Hawk Lee
e21a5c9eba
The eff537d commit changed all files from 755 to 644.
Restores original executable permissions.
205 lines
8.7 KiB
Python
Executable File
205 lines
8.7 KiB
Python
Executable File
import torch
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import numpy as np
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class AIIA_Audio_Silence_Splitter:
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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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"max_duration": ("FLOAT", {"default": 27.0, "min": 5.0, "max": 29.0, "step": 0.1}),
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"silence_threshold": ("FLOAT", {"default": 0.001, "min": 0.0001, "max": 0.1, "step": 0.0001}),
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"min_silence_duration": ("FLOAT", {"default": 0.3, "min": 0.05, "max": 2.0, "step": 0.05}),
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}
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}
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RETURN_TYPES = ("WHISPER_CHUNKS", "INT")
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RETURN_NAMES = ("whisper_chunks", "chunk_count")
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FUNCTION = "split_audio"
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CATEGORY = "AIIA/audio"
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def split_audio(self, audio, max_duration, silence_threshold, min_silence_duration):
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waveform = audio["waveform"]
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sample_rate = audio["sample_rate"]
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# 强制在 CPU 处理以防 OOM
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waveform_cpu = waveform.cpu()
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# 计算能量
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# waveform shape: [B, C, T]
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energy = torch.abs(waveform_cpu).mean(dim=1).squeeze()
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if energy.ndim > 1: energy = energy.mean(dim=0)
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# 1. 识别静音区间
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is_silence = energy < silence_threshold
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sil_np = is_silence.numpy()
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# 寻找变化点
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diff = np.diff(sil_np.astype(int))
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starts = np.where(diff == 1)[0]
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ends = np.where(diff == -1)[0]
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if sil_np[0]: starts = np.insert(starts, 0, 0)
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if sil_np[-1]: ends = np.append(ends, len(sil_np) - 1)
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# 保证成对
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if len(starts) > len(ends): starts = starts[:len(ends)]
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elif len(ends) > len(starts): ends = ends[:len(starts)]
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# 过滤过短的静音
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# 修正变量名：使用参数传入的 min_silence_duration
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min_sil_samples = int(min_silence_duration * sample_rate)
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valid_silence_gaps = []
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for s, e in zip(starts, ends):
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if e - s >= min_sil_samples:
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valid_silence_gaps.append((s / sample_rate, e / sample_rate))
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# 2. 贪心构建
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total_duration = waveform_cpu.shape[-1] / sample_rate
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chunks = []
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current_start = 0.0
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while current_start < total_duration:
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target_end = current_start + max_duration
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if target_end >= total_duration:
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chunks.append([current_start, total_duration])
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break
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best_gap_mid = -1
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for g_start, g_end in valid_silence_gaps:
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g_mid = (g_start + g_end) / 2
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if current_start < g_mid <= target_end:
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best_gap_mid = g_mid
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elif g_mid > target_end:
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break
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if best_gap_mid != -1:
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chunks.append([current_start, best_gap_mid])
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current_start = best_gap_mid
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else:
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chunks.append([current_start, target_end])
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current_start = target_end
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whisper_chunks_data = {
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"text": "",
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"chunks": [
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{"timestamp": [round(c[0], 3), round(c[1], 3)], "text": f"Chunk {i}", "speaker": "AIIA_SMART_CHUNK"}
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for i, c in enumerate(chunks)
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],
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"language": ""
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}
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return (whisper_chunks_data, len(chunks))
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class AIIA_Audio_PostProcess:
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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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"target_rate": (["44100", "48000", "24000", "22050", "Original"], {"default": "44100"}),
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"fade_length": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5.0, "step": 0.1, "tooltip": "Fade in/out duration in seconds"}),
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"normalize": ("BOOLEAN", {"default": True, "tooltip": "Normalize to -1dB"}),
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"resampling_alg": (["sinc_interp_hann", "sinc_interp_kaiser"], {"default": "sinc_interp_hann"}),
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"lowpass_cutoff": ("INT", {"default": 11000, "min": 0, "max": 24000, "step": 100, "tooltip": "Apply LowPass filter at this frequency (Hz) to remove aliasing noise. 0 to disable."}),
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"highpass_cutoff": ("INT", {"default": 60, "min": 0, "max": 2000, "step": 10, "tooltip": "Apply HighPass filter at this frequency (Hz) to remove low-end rumble/DC offset. 0 to disable."}),
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},
