2 pure en

app.py

@@ -8,18 +8,12 @@ import numpy as np
 import gradio as gr
 import librosa.display
 import matplotlib.pyplot as plt
-from utils import get_modelist, find_audio_files, embed_img, TEMP_DIR
+from utils import get_modelist, find_audio_files, embed_img
 from model import EvalNet
 
 
-TRANSLATE = {
-    "Gong": "宫",
-    "Shang": "商",
-    "Jue": "角",
-    "Zhi": "徵",
-    "Yu": "羽",
-}
-CLASSES = list(TRANSLATE.keys())
+CLASSES = ["Gong", "Shang", "Jue", "Zhi", "Yu"]
+TEMP_DIR = "./__pycache__/tmp"
 SAMPLE_RATE = 44100
 
 
@@ -99,10 +93,10 @@ def infer(wav_path: str, log_name: str, folder_path=TEMP_DIR):
         shutil.rmtree(folder_path)
 
     if not wav_path:
-        return None, "请输入音频 Please input an audio!"
+        return None, "Please input an audio!"
 
     try:
-        model = EvalNet(log_name, len(TRANSLATE)).model
+        model = EvalNet(log_name, len(CLASSES)).model
     except Exception as e:
         return None, f"{e}"
 
@@ -113,7 +107,7 @@ def infer(wav_path: str, log_name: str, folder_path=TEMP_DIR):
     pred_id = torch.max(output.data, 1)[1]
     return (
         os.path.basename(wav_path),
-        f"{TRANSLATE[CLASSES[pred_id]]} ({CLASSES[pred_id].capitalize()})",
+        CLASSES[pred_id].capitalize(),
     )
 
 
@@ -130,27 +124,25 @@ if __name__ == "__main__":
         gr.Interface(
             fn=infer,
             inputs=[
-                gr.Audio(label="上传录音 Upload a recording", type="filepath"),
-                gr.Dropdown(
-                    choices=models, label="选择模型 Select a model", value=models[0]
-                ),
+                gr.Audio(label="Upload a recording", type="filepath"),
+                gr.Dropdown(choices=models, label="Select a model", value=models[0]),
             ],
             outputs=[
-                gr.Textbox(label="音频文件名 Audio filename", show_copy_button=True),
+                gr.Textbox(label="Audio filename", show_copy_button=True),
                 gr.Textbox(
-                    label="中国五声调式识别 Chinese pentatonic mode recognition",
+                    label="Chinese pentatonic mode recognition",
                     show_copy_button=True,
                 ),
             ],
             examples=examples,
             cache_examples=False,
             flagging_mode="never",
-            title="建议录音时长保持在 20s 左右<br>It is recommended to keep the recording length around 20s.",
+            title="It is recommended to keep the recording length around 20s.",
         )
 
         gr.Markdown(
             """
-# 引用 Cite
+# Cite
 ```bibtex
 @dataset{zhaorui_liu_2021_5676893,
   author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},

requirements.txt

@@ -1,6 +1,6 @@
-librosa
 torch
+pillow
+librosa
 matplotlib
 torchvision
-pillow
-modelscope==1.15
+modelscope[framework]==1.18

utils.py

@@ -5,10 +5,9 @@ from modelscope import snapshot_download
 from PIL import Image
 
 MODEL_DIR = snapshot_download(
-    f"ccmusic-database/CNPM",
-    cache_dir=f"{os.getcwd()}/__pycache__",
+    "ccmusic-database/CNPM",
+    cache_dir="./__pycache__",
 )
-TEMP_DIR = f"{os.getcwd()}/flagged"
 
 
 def toCUDA(x):
@@ -34,22 +33,17 @@ def get_modelist(model_dir=MODEL_DIR):
     try:
         entries = os.listdir(model_dir)
     except OSError as e:
-        print(f"无法访问 {model_dir}: {e}")
+        print(f"Cannot access {model_dir}: {e}")
         return
 
-    # 遍历所有条目
     output = []
     for entry in entries:
-        # 获取完整路径
         full_path = os.path.join(model_dir, entry)
-        # 跳过'.git'文件夹
         if entry == ".git" or entry == "examples":
-            print(f"跳过 .git 或 examples 文件夹: {full_path}")
+            print(f"Skip .git or examples dir: {full_path}")
             continue
 
-        # 检查条目是文件还是目录
         if os.path.isdir(full_path):
-            # 打印目录路径
             output.append(os.path.basename(full_path))
 
     return output