140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
import numpy as np
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import torch
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import time
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import librosa
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import sounddevice as sd
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from scipy import ndimage
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from comfy.utils import ProgressBar
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class AudioRecorderDR:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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# 触发控制
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"trigger": ("BOOLEAN", {"default": False}),
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# 录音时长
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"record_sec": ("INT", {
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"default": 5,
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"min": 1,
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"max": 60,
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"step": 1 # 整数秒递增
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}),
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"sample_rate": (["16000", "44100", "48000"], { # 限定标准采样率
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"default": "48000"
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}),
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"n_fft": ("INT", { # 限定为2的幂次方
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"default": 2048,
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"min": 512,
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"max": 4096,
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"step": 512 # 只能选择512,1024,1536,2048,...4096
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}),
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"sensitivity": ("FLOAT", { # 灵敏度精确控制
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"default": 1.2,
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"min": 0.5,
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"max": 3.0,
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"step": 0.1 # 0.1步进
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}),
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"smooth": ("INT", { # 确保为奇数
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"default": 5,
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"min": 1,
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"max": 11,
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"step": 2 # 生成1,3,5,7,9,11
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}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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FUNCTION = "record_and_clean"
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CATEGORY = "MW-DiffRhythm"
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def _stft(self, y, n_fft):
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hop = n_fft // 4
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return librosa.stft(y, n_fft=n_fft, hop_length=hop, win_length=n_fft)
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def _istft(self, spec, n_fft):
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hop = n_fft // 4
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return librosa.istft(spec, hop_length=hop, win_length=n_fft)
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def _calc_noise_profile(self, noise_clip, n_fft):
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noise_spec = self._stft(noise_clip, n_fft)
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return {
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'mean': np.mean(np.abs(noise_spec), axis=1, keepdims=True),
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'std': np.std(np.abs(noise_spec), axis=1, keepdims=True)
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}
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def _spectral_gate(self, spec, noise_profile, sensitivity):
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threshold = noise_profile['mean'] + sensitivity * noise_profile['std']
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return np.where(np.abs(spec) > threshold, spec, 0)
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def _smooth_mask(self, mask, kernel_size):
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smoothed = ndimage.uniform_filter(mask, size=(kernel_size, kernel_size))
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return np.clip(smoothed * 1.2, 0, 1) # 增强边缘保留
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def record_and_clean(self, trigger, record_sec, n_fft, sensitivity, smooth, sample_rate, seed):
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if not trigger:
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return (None,)
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sr = int(sample_rate)
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final_audio = None
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try:
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noise_clip = None
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# 主录音
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# print(f"开始主录音 {record_sec}秒...")
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main_rec = sd.rec(int(record_sec * sr), samplerate=sr, channels=1, dtype='float32')
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pb = ProgressBar(record_sec)
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for _ in range(record_sec * 2):
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time.sleep(0.5)
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pb.update(0.5)
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sd.wait()
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audio = main_rec.flatten()
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# 自动噪声检测
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if noise_clip is None:
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# print("自动检测静默段作为噪声参考...")
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energy = librosa.feature.rms(y=audio, frame_length=n_fft, hop_length=n_fft//4)
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min_idx = np.argmin(energy)
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start = min_idx * (n_fft//4)
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noise_clip = audio[start:start + n_fft*2]
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# 降噪处理
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# print("进行频谱降噪...")
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noise_profile = self._calc_noise_profile(noise_clip, n_fft)
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spec = self._stft(audio, n_fft)
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# 多步骤处理
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mask = np.ones_like(spec) # 初始掩膜
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for _ in range(2): # 双重处理循环
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cleaned_spec = self._spectral_gate(spec, noise_profile, sensitivity)
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mask = np.where(np.abs(cleaned_spec) > 0, 1, 0)
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mask = self._smooth_mask(mask, smooth//2+1)
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spec = spec * mask
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# 相位恢复重建
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processed = self._istft(spec * mask, n_fft)
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# 动态增益归一化
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peak = np.max(np.abs(processed))
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processed = processed * (0.99 / peak) if peak > 0 else processed
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# 格式转换
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waveform = torch.from_numpy(processed).float().unsqueeze(0).unsqueeze(0)
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final_audio = {"waveform": waveform, "sample_rate": sr}
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except Exception as e:
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print(f"Recording/processing failed: {str(e)}")
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raise
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return (final_audio,)
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# 节点注册
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# NODE_CLASS_MAPPINGS = {
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# "AudioRecorderSpark": AudioRecorderSpark
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# }
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# NODE_DISPLAY_NAME_MAPPINGS = {
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# "AudioRecorderSpark": "MW Audio Recorder"
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# } |