From cbe1fc951de521111b17fa54a259ec8250f0714a Mon Sep 17 00:00:00 2001
From: wangbo <wangbolhr@163.com>
Date: Thu, 15 May 2025 20:23:06 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E8=8A=82=E7=82=B9?=
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---
 nodes.py         | 20 ++++------
 requirements.txt |  1 +
 test.py          | 99 ++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 108 insertions(+), 12 deletions(-)
 create mode 100644 test.py

diff --git a/nodes.py b/nodes.py
index 30b6843..ea2f8fa 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,7 +1,6 @@
-import soundfile as sf
 import requests
 import io
-import numpy as np
+import librosa.core as core
 import torch
 
 class AudioLoadPath:
@@ -13,7 +12,7 @@ class AudioLoadPath:
                               "duration": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1e6, "step": 0.001})}}
 
     RETURN_TYPES = ("AUDIO", )
-    CATEGORY = "Audio Reactor"
+    CATEGORY = "EasyAI"
     FUNCTION = "load"
 
     def load(self, path: str, sample_rate: int, offset: float, duration: float|None):
@@ -27,20 +26,17 @@ class AudioLoadPath:
                 response.raise_for_status()
                 audio_data = io.BytesIO(response.content)
 
-                # 使用 soundfile 从内存中读取音频数据
-                audio, file_sr = sf.read(audio_data)
-
-                # 如果需要重采样
-                if file_sr != sample_rate:
-                    # 这里需要添加重采样逻辑
-                    # 可以使用 librosa.resample 或其他方法
-                    pass
+                # 使用 librosa 直接从内存中读取音频数据
+                import warnings
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore")
+                    audio, _ = core.load(audio_data, sr=sample_rate, offset=offset, duration=duration)
 
             except Exception as e:
                 raise Exception(f"加载网络音频失败: {str(e)}")
         else:
             # 本地文件使用原有的 librosa 方式加载
-            audio, _ = librosa.load(path, sr=sample_rate, offset=offset, duration=duration)
+            audio, _ = core.load(path, sr=sample_rate, offset=offset, duration=duration)
 
         # 转换为 torch tensor 并调整维度
         audio = torch.from_numpy(audio)[None,:,None]
diff --git a/requirements.txt b/requirements.txt
index e69de29..c64d38a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1 @@
+numpy~=2.2.5
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..7ca175e
--- /dev/null
+++ b/test.py
@@ -0,0 +1,99 @@
+import unittest
+import torch
+import os
+import tempfile
+import soundfile as sf
+import numpy as np
+from nodes import AudioLoadPath
+
+class TestAudioLoadPath(unittest.TestCase):
+    def setUp(self):
+        # 创建一个临时的测试音频文件
+        self.temp_dir = tempfile.mkdtemp()
+        self.local_audio_path = os.path.join(self.temp_dir, "test_audio.wav")
+
+        # 生成一个简单的测试音频信号
+        sample_rate = 22050
+        duration = 2.0  # 2秒
+        t = np.linspace(0, duration, int(sample_rate * duration))
+        audio_data = np.sin(2 * np.pi * 440 * t)  # 440Hz的正弦波
+        sf.write(self.local_audio_path, audio_data, sample_rate)
+
+        # 初始化测试类
+        self.audio_loader = AudioLoadPath()
+
+        # 一个可用的测试音频URL（请替换为实际可用的URL）
+        self.test_url = "https://wangbo0808.oss-cn-shanghai.aliyuncs.com/%E5%B0%8F%E7%8C%B4%E5%AD%90%E4%B8%8B%E5%B1%B1.mp3"
+
+    def test_local_file_loading(self):
+        # 测试本地文件加载
+        sample_rate = 22050
+        audio_tensor = self.audio_loader.load(
+            path=self.local_audio_path,
+            sample_rate=sample_rate,
+            offset=0.0,
+            duration=1.0
+        )[0]
+
+        # 验证返回的张量格式和维度
+        self.assertIsInstance(audio_tensor, torch.Tensor)
+        self.assertEqual(len(audio_tensor.shape), 3)  # [1, samples, 1]
+        self.assertEqual(audio_tensor.shape[0], 1)
+        self.assertEqual(audio_tensor.shape[2], 1)
+
+        # 验证采样率转换
+        expected_samples = int(sample_rate * 1.0)  # 1秒的音频
+        self.assertEqual(audio_tensor.shape[1], expected_samples)
+
+    def test_url_loading(self):
+        # 测试网络文件加载
+        try:
+            audio_tensor = self.audio_loader.load(
+                path=self.test_url,
+                sample_rate=22050,
+                offset=0.0,
+                duration=0.0
+            )[0]
+
+            # 验证返回的张量格式和维度
+            self.assertIsInstance(audio_tensor, torch.Tensor)
+            self.assertEqual(len(audio_tensor.shape), 3)
+
+        except Exception as e:
+            # 如果测试URL不可用，这个测试可能会失败
+            print(f"URL加载测试失败: {str(e)}")
+
+    def test_invalid_path(self):
+        # 测试无效路径
+        with self.assertRaises(Exception):
+            self.audio_loader.load(
+                path="nonexistent_file.wav",
+                sample_rate=22050,
+                offset=0.0,
+                duration=0.0
+            )
+
+    def test_duration_and_offset(self):
+        # 测试偏移和持续时间参数
+        sample_rate = 22050
+        offset = 0.5
+        duration = 1.0
+
+        audio_tensor = self.audio_loader.load(
+            path=self.local_audio_path,
+            sample_rate=sample_rate,
+            offset=offset,
+            duration=duration
+        )[0]
+
+        # 验证音频长度
+        expected_samples = int(sample_rate * duration)
+        self.assertEqual(audio_tensor.shape[1], expected_samples)
+
+    def tearDown(self):
+        # 清理临时文件
+        import shutil
+        shutil.rmtree(self.temp_dir)
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file