upl base

.gitattributes

@@ -1,35 +1,35 @@
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text
 *.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
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 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*.tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db* filter=lfs diff=lfs merge=lfs -text
+*.ark* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text
+**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

.gitignore

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+*.pt
+__pycache__/*
+tmp/*
+flagged/*
+test.py
+rename.sh

README.md

@@ -1,6 +1,6 @@
 ---
 title: CTIS
-emoji: 👀
+emoji: 🪕🎶
 colorFrom: indigo
 colorTo: blue
 sdk: gradio

app.py

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+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
+from utils import get_modelist, find_files, embed_img, TEMP_DIR
+from collections import Counter
+from model import EvalNet
+
+
+TRANSLATE = {
+    "C0090": ["大笒", "da4_cen2"],
+    "C0091": ["高音横笛", "Treble_heng2_di2"],
+    "C0092": ["低音横笛", "Bass_heng2_di2"],
+    "C0093": ["中音横笛", "Alto_heng2_di2"],
+    "C0094": ["唢呐", "suo3_na"],
+    "C0095": ["长唢呐", "chang2_suo3_na"],
+    "C0096": ["小筚篥", "Treble_bi4_li"],
+    "C0097": ["中音筚篥", "Alto_bi4_li"],
+    "C0098": ["低音筚篥", "Bass_bi4_li"],
+    "C0099": ["短箫", "duan3_xiao1"],
+    "C0100": ["短箫(传统)", "duan3_xiao1_(traditional)"],
+    "C0101": ["洞箫", "dong4_xiao1"],
+    "C0113": ["尖子号", "jian1_zi3_hao4"],
+    "C0114": ["尖子号2", "jian1_zi3_hao4_2"],
+    "C0117": ["南音洞箫", "nan2_yin1_dong4_xiao1"],
+    "C0123": ["南嗳仔", "nan2_ai1_zai3"],
+    "C0124": ["大吹", "da4_chui1"],
+    "C0182": ["长号", "chang2_hao4"],
+    "C0183": ["老长号", "lao3_chang2_hao4"],
+    "C0187": ["高音唢呐", "Treble_suo3_na"],
+    "C0188": ["低音唢呐", "Bass_suo3_na"],
+    "C0200": ["大芦笙", "da4_lu2_sheng1"],
+    "C0201": ["小芦笙", "xiao3_lu2_sheng1"],
+    "C0237": ["G调梆笛", "bang1_di2_in_G"],
+    "C0243": ["高音键笙", "Treble_jian4_sheng1"],
+    "C0244": ["传统笙", "traditional_sheng1"],
+    "C0257": ["低音加键唢呐", "Bass_jia1_jian4_suo3_na"],
+    "C0259": ["中音加键唢呐", "Alto_jia1_jian4_suo3_na"],
+    "C0263": ["中音笙", "Alto_sheng1"],
+    "C0264": ["低音笙", "Bass_sheng1"],
+    "C0265": ["管子", "guan3_zi"],
+    "C0280": ["A调曲笛", "qu3_di2_in_A"],
+    "C0281": ["G调新笛", "xin1_di2_in_G"],
+    "C0282": ["萧", "xiao1"],
+    "C0283": ["埙", "xun1"],
+    "C0296": ["唢呐2", "suo3_na_2"],
+    "C0303": ["小闷笛", "xiao3_men1_di2"],
+    "C0304": ["侗笛", "dong4_di2"],
+    "C0305": ["德", "de2"],
+    "C0306": ["拉祜族葫芦笙", "la1_hu2_zu2_hu2_lu2_sheng1"],
+    "C0308": ["吐良", "tu3_liang2"],
+    "C0309": ["葫芦丝", "hu2_lu2_si1"],
+    "C0310": ["F调巴乌", "ba1_wu1_in_F"],
+    "C0311": ["俄比", "e2_bi3"],
+    "C0316": ["侗巴", "dong4_ba1"],
+    "D0015": ["扬琴", "yang2_qin2"],
+    "D0048": ["低音大锣", "Bass_da4_luo2"],
+    "D0049": ["虎音锣", "hu3_yin1_luo2"],
+    "D0050": ["小钹", "xiao3_bo1"],
+    "D0051": ["钹", "bo1"],
+    "D0058": ["提手(板)", "ti2_shou3_(ban3)"],
+    "D0060": ["川小锣", "chuan1_xiao3_luo2"],
+    "D0061": ["大铛铛", "da4_cheng1_cheng1"],
+    "D0062": ["小铛铛", "xiao3_cheng1_cheng1"],
+    "D0063": ["二馨", "er4_xin1"],
+    "D0064": ["川大钵", "chuan1_da4_bo1"],
+    "D0065": ["苏钵", "su1_bo1"],
+    "D0066": ["川剧堂鼓", "chuan1_ju4_tang2_gu3"],
