Rename app.py to index.html

app.py

@@ -1,205 +0,0 @@
-import os
-import torch
-import shutil
-import librosa
-import warnings
-import numpy as np
-import gradio as gr
-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,
-    _L,
-    SAMPLE_RATE,
-    TEMP_DIR,
-    TRANSLATE,
-    CLASSES,
-    EN_US,
-)
-
-
-def circular_padding(spec: np.ndarray, end: int):
-    size = len(spec)
-    if end <= size:
-        return spec
-
-    num_padding = end - size
-    num_repeat = num_padding // size + int(num_padding % size != 0)
-    padding = np.tile(spec, num_repeat)
-    return np.concatenate((spec, padding))[:end]
-
-
-def wav2mel(audio_path: str, width=3):
-    y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
-    total_frames = len(y)
-    if total_frames % (width * sr) != 0:
-        count = total_frames // (width * sr) + 1
-        y = circular_padding(y, count * width * sr)
-
-    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"{TEMP_DIR}/{i}.jpg",
-            bbox_inches="tight",
-            pad_inches=0.0,
-        )
-        plt.close()
-
-
-def wav2cqt(audio_path: str, width=3):
-    y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
-    total_frames = len(y)
-    if total_frames % (width * sr) != 0:
-        count = total_frames // (width * sr) + 1
-        y = circular_padding(y, count * width * sr)
-
-    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"{TEMP_DIR}/{i}.jpg",
-            bbox_inches="tight",
-            pad_inches=0.0,
-        )
-        plt.close()
-
-
-def wav2chroma(audio_path: str, width=3):
-    y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
-    total_frames = len(y)
-    if total_frames % (width * sr) != 0:
-        count = total_frames // (width * sr) + 1
-        y = circular_padding(y, count * width * sr)
-
-    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"{TEMP_DIR}/{i}.jpg",
-            bbox_inches="tight",
-            pad_inches=0.0,
-        )
-        plt.close()
-
-
-def most_frequent_value(lst: list):
-    counter = Counter(lst)
-    max_count = max(counter.values())
-    for element, count in counter.items():
-        if count == max_count:
-            return element
-
-    return None
-
-
-def infer(wav_path: str, log_name: str, folder_path=TEMP_DIR):
-    status = "Success"
-    filename = result = None
-    try:
-        if os.path.exists(folder_path):
-            shutil.rmtree(folder_path)
-
-        if not wav_path:
-            raise ValueError("请输入音频!")
-
-        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])
-
-        pred_id = most_frequent_value(preds)
-        filename = os.path.basename(wav_path)
-        result = (
-            CLASSES[pred_id].capitalize()
-            if EN_US
-            else f"{TRANSLATE[CLASSES[pred_id]]} ({CLASSES[pred_id].capitalize()})"
-        )
-
-    except Exception as e:
-        status = f"{e}"
-
-    return status, filename, result
-
-
-if __name__ == "__main__":
-    warnings.filterwarnings("ignore")
-    models = get_modelist(assign_model="vit_l_16_mel")
-    examples = []
-    example_wavs = find_files()
-    for wav in example_wavs:
-        examples.append([wav, models[0]])
-
-    with gr.Blocks() as demo:
-        gr.Interface(
-            fn=infer,
-            inputs=[
-                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=_L("状态栏"), buttons=["copy"]),
-                gr.Textbox(label=_L("音频文件名"), buttons=["copy"]),
-                gr.Textbox(label=_L("古筝演奏技法识别"), buttons=["copy"]),
-            ],
-            examples=examples,
-            cache_examples=False,
-            flagging_mode="never",
-            title=_L("建议录音时长保持在 3s 左右"),
-        )
-
-        gr.Markdown(f"# {_L('引用')}" + """
-            ```bibtex
-            @article{Zhou-2025,
-                author  = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
-                title   = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
-                journal = {Transactions of the International Society for Music Information Retrieval},
-                volume  = {8},
-                number  = {1},
-                pages   = {22--38},
-                month   = {Mar},
-                year    = {2025},
-                url     = {https://doi.org/10.5334/tismir.194},
-                doi     = {10.5334/tismir.194}
-            }
-            ```""")
-
-    demo.launch(
-        theme=gr.themes.Ocean(),
-        css="#gradio-share-link-button-0 { display: none; }",
-        ssr_mode=False,
-    )

index.html

@@ -0,0 +1,18 @@
+<html>
+	<head>
+		<style>
+            html, body {
+                margin: 0;
+                padding: 0;
+            }
+            iframe {
+                width: 100%;
+                height: 100%;
+                border: none;
+            }
+        </style>
+	</head>
+	<body>
+        <iframe src="https://ccmusic-database-gz-isotech.ms.show"></iframe>
+	</body>
+</html>