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CGlide
280
load_audio_ui.py
280
load_audio_ui.py
@@ -1,141 +1,141 @@
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import folder_paths
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import os
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import torch
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import av
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def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
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"""Convert audio to float 32 bits PCM format."""
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if wav.dtype.is_floating_point:
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return wav
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elif wav.dtype == torch.int16:
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return wav.float() / (2 ** 15)
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elif wav.dtype == torch.int32:
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return wav.float() / (2 ** 31)
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raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
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def load_audio_file(filepath: str) -> tuple[torch.Tensor, int]:
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"""Uses the latest ComfyUI av-based decoding for maximum compatibility."""
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with av.open(filepath) as af:
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if not af.streams.audio:
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raise ValueError("No audio stream found in the file.")
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stream = af.streams.audio[0]
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sr = stream.codec_context.sample_rate
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n_channels = stream.channels
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frames = []
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for frame in af.decode(streams=stream.index):
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buf = torch.from_numpy(frame.to_ndarray())
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if buf.shape[0] != n_channels:
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buf = buf.view(-1, n_channels).t()
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frames.append(buf)
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if not frames:
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raise ValueError("No audio frames decoded.")
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wav = torch.cat(frames, dim=1)
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wav = f32_pcm(wav)
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return wav, sr
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class LoadAudioUI:
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@classmethod
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def INPUT_TYPES(s):
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try:
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files = folder_paths.get_filename_list("audio")
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except:
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files = []
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if not files:
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input_dir = folder_paths.get_input_directory()
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if os.path.exists(input_dir):
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all_files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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try:
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files = sorted(folder_paths.filter_files_content_types(all_files, ["audio", "video"]))
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except:
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files = sorted(all_files)
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if not files or len(files) == 0:
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files = ["none"]
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return {
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"required": {
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"audio": (files, {"audio_upload": True}), # Moved to the top so it appears first
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"start_time": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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"end_time": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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"duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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},
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"optional": {
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"audioUI": ("AUDIO_UI",)
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}
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}
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CATEGORY = "WhatDreamsCost"
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RETURN_TYPES = ("AUDIO", "FLOAT")
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RETURN_NAMES = ("audio", "duration")
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FUNCTION = "load_audio"
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@classmethod
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def VALIDATE_INPUTS(cls, audio, **kwargs):
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# CRITICAL FIX: This bypasses the "Value not in list" error.
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# By returning True, we tell ComfyUI to allow the 'audio' value even if it isn't in
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# the current dropdown list. This allows the execution to reach load_audio(),
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# where our fallback silence logic can handle the missing file gracefully.
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return True
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def load_audio(self, audio, start_time, end_time, duration, **kwargs):
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# Determine the annotated file path if a file is actually selected
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# We wrap this in a try/except because get_annotated_filepath can fail if
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# the input string is malformed or doesn't follow expected paths.
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try:
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audio_path = folder_paths.get_annotated_filepath(audio) if audio != "none" else ""
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except:
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audio_path = ""
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# --- FALLBACK LOGIC ---
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# If the file is 'none' or doesn't exist on disk, provide 1 second of silence
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if audio == "none" or not audio_path or not os.path.exists(audio_path):
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missing_info = audio if audio != "none" else "None selected"
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print(f"!!! [LoadAudioUI] Warning: Audio file '{missing_info}' not found. Outputting 1 second of silence.")
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sample_rate = 44100
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# 1 second of silence (stereo) -> shape [channels, time]
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waveform = torch.zeros((2, 44100))
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else:
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try:
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waveform, sample_rate = load_audio_file(audio_path)
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except Exception as e:
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# If decoding fails for any reason, fallback to silence rather than crashing the workflow
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print(f"!!! [LoadAudioUI] Error decoding {audio}: {e}. Falling back to silence.")
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sample_rate = 44100
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waveform = torch.zeros((2, 44100))
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# Convert seconds to frames
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start_frame = int(start_time * sample_rate)
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if end_time > 0:
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end_frame = int(end_time * sample_rate)
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# Ensure the end_frame does not exceed the actual audio length
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end_frame = min(end_frame, waveform.shape[1])
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else:
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# 0 defaults to the end of the file
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end_frame = waveform.shape[1]
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# Ensure start frame stays within bounds and doesn't pass the end frame
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start_frame = min(start_frame, end_frame)
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# Trim the waveform tensor -> shape: [channels, time]
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trimmed_waveform = waveform[:, start_frame:end_frame]
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# Final safety check: if trimming resulted in zero length, give it a tiny bit of padding
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# to prevent downstream nodes from crashing on empty tensors
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if trimmed_waveform.shape[1] == 0:
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trimmed_waveform = torch.zeros((waveform.shape[0], 1))
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# Format for ComfyUI's standard AUDIO type: [batch, channels, time]
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audio_output = {"waveform": trimmed_waveform.unsqueeze(0), "sample_rate": sample_rate}
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# Calculate the final trimmed duration in seconds as a float
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final_duration = float(trimmed_waveform.shape[1] / sample_rate)
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import folder_paths
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import os
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import torch
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import av
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def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
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"""Convert audio to float 32 bits PCM format."""
