sync ms

app.py

@@ -9,18 +9,16 @@ import librosa.display
 import matplotlib.pyplot as plt
 from collections import Counter
 from model import EvalNet
-from utils import get_modelist, find_files, embed_img
-
-
-TRANSLATE = {
-    "m_chest": "Chest Voice, Male",
-    "f_chest": "Chest Voice, Female",
-    "m_falsetto": "Falsetto Voice, Male",
-    "f_falsetto": "Falsetto Voice, Female",
-}
-CLASSES = list(TRANSLATE.keys())
-TEMP_DIR = "./__pycache__/tmp"
-SAMPLE_RATE = 22050
+from utils import (
+    get_modelist,
+    find_files,
+    embed_img,
+    _L,
+    SAMPLE_RATE,
+    TEMP_DIR,
+    TRANSLATE,
+    CLASSES,
+)
 
 
 def wav2mel(audio_path: str, width=0.496145124716553):
@@ -97,35 +95,39 @@ def most_frequent_value(lst: list):
 
 
 def infer(wav_path: str, log_name: str, folder_path=TEMP_DIR):
-    if os.path.exists(folder_path):
-        shutil.rmtree(folder_path)
+    status = "Success"
+    filename = result = None
+    try:
+        if os.path.exists(folder_path):
+            shutil.rmtree(folder_path)
 
-    if not wav_path:
-        return None, "Please input an audio!"
+        if not wav_path:
+            raise ValueError("请输入音频!")
 
-    spec = log_name.split("_")[-3]
-    os.makedirs(folder_path, exist_ok=True)
-    try:
+        spec = log_name.split("_")[-3]
+        os.makedirs(folder_path, exist_ok=True)
         model = EvalNet(log_name, len(TRANSLATE)).model
         eval("wav2%s" % spec)(wav_path)
+        jpgs = find_files(folder_path, ".jpg")
+        preds = []
+        for jpg in jpgs:
+            input = embed_img(jpg)
+            output: torch.Tensor = model(input)
+            preds.append(torch.max(output.data, 1)[1])
 
-    except Exception as e:
-        return None, f"{e}"
+        pred_id = most_frequent_value(preds)
+        filename = os.path.basename(wav_path)
+        result = TRANSLATE[CLASSES[pred_id]]
 
-    jpgs = find_files(folder_path, ".jpg")
-    preds = []
-    for jpg in jpgs:
-        input = embed_img(jpg)
-        output: torch.Tensor = model(input)
-        preds.append(torch.max(output.data, 1)[1])
+    except Exception as e:
+        status = f"{e}"
 
-    pred_id = most_frequent_value(preds)
-    return os.path.basename(wav_path), TRANSLATE[CLASSES[pred_id]]
+    return status, filename, result
 
 
 if __name__ == "__main__":
     warnings.filterwarnings("ignore")
-    models = get_modelist(assign_model="AlexNet_mel")
+    models = get_modelist(assign_model="alexnet_mel")
     examples = []
     example_wavs = find_files()
     for wav in example_wavs:
@@ -135,34 +137,35 @@ if __name__ == "__main__":
         gr.Interface(
             fn=infer,
             inputs=[
-                gr.Audio(label="Upload a recording", type="filepath"),
-                gr.Dropdown(choices=models, label="Select a model", value=models[0]),
+                gr.Audio(label=_L("上传录音"), type="filepath"),
+                gr.Dropdown(choices=models, label=_L("选择模型"), value=models[0]),
             ],
             outputs=[
-                gr.Textbox(label="Audio filename", show_copy_button=True),
-                gr.Textbox(label="Singing method recognition", show_copy_button=True),
+                gr.Textbox(label=_L("状态栏"), show_copy_button=True),
+                gr.Textbox(label=_L("音频文件名"), show_copy_button=True),
+                gr.Textbox(label=_L("唱法识别"), show_copy_button=True),
             ],
             examples=examples,
             cache_examples=False,
             allow_flagging="never",
-            title="It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
+            title=_L("建议录音时长保持在 5s 左右, 过长会影响识别效率"),
         )
 
         gr.Markdown(
-            """
-# Cite
-```bibtex
-@dataset{zhaorui_liu_2021_5676893,
-  author    = {Zhaorui Liu and Zijin Li},
-  title     = {Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)},
-  month     = nov,
-  year      = 2021,
-  publisher = {Zenodo},
-  version   = {1.1},
-  doi       = {10.5281/zenodo.5676893},
-  url       = {https://doi.org/10.5281/zenodo.5676893}
-}
-```"""
+            f"# {_L('引用')}"
+            + """
+            ```bibtex
+            @dataset{zhaorui_liu_2021_5676893,
+                author    = {Zhaorui Liu and Zijin Li},
+                title     = {Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)},
+                month     = nov,
+                year      = 2021,
+                publisher = {Zenodo},
+                version   = {1.1},
+                doi       = {10.5281/zenodo.5676893},
+                url       = {https://doi.org/10.5281/zenodo.5676893}
+            }
+            ```"""
         )
 
     demo.launch()

model.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 import torchvision.models as models
-from datasets import load_dataset
+from modelscope.msdatasets import MsDataset
 from utils import MODEL_DIR
 