+    "D0067": ["川铰", "chuan1_jiao3"],
+    "D0068": ["川大锣", "chuan1_da4_luo2"],
+    "D0069": ["蛮锣", "man2_luo2"],
+    "D0070": ["包锣", "bao1_luo2"],
+    "D0071": ["引鼓", "yin3_gu3"],
+    "D0102": ["上杖鼓", "shang4_zhang4_gu3"],
+    "D0103": ["小锣", "xiao3_luo2"],
+    "D0104": ["圆锣", "yuan2_luo2"],
+    "D0105": ["杖鼓", "zhang4_gu3"],
+    "D0125": ["南鼓", "nan2_gu3"],
+    "D0126": ["压脚鼓", "ya1_jiao3_gu3"],
+    "D0127": ["钟", "zhong1"],
+    "D0128": ["草锣", "cao3_luo2"],
+    "D0129": ["锣仔", "luo2_zai3"],
+    "D0130": ["响盏", "xiang3_zhan3"],
+    "D0131": ["小叫", "xiao3_jiao4"],
+    "D0132": ["拍", "pai1"],
+    "D0137": ["渔鼓", "yu2_gu3"],
+    "D0138": ["简板", "jian3_ban3"],
+    "D0140": ["脚梆子", "jiao3_bang1_zi"],
+    "D0143": ["双铃", "shuang1_ling2"],
+    "D0144": ["小叫锣", "xiao3_jiao4_luo2"],
+    "D0145": ["拍板", "pai1_ban3"],
+    "D0146": ["四宝", "si4_bao3"],
+    "D0147": ["响盏2", "xiang3_zhan3_2"],
+    "D0172": ["碗碗", "wan3_wan3"],
+    "D0173": ["代子", "dai4_zi"],
+    "D0176": ["福(新)", "fu2_(reformed)"],
+    "D0177": ["禄(新)", "lu4_(reformed)"],
+    "D0178": ["寿(新)", "shou4_(reformed)"],
+    "D0179": ["宜春三星鼓福鼓老鼓", "yi2_chun1_san1_xing1_gu3_fu2_gu3_(traditional)"],
+    "D0180": ["宜春三星鼓禄鼓老鼓", "yi2_chun1_san1_xing1_gu3_lu4_gu3_(traditional)"],
+    "D0181": ["宜春三星鼓寿鼓老鼓", "yi2_chun1_san1_xing1_gu3_shou4_gu3_(traditional)"],
+    "D0184": ["宜春三星鼓双铛", "yi2_chun1_san1_xing1_gu3_shuang1_ding1"],
+    "D0185": ["宜春三星鼓单铛", "yi2_chun1_san1_xing1_gu3_dan1_ding1"],
+    "D0186": ["宜春三星鼓镲", "yi2_chun1_san1_xing1_gu3_chao3"],
+    "D0241": ["编钟", "bian1_zhong1"],
+    "D0242": ["编磬", "bian1_qing4"],
+    "D0245": ["南梆子", "nan2_bang1_zi"],
+    "D0246": ["北梆子", "bei3_bang1_zi"],
+    "D0247": ["碰铃", "peng4_ling2"],
+    "D0248": ["中国大鼓", "Chinese_da4_gu3"],
+    "D0249": ["花盆鼓", "hua1_pen2_gu3"],
+    "D0250": ["小堂鼓", "xiao3_tang2_gu3"],
+    "D0251": ["扁鼓", "bian3_gu3"],
+    "D0252": ["五音排鼓", "wu3_yin1_pai2_gu3"],
+    "D0268": ["草帽镲", "cao3_mao4_chao3"],
+    "D0269": ["铙", "nao2"],
+    "D0270": ["铙钹", "nao2_bo1"],
+    "D0271": ["小镲", "xiao3_chao3"],
+    "D0272": ["抄锣", "chao1_luo2"],
+    "D0273": ["中虎", "zhong1_hu3"],
+    "D0274": ["武锣", "wu3_luo2"],
+    "D0275": ["小锣2", "xiao3_luo2_2"],
+    "D0276": ["马锣", "ma3_luo2"],
+    "D0277": ["木鱼", "mu4_yu2"],
+    "D0278": ["板鼓", "ban3_gu3"],
+    "D0279": ["云锣", "yun2_luo2"],
+    "D0284": ["斗锣", "dou3_luo2"],
+    "D0286": ["曲锣", "qu3_luo2"],
+    "D0287": ["深波", "shen1_bo1"],
+    "D0290": ["大镲", "da4_chao3"],
+    "D0298": ["编铓", "bian1_zhang1"],
+    "D0299": ["牛铃", "niu2_ling2"],
+    "D0315": ["竹排琴", "zhu2_pai2_qin2"],
+    "D0325": ["那格拉", "na4_ge2_la1"],
+    "D0326": ["库休克", "ku4_xiu1_ke4"],
+    "D0327": ["萨巴依", "sa4_ba1_yi1"],
+    "D0328": ["手鼓", "shou3_gu3"],
+    "L0044": ["锡剧主胡", "xi1_ju4_zhu3_hu2"],
+    "L0045": ["扬剧主胡", "yang2_ju4_zhu3_hu2"],
+    "L0046": ["扬剧主胡F调", "yang2_ju4_zhu3_hu2_in_F"],
+    "L0047": ["扬剧主胡(小西皮)", "yang2_ju4_zhu3_hu2_(xiao3_xi1_pi2)"],
+    "L0053": ["广西彩调主胡", "guang3_xi1_cai3_diao4_zhu3_hu2"],
+    "L0055": ["牛腿琴", "niu2_tui3_qin2"],
+    "L0056": ["壮剧马骨胡D调", "zhuang4_ju4_ma3_gu3_hu2_in_D"],
+    "L0072": ["盖板(新)D调", "gai4_ban3_(reformed)_in_D"],
+    "L0073": ["盖板(传统)", "gai4_ban3_(traditional)"],
+    "L0074": ["壮剧土胡", "zhuang4_ju4_tu3_hu2"],
