704 lines
26 KiB
Python
704 lines
26 KiB
Python
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) |