 
@@ -17,7 +17,7 @@ class EvalNet:
         self.m_type, self.input_size = self._model_info(m_ver)
 
         if not hasattr(models, m_ver):
-            raise Exception("Unsupported model.")
+            raise Exception("不支持的模型")
 
         self.model = eval("models.%s()" % m_ver)
         linear_output = self._set_outsize()
@@ -34,11 +34,15 @@ class EvalNet:
             if ver == bb["ver"]:
                 return bb
 
-        print("Backbone name not found, using default option - alexnet.")
+        print("未找到骨干网络名称，使用默认选项 - alexnet")
         return backbone_list[0]
 
     def _model_info(self, m_ver: str):
-        backbone_list = load_dataset("monetjoe/cv_backbones", split="train")
+        backbone_list = MsDataset.load(
+            "monetjoe/cv_backbones",
+            split="v1",
+            trust_remote_code=True,
+        )
         backbone = self._get_backbone(m_ver, backbone_list)
         m_type = str(backbone["type"])
         input_size = int(backbone["input_size"])

requirements.txt

@@ -1,5 +1,7 @@
-torch
-pillow
+torch==2.6.0+cu118
+-f https://download.pytorch.org/whl/torch
+torchvision==0.21.0+cu118
+-f https://download.pytorch.org/whl/torchvision
 librosa
 matplotlib
-torchvision
+modelscope[framework]==1.21.0

utils.py

@@ -1,15 +1,55 @@
 import os
 import torch
 import torchvision.transforms as transforms
-from huggingface_hub import snapshot_download
+import huggingface_hub
+import modelscope
 from PIL import Image
 
-MODEL_DIR = snapshot_download(
-    "ccmusic-database/chest_falsetto",
-    cache_dir="./__pycache__",
+EN_US = os.getenv("LANG") != "zh_CN.UTF-8"
+
+ZH2EN = {
+    "上传录音": "Upload a recording",
+    "选择模型": "Select a model",
+    "状态栏": "Status",
+    "音频文件名": "Audio filename",
+    "唱法识别": "Singing method recognition",
+    "建议录音时长保持在 5s 左右, 过长会影响识别效率": "It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
+    "引用": "Cite",
+    "男真声": "Chest Voice, Male",
+    "女真声": "Chest Voice, Female",
+    "男假声": "Falsetto Voice, Male",
+    "女假声": "Falsetto Voice, Female",
+}
+
+
+MODEL_DIR = (
+    huggingface_hub.snapshot_download(
+        "ccmusic-database/chest_falsetto",
+        cache_dir="./__pycache__",
+    )
+    if EN_US
+    else modelscope.snapshot_download(
+        "ccmusic-database/chest_falsetto",
+        cache_dir="./__pycache__",
+    )
 )
 
 
+def _L(zh_txt: str):
+    return ZH2EN[zh_txt] if EN_US else zh_txt
+
+
+TRANSLATE = {
+    "m_chest": _L("男真声"),
+    "f_chest": _L("女真声"),
+    "m_falsetto": _L("男假声"),
+    "f_falsetto": _L("女假声"),
+}
+CLASSES = list(TRANSLATE.keys())
+TEMP_DIR = "./__pycache__/tmp"
+SAMPLE_RATE = 22050
+
+
 def toCUDA(x):
     if hasattr(x, "cuda"):
         if torch.cuda.is_available():
@@ -30,19 +70,16 @@ def find_files(folder_path=f"{MODEL_DIR}/examples", ext=".wav"):
 
 
 def get_modelist(model_dir=MODEL_DIR, assign_model=""):
-    try:
-        entries = os.listdir(model_dir)
-    except OSError as e:
-        print(f"Cannot access {model_dir}: {e}")
-        return
-
     output = []
-    for entry in entries:
+    for entry in os.listdir(model_dir):
+        # 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"Skip .git / examples dir: {full_path}")
+            print(f"跳过 .git 或 examples 文件夹: {full_path}")
             continue
 
+        # 检查条目是文件还是目录
         if os.path.isdir(full_path):
             model = os.path.basename(full_path)
             if assign_model and assign_model.lower() in model: