/multilingual/multi-dataset/xtts_v2"
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# **VITS 高质量单语言模型**
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VITS_MODEL = "tts_models/en/ljspeech/vits"
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# **高质量的参考音频**
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speaker_wav_en = "example_speaker_en.wav" # 必须提供此音频，确保质量良好
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# **要转换的文本**
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text_en = "This is a high-quality text-to-speech conversion using XTTS v2 and VITS."
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text = "记者从越秀区了解到，广州博物馆隆重推出“吉祥有年——广州博物馆藏吉祥文物展”及“吉祥有年·潮派趁墟”主题新春市集，让市民群众在探秘文物珍宝的同时，也能解锁非遗新体验、品尝地道广府味，一起喜迎吉祥乙巳蛇年。"
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# **加载 XTTS v2**
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print("🚀 正在加载 XTTS v2 模型，请稍候...")
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tts_xtts = TTS(XTTS_MODEL)
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# **使用 XTTS v2 生成语音**
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tts_xtts.tts_to_file(
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text=text,
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file_path="output_xtts.wav",
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speaker_wav=speaker_wav_en, # 参考音频（用于克隆音色）
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language="zh-cn", # 语言代码，必须匹配文本语言
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split_sentences=True # 让模型自动优化长文本
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)
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print("✅ XTTS v2 语音合成完成！已保存到 output_xtts.wav 🎵")
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# **加载 VITS**
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print("🚀 正在加载 VITS 模型，请稍候...")
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tts_vits = TTS(VITS_MODEL)
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# **使用 VITS 生成语音**
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tts_vits.tts_to_file(
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text=text_en,
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split_sentences=True,
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file_path="output_vits.wav"
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)
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print("✅ VITS 语音合成完成！已保存到 output_vits.wav 🎵")
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@ -0,0 +1,9 @@
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from TTS.utils.manage import ModelManager
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# 获取 Coqui TTS 官方模型列表
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manager = ModelManager()
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models = manager.list_models()
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print("✅ 可用的 TTS 预训练模型列表：")
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for model in models:
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print(model)
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import sounddevice as sd
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import wavio
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# 录制参数
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duration = 5 # 录制 5 秒
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samplerate = 22050 # 采样率 (XTTS v2 兼容的)
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filename = "example_speaker.wav"
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print("开始录音，说话吧...")
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audio = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype='int16')
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sd.wait()
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print("录音完成，保存中...")
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# 保存 WAV 文件
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wavio.write(filename, audio, samplerate, sampwidth=2)
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print(f"录音已保存为 {filename}")
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