+    "L0075": ["晋剧晋胡", "jin4_ju4_jin4_hu2"],
+    "L0076": ["壮剧土胡2", "zhuang4_ju4_tu3_hu2_2"],
+    "L0077": ["晋剧二股弦", "jin4_ju4_er4_gu3_xian2"],
+    "L0080": ["吕剧坠琴", "lv3_ju4_zhui4_qin2"],
+    "L0084": ["奚琴(传统)", "xi1_qin2_(traditional)"],
+    "L0085": ["奚琴(改良)", "xi1_qin2_(reformed)"],
+    "L0086": ["中音奚琴(改良)", "Alto_xi1_qin2_(reformed)"],
+    "L0115": ["莱芜梆子-梆胡", "lai2_wu2_bang1_zi-bang1_hu2"],
+    "L0121": ["六角弦", "liu4_jiao3_xian2"],
+    "L0122": ["壳仔弦", "ke2_zai3_xian2"],
+    "L0133": ["陇剧陇胡(传统)", "long3_ju4_long3_hu2_(traditional)"],
+    "L0134": ["陇剧陇胡(改良)D调", "long3_ju4_long3_hu2_(reformed)_in_D"],
+    "L0135": ["齐琴", "qi2_qin2"],
+    "L0136": ["渔胡", "yu2_hu2"],
+    "L0139": ["坠胡", "zhui4_hu2"],
+    "L0141": ["越胡", "yue4_hu2"],
+    "L0148": ["板胡", "ban3_hu2"],
+    "L0149": ["绍剧板胡", "shao4_ju4_ban3_hu2"],
+    "L0150": ["宛梆子梆胡", "yuan1_bang1_zi_bang1_hu2"],
+    "L0151": ["四弦", "si4_xian2"],
+    "L0152": ["滇葫(小二胡)", "dian1_hu2_(xiao3_er4_hu2)"],
+    "L0153": ["云南花灯丝弦", "yun2_nan2_hua1_deng1_si1_xian2"],
+    "L0154": ["仕胡", "shi4_hu2"],
+    "L0155": ["伬胡", "chi4_hu2"],
+    "L0156": ["工胡", "gong1_hu2"],
+    "L0157": ["大胡", "da4_hu2"],
+    "L0158": ["低音伬胡", "Bass_chi4_hu2"],
+    "L0160": ["丝弦", "si1_xian2"],
+    "L0161": ["滇胡", "dian1_hu2"],
+    "L0162": ["襄阳专用胡琴", "xiang1_yang2_zhuan1_yong4_hu2_qin2"],
+    "L0163": ["雷胡", "lei2_hu2"],
+    "L0164": ["赣胡", "gan4_hu2"],
+    "L0165": ["高腔赣胡", "gao1_qiang1_gan4_hu2"],
+    "L0166": ["高腔赣胡第2代", "gao1_qiang1_gan4_hu2_2nd_generation"],
+    "L0167": ["黔胡", "qian2_hu2"],
+    "L0168": ["花胡", "hua1_hu2"],
+    "L0169": ["花胡2", "hua1_hu2_2"],
+    "L0170": ["二股弦", "er4_gu3_xian2"],
+    "L0239": ["高音板胡", "Treble_ban3_hu2"],
+    "L0240": ["中音板胡", "Alto_ban3_hu2"],
+    "L0256": ["雷琴", "lei2_qin2"],
+    "L0266": ["二胡", "er4_hu2"],
+    "L0285": ["二弦", "er4_xian2"],
+    "L0288": ["椰胡", "ye1_hu2"],
+    "L0291": ["扁八角高胡", "bian3_ba1_jiao3_gao1_hu2"],
+    "L0292": ["六角高胡", "liu4_jiao3_gao1_hu2"],
+    "L0297": ["中胡", "zhong1_hu2"],
+    "L0307": ["芦笙", "lu2_sheng1"],
+    "L0312": ["牛角胡", "niu2_jiao3_hu2"],
+    "L0313": ["佤族独弦琴", "wa3_zu2_du2_xian2_qin2"],
+    "L0314": ["葫芦琴", "hu2_lu2_qin2"],
+    "T0006": ["陶布舒尔", "tao2_bu4_shu1_er3"],
+    "T0007": ["雅托嘎", "ya3_tuo1_ga2"],
+    "T0078": ["四股弦", "si4_gu3_xian2"],
+    "T0081": ["玄琴", "xuan2_qin2"],
+    "T0082": ["伽倻琴(改良)", "jia1_ye2_qin2_(reformed)"],
+    "T0083": ["伽倻琴", "jia1_ye2_qin2"],
+    "T0087": ["雅筝", "ya3_zheng1"],
+    "T0088": ["扬琴2", "yang2_qin2_2"],
+    "T0089": ["扬琴3", "yang2_qin2_3"],
+    "T0111": ["三弦", "san1_xian2"],
+    "T0116": ["八角月琴", "ba1_jiao3_yue4_qin2"],
+    "T0159": ["双清", "shuang1_qing1"],
+    "T0171": ["月琴", "yue4_qin2"],
+    "T0238": ["大阮", "da4_ruan3"],
+    "T0254": ["箜篌", "kong1_hou2"],
+    "T0255": ["古筝", "gu3_zheng1"],
+    "T0260": ["中阮", "zhong1_ruan3"],
+    "T0261": ["柳琴", "liu3_qin2"],
+    "T0262": ["琵琶", "pi2_pa2"],
+    "T0267": ["扬琴4", "yang2_qin2_4"],
+    "T0289": ["三弦2", "san1_xian2_2"],
+    "T0294": ["南音琵琶", "nan2_yin1_pi2_pa2"],
+    "T0295": ["南音三弦", "nan2_yin1_san1_xian2"],
+    "T0300": ["澜沧小三弦", "lan2_cang1_xiao3_san1_xian2"],
+    "T0301": ["玎", "ding1"],
+    "T0302": ["傈傈族奇奔", "li4_li4_zu2_qi2_ben1"],
+    "T0317": ["独弦琴", "du2_xian2_qin2"],