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if wav.dtype.is_floating_point:
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return wav
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elif wav.dtype == torch.int16:
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return wav.float() / (2 ** 15)
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elif wav.dtype == torch.int32:
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return wav.float() / (2 ** 31)
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raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
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def load_audio_file(filepath: str) -> tuple[torch.Tensor, int]:
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"""Uses the latest ComfyUI av-based decoding for maximum compatibility."""
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with av.open(filepath) as af:
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if not af.streams.audio:
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raise ValueError("No audio stream found in the file.")
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stream = af.streams.audio[0]
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sr = stream.codec_context.sample_rate
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n_channels = stream.channels
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frames = []
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for frame in af.decode(streams=stream.index):
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buf = torch.from_numpy(frame.to_ndarray())
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if buf.shape[0] != n_channels:
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buf = buf.view(-1, n_channels).t()
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frames.append(buf)
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if not frames:
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raise ValueError("No audio frames decoded.")
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wav = torch.cat(frames, dim=1)
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wav = f32_pcm(wav)
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return wav, sr
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class LoadAudioUI:
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@classmethod
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def INPUT_TYPES(s):
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try:
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files = folder_paths.get_filename_list("audio")
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except:
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files = []
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if not files:
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input_dir = folder_paths.get_input_directory()
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if os.path.exists(input_dir):
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all_files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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try:
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files = sorted(folder_paths.filter_files_content_types(all_files, ["audio", "video"]))
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except:
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files = sorted(all_files)
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if not files or len(files) == 0:
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files = ["none"]
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return {
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"required": {
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"audio": (files, {"audio_upload": True}), # Moved to the top so it appears first
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"start_time": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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"end_time": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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"duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100000.0, "step": 0.01}),
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},
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"optional": {
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"audioUI": ("AUDIO_UI",)
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}
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}
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CATEGORY = "WhatDreamsCost"
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RETURN_TYPES = ("AUDIO", "FLOAT")
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RETURN_NAMES = ("audio", "duration")
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FUNCTION = "load_audio"
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@classmethod
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def VALIDATE_INPUTS(cls, audio, **kwargs):
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# CRITICAL FIX: This bypasses the "Value not in list" error.
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# By returning True, we tell ComfyUI to allow the 'audio' value even if it isn't in
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# the current dropdown list. This allows the execution to reach load_audio(),
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# where our fallback silence logic can handle the missing file gracefully.
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return True
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def load_audio(self, audio, start_time, end_time, duration, **kwargs):
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# Determine the annotated file path if a file is actually selected
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# We wrap this in a try/except because get_annotated_filepath can fail if
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# the input string is malformed or doesn't follow expected paths.
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try:
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audio_path = folder_paths.get_annotated_filepath(audio) if audio != "none" else ""
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except:
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audio_path = ""
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# --- FALLBACK LOGIC ---
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# If the file is 'none' or doesn't exist on disk, provide 1 second of silence
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if audio == "none" or not audio_path or not os.path.exists(audio_path):
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missing_info = audio if audio != "none" else "None selected"
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print(f"!!! [LoadAudioUI] Warning: Audio file '{missing_info}' not found. Outputting 1 second of silence.")
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sample_rate = 44100
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# 1 second of silence (stereo) -> shape [channels, time]
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waveform = torch.zeros((2, 44100))
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else:
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try:
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waveform, sample_rate = load_audio_file(audio_path)
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except Exception as e:
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# If decoding fails for any reason, fallback to silence rather than crashing the workflow
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print(f"!!! [LoadAudioUI] Error decoding {audio}: {e}. Falling back to silence.")
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sample_rate = 44100
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waveform = torch.zeros((2, 44100))
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# Convert seconds to frames
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start_frame = int(start_time * sample_rate)
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if end_time > 0:
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end_frame = int(end_time * sample_rate)
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# Ensure the end_frame does not exceed the actual audio length
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end_frame = min(end_frame, waveform.shape[1])
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else:
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# 0 defaults to the end of the file
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end_frame = waveform.shape[1]
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# Ensure start frame stays within bounds and doesn't pass the end frame
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start_frame = min(start_frame, end_frame)
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# Trim the waveform tensor -> shape: [channels, time]
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trimmed_waveform = waveform[:, start_frame:end_frame]
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# Final safety check: if trimming resulted in zero length, give it a tiny bit of padding
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# to prevent downstream nodes from crashing on empty tensors
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if trimmed_waveform.shape[1] == 0:
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trimmed_waveform = torch.zeros((waveform.shape[0], 1))
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# Format for ComfyUI's standard AUDIO type: [batch, channels, time]
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audio_output = {"waveform": trimmed_waveform.unsqueeze(0), "sample_rate": sample_rate}
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# Calculate the final trimmed duration in seconds as a float
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final_duration = float(trimmed_waveform.shape[1] / sample_rate)
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return (audio_output, final_duration)
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