123 lines
3.7 KiB
Markdown
123 lines
3.7 KiB
Markdown
|
|
# Documentation
|
|
- Class name: SaltAudioVisualizer
|
|
- Category: SALT/Audio/Util
|
|
- Output node: True
|
|
|
|
SaltAudioVisualizer节点旨在创建音频数据的视觉表示。它处理音频输入以生成可用于分析或美学目的的可视化效果，突出显示音频的结构、频率内容和动态特性。
|
|
|
|
# Input types
|
|
## Required
|
|
- audio
|
|
- 'audio'参数是可视化过程的主要输入，代表要进行可视化的音频数据。
|
|
- Comfy dtype: AUDIO
|
|
- Python dtype: bytes
|
|
- frame_rate
|
|
- 'frame_rate'参数指定可视化的帧率，影响生成的视觉输出的时间分辨率。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
## Optional
|
|
- start_frame
|
|
- 'start_frame'参数定义要可视化的音频片段的起始点，允许选择性地可视化音频的特定部分。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
- end_frame
|
|
- 'end_frame'参数确定要可视化的音频片段的结束点，能够自定义可视化的长度。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
# Output types
|
|
该节点没有输出类型
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `CPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
class SaltAudioVisualizer:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"audio": ("AUDIO", {}),
|
|
"frame_rate": ("INT", {"default": 8, "min": 1, "max": 244}),
|
|
},
|
|
"optional": {
|
|
"start_frame": ("INT", {"min": 0, "default": 0}),
|
|
"end_frame": ("INT", {"min": 0, "default": -1}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
RETURN_NAMES = ()
|
|
OUTPUT_NODE = True
|
|
|
|
FUNCTION = "visualize_audio"
|
|
CATEGORY = "SALT/Audio/Util"
|
|
|
|
def visualize_audio(self, audio, frame_rate, start_frame=0, end_frame=-1):
|
|
TEMP = folder_paths.get_temp_directory()
|
|
os.makedirs(TEMP, exist_ok=True)
|
|
|
|
audio_segment = AudioSegment.from_file(io.BytesIO(audio), format="wav", dir=TEMP)
|
|
|
|
frame_duration_ms = 1000 / frame_rate
|
|
start_ms = start_frame * frame_duration_ms
|
|
end_ms = end_frame * frame_duration_ms if end_frame != -1 else len(audio_segment)
|
|
|
|
relevant_audio_segment = audio_segment[start_ms:end_ms]
|
|
|
|
samples = np.array(relevant_audio_segment.get_array_of_samples())
|
|
if relevant_audio_segment.channels == 2:
|
|
samples = samples.reshape((-1, 2))
|
|
samples = samples.sum(axis=1) / 2
|
|
|
|
max_val = max(abs(samples.min()), samples.max())
|
|
normalized_samples = samples / max_val
|
|
|
|
total_frames = len(normalized_samples) / (audio_segment.frame_rate / frame_rate)
|
|
frame_numbers = np.linspace(start_frame, start_frame + total_frames, num=len(normalized_samples), endpoint=False)
|
|
|
|
plt.figure(figsize=(10, 4))
|
|
plt.plot(frame_numbers, normalized_samples, linewidth=0.5)
|
|
plt.title("Audio Visualization")
|
|
plt.ylim(-1, 1)
|
|
plt.xlabel("Frame")
|
|
plt.ylabel("Amplitude")
|
|
|
|
filename = str(uuid.uuid4())+"_visualization.png"
|
|
file_path = os.path.join(TEMP, filename)
|
|
plt.savefig(file_path)
|
|
plt.close()
|
|
|
|
ui_output = {
|
|
"ui": {
|
|
"images": [
|
|
{
|
|
"filename": filename,
|
|
"subfolder": "",
|
|
"type": "temp"
|
|
}
|
|
]
|
|
}
|
|
}
|
|
|
|
return ui_output
|
|
|
|
@staticmethod
|
|
def gen_hash(input_dict):
|
|
sorted_json = json.dumps(input_dict, sort_keys=True)
|
|
hash_obj = hashlib.sha256()
|
|
hash_obj.update(sorted_json.encode('utf-8'))
|
|
return hash_obj.hexdigest()
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, **kwargs):
|
|
return cls.gen_hash(kwargs)
|
|
|
|
```
|