+    "T0318": ["弹拨尔", "dan4_bo1_er3"],
+    "T0319": ["低音热瓦普", "Bass_re4_wa3_pu3"],
+    "T0320": ["民间热瓦普", "folk_re4_wa3_pu3"],
+    "T0323": ["都它尔", "du1_ta1_er3"],
+}
+CLASSES = list(TRANSLATE.keys())
+SAMPLE_RATE = 44100
+
+
+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=2, top_db=40):
+    os.makedirs(TEMP_DIR, exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
+        non_silents = librosa.effects.split(y, top_db=top_db)
+        y = np.concatenate([y[start:end] for start, end in non_silents])
+        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()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+def wav2cqt(audio_path: str, width=2, top_db=40):
+    os.makedirs(TEMP_DIR, exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
+        non_silents = librosa.effects.split(y, top_db=top_db)
+        y = np.concatenate([y[start:end] for start, end in non_silents])
+        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()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+def wav2chroma(audio_path: str, width=2, top_db=40):
+    os.makedirs(TEMP_DIR, exist_ok=True)
+    try:
+        y, sr = librosa.load(audio_path, sr=SAMPLE_RATE)
+        non_silents = librosa.effects.split(y, top_db=top_db)
+        y = np.concatenate([y[start:end] for start, end in non_silents])
+        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()
+
+    except Exception as e:
+        print(f"Error converting {audio_path} : {e}")
+
+
+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):
+    if os.path.exists(folder_path):
+        shutil.rmtree(folder_path)
+
+    if not wav_path:
+        return None, "请输入音频 Please input an audio!"
+
+    try:
+        model = EvalNet(log_name, len(TRANSLATE)).model
+    except Exception as e:
+        return None, f"{e}"
+
+    spec = log_name.split("_")[-3]
+    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)
+    return (
+        os.path.basename(wav_path),
+        f"{TRANSLATE[CLASSES[pred_id]][0]} ({TRANSLATE[CLASSES[pred_id]][1].capitalize()})",
+    )
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings("ignore")
+    models = get_modelist()
+    examples = []
+    example_wavs = find_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=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]
+                ),
+            ],
+            outputs=[
+                gr.Textbox(label="音频文件名 Audio filename", show_copy_button=True),
+                gr.Textbox(
+                    label="中国乐器识别 Chinese instrument recognition",
+                    show_copy_button=True,
+                ),
+            ],
+            examples=examples,
+            cache_examples=False,
+            flagging_mode="never",
+            title="建议录音时长保持在 3s 左右<br>It is recommended to keep the recording length around 3s.",
+        )
+
+        gr.Markdown(
+            """
+# 引用 Cite
+```bibtex
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
+  title        = {CCMusic: an Open and Diverse Database for Chinese Music Information Retrieval Research},
+  month        = {mar},
+  year         = {2024},
+  publisher    = {HuggingFace},
+  version      = {1.2},
+  url          = {https://huggingface.co/ccmusic-database}
+}
+```"""
+        )
+
+    demo.launch()

