331 lines
12 KiB
Python
331 lines
12 KiB
Python
import sox
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import tempfile
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import os
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from glob import glob
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import torchaudio
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import librosa
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import torch
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# import sounddevice as sd
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# from scipy import ndimage
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class AdjustAudio:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"audio":("AUDIO",),
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"volume_factor": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 5.0, "step": 0.1}),
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"pitch_shift": ("INT", {"default": 0, "min": -1200, "max": 1200, "step": 100}),
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"speed_factor": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 5.0, "step": 0.1}),
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"normalize": ("BOOLEAN", {"default": False}),
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"add_echo": ("BOOLEAN", {"default": False}),
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"gain_in": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
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"gain_out": ("FLOAT", {"default": 0.88, "min": 0.1, "max": 1.0, "step": 0.1}),
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"delays": ("INT", {"default": 60, "min": 10, "max": 500, "step": 10}),
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"decays": ("FLOAT", {"default": 0.4, "min": 0.1, "max": 1.0, "step": 0.1}),
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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 = "adjust_audio_properties"
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CATEGORY = "🎤MW/MW-Audio-Tools"
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def adjust_audio_properties(self, audio, volume_factor, pitch_shift, speed_factor, normalize, add_echo, gain_in, gain_out, delays, decays):
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"""
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调节音频的音量、音高和语速，可选规范化音量和添加回声效果。
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参数说明:
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- audio (AUDIO): 输入音频。
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- volume_factor (float): 音量倍数。
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- pitch_shift (int): 音高偏移。
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- speed_factor (float): 语速倍数。
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- normalize (bool): 是否规范化音量到 -0.1dB。
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- add_echo (bool): 是否添加回声效果。
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- gain_in (float): 输入增益。
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- gain_out (float): 输出增益。
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- delays ([int]): 回声延迟时间。
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- decays ([float]): 回声衰减时间。
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返回:
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- audio (AUDIO): 处理后的音频。
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"""
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temp_files = []
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waveform = audio["waveform"].squeeze(0)
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sample_rate = audio["sample_rate"]
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# 创建临时文件
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
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audio_file_path = temp_file.name
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# 使用 torchaudio.save() 保存临时文件
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torchaudio.save(audio_file_path, waveform, sample_rate, format="wav",
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bits_per_sample=16, encoding="PCM_S")
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temp_files.append(audio_file_path)
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tfm = sox.Transformer()
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# 音量调整
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tfm.vol(volume_factor, gain_type='amplitude')
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# 音高调整
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tfm.pitch(pitch_shift / 100.0)
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# 语速调整
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tfm.tempo(speed_factor, audio_type='s')
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# 可选规范化音量
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if normalize:
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tfm.norm(-0.1)
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# 可选添加回声效果
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if add_echo:
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tfm.echo(gain_in=gain_in, gain_out=gain_out, delays=[delays], decays=[decays])
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# 使用临时文件保存中间结果
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
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temp_output = temp_file.name
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tfm.build(audio_file_path, temp_output)
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temp_files.append(temp_output)
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audio, sr = torchaudio.load(temp_output)
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audio_tensor = {"waveform": audio.unsqueeze(0), "sample_rate": sr}
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# 清理所有临时文件
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for temp_file in temp_files:
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if os.path.exists(temp_file):
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os.remove(temp_file)
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return (audio_tensor,)
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class TrimAudio:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"audio":("AUDIO",),
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"start_time": ("FLOAT", {"default": 0.00, "min": 0.00, "step": 0.01}),
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"end_time": ("FLOAT", {"default": 0.00, "min": 0.00, "step": 0.01}),
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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 = "trim_audio"
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CATEGORY = "🎤MW/MW-Audio-Tools"
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def trim_audio(self, audio, start_time, end_time):
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"""
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截取音频的指定时间段。
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参数说明:
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- audio (dict): 输入音频，包含 "waveform" 和 "sample_rate"。
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- start_time (float): 开始时间 (秒)。
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- end_time (float): 结束时间 (秒)。
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返回:
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- tuple: (audio_tensor,) 处理后的音频字典，包含 "waveform" 和 "sample_rate"。
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"""
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waveform = audio["waveform"].squeeze(0)
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sample_rate = audio["sample_rate"]
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# 记录所有临时文件路径
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temp_files = []
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# 创建并保存输入音频的临时文件
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_input_file:
