Update app.py

app.py

@@ -1,176 +1,176 @@
-import os
-import torch
-import random
-import shutil
-import librosa
-import warnings
-import numpy as np
-import gradio as gr
-import librosa.display
-import matplotlib.pyplot as plt
-import torchvision.transforms as transforms
-from utils import get_modelist, find_wav_files
-from collections import Counter
-from model import EvalNet
-from PIL import Image
-
-
-TRANSLATE = {
-    "m_chest": "男真声",
-    "f_chest": "女真声",
-    "m_falsetto": "男假声",
-    "f_falsetto": "女假声",
-}
-CLASSES = list(TRANSLATE.keys())
-
-
-def most_common_element(input_list):
-    # 使用 Counter 统计每个元素的出现次数
-    counter = Counter(input_list)
-    # 使用 most_common 方法获取出现次数最多的元素
-    most_common_element, _ = counter.most_common(1)[0]
-    return most_common_element
-
-
-def wav_to_mel(audio_path: str, width=0.07):
-    os.makedirs("./tmp", exist_ok=True)
-    try:
-        y, sr = librosa.load(audio_path, sr=48000)
-        mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)
-        log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
-        dur = librosa.get_duration(y=y, sr=sr)
-        total_frames = log_mel_spec.shape[1]
-        step = int(width * total_frames / dur)
-        count = int(total_frames / step)
-        begin = int(0.5 * (total_frames - count * step))
-        end = begin + step * count
-        for i in range(begin, end, step):
-            librosa.display.specshow(log_mel_spec[:, i : i + step])
-            plt.axis("off")
-            plt.savefig(
-                f"./tmp/mel_{round(dur, 2)}_{i}.jpg",
-                bbox_inches="tight",
-                pad_inches=0.0,
-            )
-            plt.close()
-
-    except Exception as e:
-        print(f"Error converting {audio_path} : {e}")
-
-
-def wav_to_cqt(audio_path: str, width=0.07):
-    os.makedirs("./tmp", exist_ok=True)
-    try:
-        y, sr = librosa.load(audio_path, sr=48000)
-        cqt_spec = librosa.cqt(y=y, sr=sr)
-        log_cqt_spec = librosa.power_to_db(np.abs(cqt_spec) ** 2, ref=np.max)
-        dur = librosa.get_duration(y=y, sr=sr)
-        total_frames = log_cqt_spec.shape[1]
-        step = int(width * total_frames / dur)
-        count = int(total_frames / step)
-        begin = int(0.5 * (total_frames - count * step))
-        end = begin + step * count
-        for i in range(begin, end, step):
-            librosa.display.specshow(log_cqt_spec[:, i : i + step])
-            plt.axis("off")
-            plt.savefig(
-                f"./tmp/cqt_{round(dur, 2)}_{i}.jpg",
-                bbox_inches="tight",
-                pad_inches=0.0,
-            )
-            plt.close()
-
-    except Exception as e:
-        print(f"Error converting {audio_path} : {e}")
-
-
-def wav_to_chroma(audio_path: str, width=0.07):
-    os.makedirs("./tmp", exist_ok=True)
-    try:
-        y, sr = librosa.load(audio_path, sr=48000)
-        chroma_spec = librosa.feature.chroma_stft(y=y, sr=sr)
-        log_chroma_spec = librosa.power_to_db(np.abs(chroma_spec) ** 2, ref=np.max)
-        dur = librosa.get_duration(y=y, sr=sr)
-        total_frames = log_chroma_spec.shape[1]
-        step = int(width * total_frames / dur)
-        count = int(total_frames / step)
-        begin = int(0.5 * (total_frames - count * step))
-        end = begin + step * count
-        for i in range(begin, end, step):
-            librosa.display.specshow(log_chroma_spec[:, i : i + step])
-            plt.axis("off")
-            plt.savefig(
-                f"./tmp/chroma_{round(dur, 2)}_{i}.jpg",
-                bbox_inches="tight",
-                pad_inches=0.0,
-            )
-            plt.close()
-
-    except Exception as e:
-        print(f"Error converting {audio_path} : {e}")
-
-
-def embed_img(img_path, input_size=224):
-    transform = transforms.Compose(
-        [
-            transforms.Resize([input_size, input_size]),
-            transforms.ToTensor(),
-            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
-        ]
-    )
-    img = Image.open(img_path).convert("RGB")
-    return transform(img).unsqueeze(0)
-
-
-def inference(wav_path, log_name: str, folder_path="./tmp"):
-    if os.path.exists(folder_path):
-        shutil.rmtree(folder_path)
-
-    if not wav_path:
-        wav_path = "./examples/m_falsetto.wav"
-
-    model = EvalNet(log_name).model
-    spec = log_name.split("_")[-1]
-    eval("wav_to_%s" % spec)(wav_path)
-    outputs = []
-    all_files = os.listdir(folder_path)
-    for file_name in all_files:
-        if file_name.lower().endswith(".jpg"):
-            file_path = os.path.join(folder_path, file_name)
-            input = embed_img(file_path)
-            output = model(input)
-            pred_id = torch.max(output.data, 1)[1]
-            outputs.append(pred_id)
-
-    max_count_item = most_common_element(outputs)
-    shutil.rmtree(folder_path)
-    return os.path.basename(wav_path), TRANSLATE[CLASSES[max_count_item]]
-
-
-if __name__ == "__main__":
-    warnings.filterwarnings("ignore")
-
-    models = get_modelist()
-    examples = []
-    example_wavs = find_wav_files()
-    model_num = len(models)
-    for wav in example_wavs:
-        examples.append([wav, models[random.randint(0, model_num - 1)]])
-
-    with gr.Blocks() as demo:
-        gr.Interface(
-            fn=inference,
-            inputs=[
-                gr.Audio(label="上传录音", type="filepath"),
-                gr.Dropdown(choices=models, label="选择模型", value=models[0]),
-            ],
-            outputs=[
-                gr.Textbox(label="音频文件名", show_copy_button=True),
-                gr.Textbox(label="唱法识别", show_copy_button=True),
-            ],
