import sox import tempfile import os import torchaudio import librosa import torch import ast import silentcipher import folder_paths from typing import List, Union, Optional models_dir = folder_paths.models_dir input_dir = folder_paths.get_input_directory() def get_path(): from pathlib import Path import yaml script_dir = os.path.dirname(os.path.abspath(__file__)) yaml_file = os.path.join(script_dir, "extra_help_file.yaml") try: with open(yaml_file, "r", encoding="utf-8") as f: data = yaml.safe_load(f) audios_dir = data["audios_dir"] audios_dir = Path(audios_dir) if not os.path.exists(audios_dir): raise Exception(f"Customize audios loading path: {audios_dir} not exists.") print(f"Customize audios loading path: {audios_dir}") return audios_dir except FileNotFoundError: print(f"Error: File not found - extra_help_file.yaml") except yaml.YAMLError as e: print(f"Error parsing YAML file: {e}") except KeyError: print(f"Error: Missing key 'audios_dir' in YAML file.") except Exception as e: print(f"Unexpected error: {e}") print("No customize audios loading path found, use default path.") return input_dir def get_all_files( root_dir: str, return_type: str = "list", extensions: Optional[List[str]] = None, exclude_dirs: Optional[List[str]] = None, relative_path: bool = False ) -> Union[List[str], dict]: """ 递归获取目录下所有文件路径 :param root_dir: 要遍历的根目录 :param return_type: 返回类型 - "list"(列表) 或 "dict"(按目录分组) :param extensions: 可选的文件扩展名过滤列表 (如 ['.py', '.txt']) :param exclude_dirs: 要排除的目录名列表 (如 ['__pycache__', '.git']) :param relative_path: 是否返回相对路径 (相对于root_dir) :return: 文件路径列表或字典 """ file_paths = [] file_dict = {} root_dir = os.path.normpath(root_dir) for dirpath, dirnames, filenames in os.walk(root_dir): if exclude_dirs: dirnames[:] = [d for d in dirnames if d not in exclude_dirs] current_files = [] for filename in filenames: if extensions: if not any(filename.lower().endswith(ext.lower()) for ext in extensions): continue full_path = os.path.join(dirpath, filename) if relative_path: full_path = os.path.relpath(full_path, root_dir) current_files.append(full_path) if return_type == "dict": dict_key = os.path.relpath(dirpath, root_dir) if relative_path else dirpath if current_files: file_dict[dict_key] = current_files else: file_paths.extend(current_files) return file_dict if return_type == "dict" else file_paths class LoadAudioMW: audios_dir = get_path() files = get_all_files(audios_dir, extensions=[".wav", ".mp3", ".flac", ".mp4", ".WAV", ".MP3", ".FLAC", ".MP4"], relative_path=True) @classmethod def INPUT_TYPES(s): return {"required": { "audio": (sorted(s.files),), "start_time_sec": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 10000.0, "step": 0.01, "display": "number", "tooltip": "Start time of the slice in seconds" }), "duration_sec": ("FLOAT", { "default": 5.0, "min": 0.01, "max": 10000.0, "step": 0.01, "display": "number", "tooltip": "Desired duration of the slice in seconds" }), }, "optional": { "fps": ("FLOAT", { "default": 24.0, "min": 1.0, "max": 60.0, "step": 1.0, }) } } CATEGORY = "🎤MW/MW-Audio-Tools" RETURN_TYPES = ("AUDIO", "INT") RETURN_NAMES = ("AUDIO", "frame_count") FUNCTION = "load" def load(self, audio, start_time_sec=0, duration_sec=5, fps=24.0): audio_path = os.path.join(LoadAudioMW.audios_dir, audio) waveform, sample_rate = torchaudio.load(audio_path) waveform = waveform.unsqueeze(0) if waveform is None or waveform.numel() == 0 or sample_rate <= 0: raise Exception("SliceAudio received invalid input audio.") start_time_sec = max(0.0, start_time_sec) duration_sec = max(0.01, duration_sec) start_sample = round(start_time_sec * sample_rate) num_samples_requested = round(duration_sec * sample_rate) total_samples = waveform.shape[-1] start_sample = max(0, min(start_sample, total_samples)) actual_end_sample = min(start_sample + num_samples_requested, total_samples) if start_sample >= actual_end_sample: raise ValueError(f"SliceAudio requested slice starts at or after the calculated end ({start_sample} >= {actual_end_sample}).") else: sliced_waveform = waveform[..., start_sample:actual_end_sample] output_audio = { "waveform": sliced_waveform, "sample_rate": sample_rate } return {"result": (output_audio, int(fps * duration_sec) + 1)} class AudioConcatenate: """ ComfyUI node to concatenate two audio inputs with smart channel handling. Inputs are expected in the format {"waveform": torch.Tensor, "sample_rate": int}. The node concatenates waveform_a followed by waveform_b. If sample rates differ, both waveforms are resampled to the maximum of the two rates. Channel Handling Logic: - If both inputs are mono (1 channel), output is mono. - Otherwise (if either input is > 1 channel), output is stereo (2 channels). - Mono (1) to Stereo (2) conversion is done by duplicating the channel. - Multi-channel (> 2) to Stereo (2) conversion is done by a simple averaging downmix (average all channels to mono, then duplicate for stereo). A warning is printed as this is a basic downmix method. Output is in the same {"waveform": torch.Tensor, "sample_rate": int} format, with the higher sample rate and the determined output channel count (1 or 2). """ @classmethod def INPUT_TYPES(s): return { "required": { "audio_a": ("AUDIO",), "audio_b": ("AUDIO",), } } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio",) FUNCTION = "concatenate" CATEGORY = "🎤MW/MW-Audio-Tools" def concatenate(self, audio_a, audio_b): """ Concatenates two audio waveforms after resampling and smart channel adjustment. Args: audio_a (dict): First audio input {"waveform": tensor, "sample_rate": int}. audio_b (dict): Second audio input {"waveform": tensor, "sample_rate": int}. Returns: tuple: A tuple containing the output audio dictionary. ({"waveform": concatenated_tensor, "sample_rate": max_sr},) """ waveform_a = audio_a["waveform"] sr_a = audio_a["sample_rate"] waveform_b = audio_b["waveform"] sr_b = audio_b["sample_rate"] final_sr = max(sr_a, sr_b) resampled_waveform_a = waveform_a if sr_a != final_sr: print(f"Concatenate Audio Node: Resampling audio A from {sr_a} Hz to {final_sr} Hz") resample_a = torchaudio.transforms.Resample(orig_freq=sr_a, new_freq=final_sr).to(waveform_a.device) resampled_waveform_a = resample_a(waveform_a) resampled_waveform_b = waveform_b if sr_b != final_sr: print(f"Concatenate Audio Node: Resampling audio B from {sr_b} Hz to {final_sr} Hz") resample_b = torchaudio.transforms.Resample(orig_freq=sr_b, new_freq=final_sr).to(waveform_b.device) resampled_waveform_b = resample_b(waveform_b) channels_a = resampled_waveform_a.shape[1] channels_b = resampled_waveform_b.shape[1] if channels_a == 1 and channels_b == 1: target_channels = 1 print("Concatenate Audio Node: Both inputs are mono, output will be mono (1 channel).") else: target_channels = 2 print(f"Concatenate Audio Node: At least one input is not mono ({channels_a} vs {channels_b}), output will be stereo (2 channels).") def adjust_channels(wf, current_channels, target_channels, name): if current_channels == target_channels: return wf elif target_channels == 1 and current_channels > 1: print(f"Concatenate Audio Node Warning: Attempting to downmix {name} from {current_channels} to {target_channels} (mono). Simple average downmix applied.") return wf.mean(dim=1, keepdim=True) elif target_channels == 2: if current_channels == 1: print(f"Concatenate Audio Node: Converting {name} from {current_channels} to {target_channels} channels (mono to stereo).") return wf.repeat(1, target_channels, 1) elif current_channels > 2: print(f"Concatenate Audio Node Warning: Converting {name} from {current_channels} to {target_channels} channels (multi-channel to stereo). Applying simple average downmix.") mono_wf = wf.mean(dim=1, keepdim=True) return mono_wf.repeat(1, target_channels, 1) else: raise RuntimeError(f"Concatenate Audio Node: Unsupported channel adjustment requested for {name}: from {current_channels} to {target_channels}.") adjusted_waveform_a = adjust_channels(resampled_waveform_a, channels_a, target_channels, "Audio A") adjusted_waveform_b = adjust_channels(resampled_waveform_b, channels_b, target_channels, "Audio B") concatenated_waveform = torch.cat((adjusted_waveform_a, adjusted_waveform_b), dim=2) output_audio = { "waveform": concatenated_waveform, "sample_rate": final_sr } return (output_audio,) MODEL_CACHE = None class AudioAddWatermark: def __init__(self): if torch.backends.mps.is_available(): device = "mps" elif torch.cuda.is_available(): device = "cuda" else: device = "cpu" self.device = device @classmethod def INPUT_TYPES(s): return {"required": { "audio": ("AUDIO",), "add_watermark": ("BOOLEAN", { "default": False, "tooltip": "Enable audio watermark embedding" }), "key": ("STRING", { "default": "[212, 211, 146, 56, 201]", "tooltip": "Encryption key as list of integers (e.g. [212,211,146,56,201])" }), "unload_model": ("BOOLEAN", { "default": True, "tooltip": "Unload model from memory after use" }) } } CATEGORY = "🎤MW/MW-Audio-Tools" RETURN_TYPES = ("AUDIO", "STRING") RETURN_NAMES = ("audio", "watermark") FUNCTION = "watermarkgen" def watermarkgen(self, audio, add_watermark, key, unload_model): """Main watermark processing pipeline""" ckpt_path = os.path.join(models_dir, "TTS", "SilentCipher", "44_1_khz", "73999_iteration") os.makedirs(ckpt_path, exist_ok=True) config_path = os.path.join(models_dir, ckpt_path, "hparams.yaml") global MODEL_CACHE if MODEL_CACHE is None: MODEL_CACHE = silentcipher.get_model( model_type="44.1k", ckpt_path=ckpt_path, config_path=config_path, device=self.device, ) watermarker = MODEL_CACHE audio_array, sample_rate = self.load_audio(audio) audio_array = audio_array.to(self.device) if add_watermark: key = self._parse_key(key) audio_array, sample_rate = self.watermark(watermarker, audio_array, sample_rate, key) watermark = self.verify(watermarker, audio_array, sample_rate) if unload_model: del watermarker MODEL_CACHE = None torch.cuda.empty_cache() return ({"waveform": audio_array.unsqueeze(0).unsqueeze(0).cpu(), "sample_rate": sample_rate}, watermark) @torch.inference_mode() def watermark(self, watermarker: silentcipher.server.Model, audio_array: torch.Tensor, sample_rate: int, watermark_key: list[int], ) -> tuple[torch.Tensor, int]: if len(audio_array.shape) > 1 and audio_array.shape[0] > 1: audio_array = audio_array.mean(dim=0) audio_array = audio_array.to(self.device) audio_array_44khz = torchaudio.functional.resample( audio_array, orig_freq=sample_rate, new_freq=44100 ).to(self.device) if len(audio_array_44khz.shape) != 1: audio_array_44khz = audio_array_44khz.reshape(-1) try: encoded, _ = watermarker.encode_wav(audio_array_44khz, 44100, watermark_key, calc_sdr=False, message_sdr=30) verify_result = watermarker.decode_wav(encoded, 44100, phase_shift_decoding=True) if not verify_result["status"]: encoded, _ = watermarker.encode_wav(audio_array_44khz, 44100, watermark_key, calc_sdr=False, message_sdr=25) verify_result = watermarker.decode_wav(encoded, 44100, phase_shift_decoding=True) except Exception as e: return audio_array, sample_rate output_sample_rate = min(44100, sample_rate) if output_sample_rate != 44100: encoded = torchaudio.functional.resample( encoded, orig_freq=44100, new_freq=output_sample_rate ).to(self.device) return encoded, output_sample_rate @torch.inference_mode() def verify(self, watermarker: silentcipher.server.Model, watermarked_audio: torch.Tensor, sample_rate: int, ) -> str: if len(watermarked_audio.shape) > 1 and watermarked_audio.shape[0] > 1: watermarked_audio = watermarked_audio.mean(dim=0) if sample_rate != 44100: watermarked_audio_44khz = torchaudio.functional.resample( watermarked_audio, orig_freq=sample_rate, new_freq=44100 ).to(self.device) else: watermarked_audio_44khz = watermarked_audio.to(self.device) if len(watermarked_audio_44khz.shape) != 1: watermarked_audio_44khz = watermarked_audio_44khz.reshape(-1) result_phase = watermarker.decode_wav(watermarked_audio_44khz, 44100, phase_shift_decoding=True) result_no_phase = watermarker.decode_wav(watermarked_audio_44khz, 44100, phase_shift_decoding=False) if result_phase["status"]: watermark = "Watermarked:" + str(result_phase["messages"][0]) elif result_no_phase["status"]: watermark = "Watermarked:" + str(result_no_phase["messages"][0]) else: watermark = "No watermarked" return watermark def load_watermarker(self, device: str = "cuda", use_cache = True) -> silentcipher.server.Model: ckpt_path = os.path.join(models_dir, "TTS", "SilentCipher", "44_1_khz", "73999_iteration") config_path = os.path.join(models_dir, ckpt_path, "hparams.yaml") if not use_cache and MODEL_CACHE is not None: return MODEL_CACHE else: model = silentcipher.get_model( model_type="44.1k", ckpt_path=ckpt_path, config_path=config_path, device=device, ) MODEL_CACHE = model del model torch.cuda.empty_cache() return MODEL_CACHE def _parse_key(self, key_string): """Safely parse encryption key from string Args: key_string: String representation of key list Returns: List[int]: Parsed key sequence """ try: return ast.literal_eval(key_string) except (ValueError, SyntaxError) as e: raise ValueError(f"Invalid key format: {str(e)}") def load_audio(self, audio) -> tuple[torch.Tensor, int]: waveform = audio["waveform"].squeeze(0) audio_array = waveform.mean(dim=0) sample_rate = audio["sample_rate"] return audio_array, int(sample_rate) class AdjustAudio: @classmethod def INPUT_TYPES(s): return { "required": { "audio":("AUDIO",), "volume_factor": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}), "pitch_shift": ("INT", {"default": 0, "min": -1200, "max": 1200, "step": 100}), "speed_factor": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 5.0, "step": 0.1}), "normalize": ("BOOLEAN", {"default": True}), "add_echo": ("BOOLEAN", {"default": False}), "gain_in": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}), "gain_out": ("FLOAT", {"default": 0.88, "min": 0.1, "max": 1.0, "step": 0.1}), "delays": ("INT", {"default": 60, "min": 10, "max": 500, "step": 10}), "decays": ("FLOAT", {"default": 0.4, "min": 0.1, "max": 1.0, "step": 0.1}), }, } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio",) FUNCTION = "adjust_audio_properties" CATEGORY = "🎤MW/MW-Audio-Tools" def adjust_audio_properties(self, audio, volume_factor, pitch_shift, speed_factor, normalize, add_echo, gain_in, gain_out, delays, decays): """ 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。 参数说明: - audio (AUDIO): 输入音频。 - volume_factor (float): 音量倍数。 - pitch_shift (int): 音高偏移。 - speed_factor (float): 语速倍数。 - normalize (bool): 是否规范化音量到 -0.1dB。 - add_echo (bool): 是否添加回声效果。 - gain_in (float): 输入增益。 - gain_out (float): 输出增益。 - delays ([int]): 回声延迟时间。 - decays ([float]): 回声衰减时间。 返回: - audio (AUDIO): 处理后的音频。 """ temp_files = [] waveform = audio["waveform"].squeeze(0) sample_rate = audio["sample_rate"] with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: audio_file_path = temp_file.name torchaudio.save(audio_file_path, waveform, sample_rate, format="wav", bits_per_sample=16, encoding="PCM_S") temp_files.append(audio_file_path) tfm = sox.Transformer() tfm.vol(volume_factor, gain_type='amplitude') tfm.pitch(pitch_shift / 100.0) tfm.tempo(speed_factor, audio_type='s') if normalize: tfm.norm(-0.1) if add_echo: tfm.echo(gain_in=gain_in, gain_out=gain_out, delays=[delays], decays=[decays]) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: temp_output = temp_file.name tfm.build(audio_file_path, temp_output) temp_files.append(temp_output) audio, sr = torchaudio.load(temp_output) audio_tensor = {"waveform": audio.unsqueeze(0), "sample_rate": sr} for temp_file in temp_files: if os.path.exists(temp_file): os.remove(temp_file) return (audio_tensor,) class TrimAudio: @classmethod def INPUT_TYPES(s): return { "required": { "audio":("AUDIO",), "start_time": ("FLOAT", {"default": 0.00, "min": 0.00, "step": 0.01}), "end_time": ("FLOAT", {"default": 0.00, "min": 0.00, "step": 0.01}), }, } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio",) FUNCTION = "trim_audio" CATEGORY = "🎤MW/MW-Audio-Tools" def trim_audio(self, audio, start_time, end_time): """ 截取音频的指定时间段。 参数说明: - audio (dict): 输入音频，包含 "waveform" 和 "sample_rate"。 - start_time (float): 开始时间 (秒)。 - end_time (float): 结束时间 (秒)。 返回: - tuple: (audio_tensor,) 处理后的音频字典，包含 "waveform" 和 "sample_rate"。 """ waveform = audio["waveform"].squeeze(0) sample_rate = audio["sample_rate"] temp_files = [] with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_input_file: audio_file_path = temp_input_file.name temp_files.append(audio_file_path) torchaudio.save(audio_file_path, waveform, sample_rate, format="wav", bits_per_sample=16, encoding="PCM_S") tfm = sox.Transformer() tfm.trim(start_time, end_time) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_output_file: temp_output = temp_output_file.name temp_files.append(temp_output) tfm.build(audio_file_path, temp_output) audio, sr = torchaudio.load(temp_output) audio_tensor = {"waveform": audio.unsqueeze(0), "sample_rate": sr} for temp_file in temp_files: if os.path.exists(temp_file): os.remove(temp_file) return (audio_tensor,) class RemoveSilence: @classmethod def INPUT_TYPES(cls): return { "required": { "audio": ("AUDIO",), "min_silence_duration": ("FLOAT", {"default": 0.3, "min": 0.1, "max": 3.0, "step": 0.1}), "silence_threshold": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 100, "step": 0.1}), "buffer_around_silence": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("AUDIO",) RETURN_NAMES = ("audio",) FUNCTION = "remove_silence" CATEGORY = "🎤MW/MW-Audio-Tools" def remove_silence(self, audio, min_silence_duration, silence_threshold, buffer_around_silence=False): temp_files = [] waveform = audio["waveform"].squeeze(0) sample_rate = audio["sample_rate"] with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_input_file: audio_file_path = temp_input_file.name temp_files.append(audio_file_path) torchaudio.save(audio_file_path, waveform, sample_rate, format="wav", bits_per_sample=16, encoding="PCM_S") y, sr = librosa.load(audio_file_path, sr=sample_rate) normalized_y = librosa.util.normalize(y) torchaudio.save(audio_file_path, torch.from_numpy(normalized_y).unsqueeze(0), sample_rate, format="wav", bits_per_sample=16, encoding="PCM_S") tfm_silence = sox.Transformer() tfm_silence.silence(min_silence_duration=min_silence_duration, silence_threshold=silence_threshold, buffer_around_silence=buffer_around_silence) with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_base: temp_output = temp_base.name temp_files.append(temp_output) tfm_silence.build(audio_file_path, temp_output, sample_rate_in=sample_rate) processed_audio, sr = torchaudio.load(temp_output) audio_tensor = {"waveform": processed_audio.unsqueeze(0), "sample_rate": sr} for temp_file in temp_files: if os.path.exists(temp_file): os.remove(temp_file) return (audio_tensor,) class MultiLinePromptAT: @classmethod def INPUT_TYPES(cls): return { "required": { "multi_line_prompt": ("STRING", { "multiline": True, "default": ""}), }, } CATEGORY = "🎤MW/MW-Audio-Tools" RETURN_TYPES = ("STRING",) RETURN_NAMES = ("prompt",) FUNCTION = "promptgen" def promptgen(self, multi_line_prompt: str): return (multi_line_prompt.strip(),) class StringEditNode: @classmethod def INPUT_TYPES(cls): return { "required": { "text": ("STRING", {"forceInput": True}), }, "optional": { "paste_edit_text": ("STRING", {"multiline": True, "default": "",}), "split_tag": ("STRING", {"default": "",}), "form_right": ("BOOLEAN", {"default": False}), }, } RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING") RETURN_NAMES = ("text", "split_1", "split_2", "edited_text", "merged_text") FUNCTION = "edit_text" CATEGORY = "🎤MW/MW-Audio-Tools" OUTPUT_NODE = False def edit_text(self, text, split_tag="", paste_edit_text="", form_right=False): str_1, str_2, str_3, str_4, str_5 = text, "", "", paste_edit_text, "" str_5 = text + paste_edit_text if split_tag != "" and text.find(split_tag) > 0: if form_right: parts = text.rsplit(split_tag, 1) str_2, str_3 = parts[0].strip(), parts[1].strip() else: parts = text.split(split_tag, 1) str_2, str_3 = parts[0].strip(), parts[1].strip() return (str_1, str_2, str_3, str_4, str_5)