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"optional": {
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"splice_info": ("SPLICE_INFO",),
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}
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}
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RETURN_TYPES = ("AUDIO", "SPLICE_INFO")
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RETURN_NAMES = ("audio", "splice_info")
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FUNCTION = "process_audio"
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CATEGORY = "AIIA/Audio"
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def process_audio(self, audio, target_rate, fade_length, normalize, resampling_alg, lowpass_cutoff, highpass_cutoff, splice_info=None):
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import torchaudio
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import copy
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import torchaudio.functional as F
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waveform = audio["waveform"] # [B, C, T] usually
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original_rate = audio["sample_rate"]
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if waveform.ndim == 2:
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waveform = waveform.unsqueeze(0)
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new_splice_info = copy.deepcopy(splice_info) if splice_info else None
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# 1. Resample
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if target_rate != "Original":
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new_rate = int(target_rate)
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if new_rate != original_rate:
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# Map legacy/UI friendly names to valid torchaudio methods if necessary
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valid_method = resampling_alg
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if resampling_alg in ["sinc_best", "sinc_fast"]: valid_method = "sinc_interp_hann"
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elif resampling_alg == "kaiser_best": valid_method = "sinc_interp_kaiser"
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resampler = torchaudio.transforms.Resample(
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orig_freq=original_rate,
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new_freq=new_rate,
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resampling_method=valid_method,
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dtype=waveform.dtype
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)
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waveform = resampler(waveform)
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# Update splice info if present
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if new_splice_info and "splice_points" in new_splice_info:
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scale_factor = new_rate / original_rate
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new_splice_info["splice_points"] = [int(p * scale_factor) for p in new_splice_info["splice_points"]]
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if "total_samples" in new_splice_info:
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new_splice_info["total_samples"] = int(new_splice_info["total_samples"] * scale_factor)
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original_rate = new_rate
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# 2. HighPass Filter (Remove Rumble / DC Offset)
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if highpass_cutoff > 0:
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# Single pass (2nd order, 12dB/oct) is safer for preserving voice body.
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# Cascading here was too aggressive and killed the bass details.
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waveform = F.highpass_biquad(waveform, original_rate, cutoff_freq=highpass_cutoff)
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# 3. LowPass Filter (Anti-Aliasing / Denoise)
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if lowpass_cutoff > 0:
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# lowpass_biquad is a 2nd order filter (12dB/octave).
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# To remove stubborn aliasing ("stripes"), we need a steeper slope.
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# We cascade it 6 times to create a ~72dB/octave "Brick Wall" effect.
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for _ in range(6):
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waveform = F.lowpass_biquad(waveform, original_rate, cutoff_freq=lowpass_cutoff)
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# 3. Fade In/Out
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if fade_length > 0:
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fade_samples = int(fade_length * original_rate)
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if fade_samples * 2 < waveform.shape[-1]:
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fade_in = torch.linspace(0, 1, fade_samples, device=waveform.device)
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fade_out = torch.linspace(1, 0, fade_samples, device=waveform.device)
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# Apply to last dim (time)
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# Ensure we handle multiple channels correctly
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if waveform.ndim == 3:
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waveform[:, :, :fade_samples] *= fade_in
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waveform[:, :, -fade_samples:] *= fade_out
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else:
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waveform[..., :fade_samples] *= fade_in
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waveform[..., -fade_samples:] *= fade_out
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# 3. Normalize (to -1dB = 0.891)
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if normalize:
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peak = waveform.abs().max()
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# Safe Normalize: Only normalize if signal is above -30dB (0.03)
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# This prevents boosting pure silence/noise floors into loud artifacts.
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if peak > 0.03:
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target_peak = 0.891 # -1.0 dB
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waveform = waveform * (target_peak / peak)
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return ({"waveform": waveform, "sample_rate": original_rate}, new_splice_info)
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NODE_CLASS_MAPPINGS = {
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"AIIA_Audio_Silence_Splitter": AIIA_Audio_Silence_Splitter,
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"AIIA_Audio_PostProcess": AIIA_Audio_PostProcess
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AIIA_Audio_Silence_Splitter": "Audio Smart Chunker (Silence-based)",
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"AIIA_Audio_PostProcess": "Audio Post-Process (Resample/Fade/Norm)"
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}
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