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audio_file_path = temp_input_file.name
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temp_files.append(audio_file_path)
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# 使用 torchaudio.save() 保存临时文件
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torchaudio.save(audio_file_path, waveform, sample_rate, format="wav",
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bits_per_sample=16, encoding="PCM_S")
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tfm = sox.Transformer()
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# 设置截取时间
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tfm.trim(start_time, end_time)
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# 使用临时文件保存截取结果
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_output_file:
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temp_output = temp_output_file.name
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temp_files.append(temp_output)
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tfm.build(audio_file_path, temp_output)
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# 加载处理后的音频
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audio, sr = torchaudio.load(temp_output)
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audio_tensor = {"waveform": audio.unsqueeze(0), "sample_rate": sr}
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# 清理所有临时文件
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for temp_file in temp_files:
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if os.path.exists(temp_file):
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os.remove(temp_file)
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return (audio_tensor,)
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class RemoveSilence:
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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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"audio": ("AUDIO",),
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"min_silence_duration": ("FLOAT", {"default": 0.3, "min": 0.1, "max": 3.0, "step": 0.1}),
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"silence_threshold": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 100, "step": 0.1}),
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"buffer_around_silence": ("BOOLEAN", {"default": False}),
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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 = "remove_silence"
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CATEGORY = "🎤MW/MW-Audio-Tools"
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def remove_silence(self, audio, min_silence_duration, silence_threshold, buffer_around_silence=False):
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temp_files = []
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waveform = audio["waveform"].squeeze(0)
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sample_rate = audio["sample_rate"]
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_input_file:
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audio_file_path = temp_input_file.name
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temp_files.append(audio_file_path)
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torchaudio.save(audio_file_path, waveform, sample_rate, format="wav",
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bits_per_sample=16, encoding="PCM_S")
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y, sr = librosa.load(audio_file_path, sr=sample_rate)
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normalized_y = librosa.util.normalize(y)
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torchaudio.save(audio_file_path, torch.from_numpy(normalized_y).unsqueeze(0), sample_rate, format="wav",
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bits_per_sample=16, encoding="PCM_S")
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tfm_silence = sox.Transformer()
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tfm_silence.silence(min_silence_duration=min_silence_duration,
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silence_threshold=silence_threshold,
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buffer_around_silence=buffer_around_silence)
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_base:
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temp_output = temp_base.name
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temp_files.append(temp_output)
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tfm_silence.build(audio_file_path, temp_output, sample_rate_in=sample_rate)
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processed_audio, sr = torchaudio.load(temp_output)
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audio_tensor = {"waveform": processed_audio.unsqueeze(0), "sample_rate": sr}
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for temp_file in temp_files:
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if os.path.exists(temp_file):
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os.remove(temp_file)
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return (audio_tensor,)
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# class AudioDenoising:
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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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# "audio": ("AUDIO",),
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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 = "clean"
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# CATEGORY = "🎤MW/MW-Audio-Tools"
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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 clean(self, audio, n_fft, sensitivity, smooth, seed):
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# noise_clip = None
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# waveform = audio["waveform"].squeeze().numpy().astype(np.float32)
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# sample_rate = audio["sample_rate"]
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# energy = librosa.feature.rms(y=waveform, 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 = waveform[start:start + n_fft*2]
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# # 降噪处理
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# noise_profile = self._calc_noise_profile(noise_clip, n_fft)
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# spec = self._stft(waveform, 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": sample_rate}
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# return (final_audio,)
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from .AddSubtitlesToVideo import AddSubtitlesToTensor
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from .MWAudioRecorderAT import AudioRecorderAT
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NODE_CLASS_MAPPINGS = {
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"AdjustAudio": AdjustAudio,
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"TrimAudio": TrimAudio,
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"RemoveSilence": RemoveSilence,
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# "AudioDenoising": AudioDenoising,
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"AudioRecorderAT": AudioRecorderAT,
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"AddSubtitlesToVideo": AddSubtitlesToTensor,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AdjustAudio": "Adjust Audio",
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"TrimAudio": "Trim Audio",
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"RemoveSilence": "Remove Silence",
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# "AudioDenoising": "Audio Denoising",
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"AudioRecorderAT": "MW Audio Recorder",
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"AddSubtitlesToVideo": "Add Subtitles To Video",
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} |