-            examples=examples,
-            allow_flagging="never",
-            title="建议录音时长保持在 5s 左右, 过长会影响识别效率",
-        )
-
-    demo.launch()
+import os
+import torch
+import random
+import shutil
+import librosa
+import warnings
+import numpy as np
+import gradio as gr
+import librosa.display
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+from utils import get_modelist, find_wav_files
+from collections import Counter
+from model import EvalNet
+from PIL import Image
+
+
+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())
+
+
+def most_common_element(input_list):
+    # 使用 Counter 统计每个元素的出现次数
+    counter = Counter(input_list)
+    # 使用 most_common 方法获取出现次数最多的元素
+    most_common_element, _ = counter.most_common(1)[0]
+    return most_common_element
+
+
+def wav_to_mel(audio_path: str, width=0.07):
+    os.makedirs("./tmp", exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=48000)
+        mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)
+        log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
+        dur = librosa.get_duration(y=y, sr=sr)
+        total_frames = log_mel_spec.shape[1]
+        step = int(width * total_frames / dur)
+        count = int(total_frames / step)
+        begin = int(0.5 * (total_frames - count * step))
+        end = begin + step * count
+        for i in range(begin, end, step):
+            librosa.display.specshow(log_mel_spec[:, i : i + step])
+            plt.axis("off")
+            plt.savefig(
+                f"./tmp/mel_{round(dur, 2)}_{i}.jpg",
+                bbox_inches="tight",
+                pad_inches=0.0,
+            )
+            plt.close()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+def wav_to_cqt(audio_path: str, width=0.07):
+    os.makedirs("./tmp", exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=48000)
+        cqt_spec = librosa.cqt(y=y, sr=sr)
+        log_cqt_spec = librosa.power_to_db(np.abs(cqt_spec) ** 2, ref=np.max)
+        dur = librosa.get_duration(y=y, sr=sr)
+        total_frames = log_cqt_spec.shape[1]
+        step = int(width * total_frames / dur)
+        count = int(total_frames / step)
+        begin = int(0.5 * (total_frames - count * step))
+        end = begin + step * count
+        for i in range(begin, end, step):
+            librosa.display.specshow(log_cqt_spec[:, i : i + step])
+            plt.axis("off")
+            plt.savefig(
+                f"./tmp/cqt_{round(dur, 2)}_{i}.jpg",
+                bbox_inches="tight",
+                pad_inches=0.0,
+            )
+            plt.close()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+def wav_to_chroma(audio_path: str, width=0.07):
+    os.makedirs("./tmp", exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=48000)
+        chroma_spec = librosa.feature.chroma_stft(y=y, sr=sr)
+        log_chroma_spec = librosa.power_to_db(np.abs(chroma_spec) ** 2, ref=np.max)
+        dur = librosa.get_duration(y=y, sr=sr)
+        total_frames = log_chroma_spec.shape[1]
+        step = int(width * total_frames / dur)
+        count = int(total_frames / step)
+        begin = int(0.5 * (total_frames - count * step))
+        end = begin + step * count
+        for i in range(begin, end, step):
+            librosa.display.specshow(log_chroma_spec[:, i : i + step])
+            plt.axis("off")
+            plt.savefig(
+                f"./tmp/chroma_{round(dur, 2)}_{i}.jpg",
+                bbox_inches="tight",
+                pad_inches=0.0,
+            )
+            plt.close()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+def embed_img(img_path, input_size=224):
+    transform = transforms.Compose(
+        [
+            transforms.Resize([input_size, input_size]),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+        ]
+    )
+    img = Image.open(img_path).convert("RGB")
+    return transform(img).unsqueeze(0)
+
+
+def inference(wav_path, log_name: str, folder_path="./tmp"):
+    if os.path.exists(folder_path):
+        shutil.rmtree(folder_path)
+
+    if not wav_path:
+        wav_path = "./examples/m_falsetto.wav"
+
+    model = EvalNet(log_name).model
+    spec = log_name.split("_")[-1]
+    eval("wav_to_%s" % spec)(wav_path)
+    outputs = []
+    all_files = os.listdir(folder_path)
+    for file_name in all_files:
+        if file_name.lower().endswith(".jpg"):
+            file_path = os.path.join(folder_path, file_name)
+            input = embed_img(file_path)
+            output = model(input)
+            pred_id = torch.max(output.data, 1)[1]
+            outputs.append(pred_id)
+
+    max_count_item = most_common_element(outputs)
+    shutil.rmtree(folder_path)
+    return os.path.basename(wav_path), TRANSLATE[CLASSES[max_count_item]]
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings("ignore")
+
+    models = get_modelist()
+    examples = []
+    example_wavs = find_wav_files()
+    model_num = len(models)
+    for wav in example_wavs:
+        examples.append([wav, models[random.randint(0, model_num - 1)]])
+
+    with gr.Blocks() as demo:
+        gr.Interface(
+            fn=inference,
+            inputs=[
+                gr.Audio(label="Upload a recording", type="filepath"),
+                gr.Dropdown(choices=models, label="Select a model", value=models[0]),
+            ],
+            outputs=[
+                gr.Textbox(label="Audio filename", show_copy_button=True),
+                gr.Textbox(label="Singing method recognition", show_copy_button=True),
+            ],
+            examples=examples,
+            allow_flagging="never",
+            title="It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
+        )
+
+    demo.launch()