123 lines
4.1 KiB
Markdown
123 lines
4.1 KiB
Markdown
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# Documentation
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- Class name: `FL_AudioFrameCalculator`
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- Category: `🏵️Fill Nodes`
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- Output node: `False`
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FL_AudioFrameCalculator节点专门用于处理音频文件，通过计算音频中指定小节内的帧数来实现这一目的。该节点基于每分钟节拍数（BPM）、帧率以及其他参数进行计算。它利用音频处理库来提取和操作音频数据，从而为后续处理或分析提供关于音频结构和时间的深入洞察。
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# Input types
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## Required
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- audio_file_path
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- 指定要处理的音频文件的绝对路径。这对于定位和加载音频数据以进行分析至关重要。
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- Comfy dtype: STRING
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- Python dtype: str
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- start_bar
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- 确定音频分析的起始小节，允许在音频文件内进行有针对性的处理。
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- Comfy dtype: INT
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- Python dtype: int
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- bar_count
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- 定义要分析的小节数，使得可以计算音频特定部分的帧数。
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- Comfy dtype: INT
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- Python dtype: int
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- fps
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- 每秒帧数设置，这影响了指定音频段内总帧数的计算。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- AUDIO
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- 经处理的音频片段，适用于进一步的音频操作或分析。
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- Comfy dtype: AUDIO
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- Python dtype: tuple
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- Frame Count
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- 在指定音频段内计算得出的总帧数。
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- Comfy dtype: INT
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- Python dtype: int
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- BPM
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- 在处理过程中确定的音频每分钟节拍数。
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- Comfy dtype: INT
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- Python dtype: int
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- FPS
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- 输入中指定的每秒帧数参数，返回作为参考。
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- Comfy dtype: INT
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- Python dtype: int
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class FL_AudioFrameCalculator:
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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_file_path": ("STRING", {
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"default": "/path/to/audio/file/example.wav",
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"description": "Absolute path to the audio file"
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}),
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"start_bar": ("INT", {
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"default": 0,
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"description": "Start bar"
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}),
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"bar_count": ("INT", {
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"default": 8,
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"description": "Number of bars"
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}),
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"fps": ("INT", {
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"default": 10,
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"description": "Frames per second"
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}),
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}
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}
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RETURN_TYPES = ("AUDIO", "INT", "INT", "INT")
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RETURN_NAMES = ("AUDIO", "Frame Count", "BPM", "FPS")
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FUNCTION = "process_audio"
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CATEGORY = "🏵️Fill Nodes"
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def process_audio(self, audio_file_path, start_bar, bar_count, fps):
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if not os.path.isfile(audio_file_path):
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raise FileNotFoundError(f"Audio file does not exist: {audio_file_path}")
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waveform, sample_rate = torchaudio.load(audio_file_path)
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y_full = waveform.numpy()[0] # Assuming mono audio for simplicity
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tempo, _ = librosa.beat.beat_track(y=y_full, sr=sample_rate)
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bpm = int(tempo)
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# Calculate the duration of a single bar in seconds (4 beats per bar)
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bar_duration = 60 / bpm * 4
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# Calculate start and end times in seconds based on bars
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start_time = start_bar * bar_duration
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end_time = start_time + (bar_count * bar_duration)
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# Convert times to sample indices
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start_sample = int(start_time * sample_rate)
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end_sample = int(end_time * sample_rate)
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# Extract the selected audio segment
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audio_segment = waveform[:, start_sample:end_sample]
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# Calculate the number of frames based on FPS and segment duration
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duration_seconds = end_time - start_time
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frames_count = int(duration_seconds * fps)
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audio_output = (audio_segment.cpu().numpy(), sample_rate)
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print("-_-_-_-_-_-_-_-_-_-")
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print(f"BPM: {bpm}")
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print(f"Start Time: {start_time}")
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print(f"End Time: {end_time}")
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print(f"Frame Count: {frames_count}")
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print("-_-_-_-_-_-_-_-_-_-")
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return (audio_output, frames_count, bpm, fps)
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```
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