model.py

@@ -0,0 +1,145 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+from modelscope.msdatasets import MsDataset
+from utils import MODEL_DIR
+
+
+class EvalNet:
+    model: nn.Module = None
+    m_type = "squeezenet"
+    input_size = 224
+    output_size = 512
+
+    def __init__(self, log_name: str, cls_num: int):
+        saved_model_path = f"{MODEL_DIR}/{log_name}/save.pt"
+        m_ver = "_".join(log_name.split("_")[:-3])
+        self.m_type, self.input_size = self._model_info(m_ver)
+
+        if not hasattr(models, m_ver):
+            raise Exception("Unsupported model.")
+
+        self.model = eval("models.%s()" % m_ver)
+        linear_output = self._set_outsize()
+        self._set_classifier(cls_num, linear_output)
+        checkpoint = torch.load(saved_model_path, map_location="cpu")
+        if torch.cuda.is_available():
+            checkpoint = torch.load(saved_model_path)
+
+        self.model.load_state_dict(checkpoint, False)
+        self.model.eval()
+
+    def _get_backbone(self, ver: str, backbone_list: list):
+        for bb in backbone_list:
+            if ver == bb["ver"]:
+                return bb
+
+        print("Backbone name not found, using default option - alexnet.")
+        return backbone_list[0]
+
+    def _model_info(self, m_ver: str):
+        backbone_list = MsDataset.load(
+            "monetjoe/cv_backbones",
+            split="v1",
+        )
+        backbone = self._get_backbone(m_ver, backbone_list)
+        m_type = str(backbone["type"])
+        input_size = int(backbone["input_size"])
+        return m_type, input_size
+
+    def _classifier(self, cls_num: int, output_size: int, linear_output: bool):
+        q = (1.0 * output_size / cls_num) ** 0.25
+        l1 = int(q * cls_num)
+        l2 = int(q * l1)
+        l3 = int(q * l2)
+        if linear_output:
+            return torch.nn.Sequential(
+                nn.Dropout(),
+                nn.Linear(output_size, l3),
+                nn.ReLU(inplace=True),
+                nn.Dropout(),
+                nn.Linear(l3, l2),
+                nn.ReLU(inplace=True),
+                nn.Dropout(),
+                nn.Linear(l2, l1),
+                nn.ReLU(inplace=True),
+                nn.Linear(l1, cls_num),
+            )
+
+        else:
+            return torch.nn.Sequential(
+                nn.Dropout(),
+                nn.Conv2d(output_size, l3, kernel_size=(1, 1), stride=(1, 1)),
+                nn.ReLU(inplace=True),
+                nn.AdaptiveAvgPool2d(output_size=(1, 1)),
+                nn.Flatten(),
+                nn.Linear(l3, l2),
+                nn.ReLU(inplace=True),
+                nn.Dropout(),
+                nn.Linear(l2, l1),
+                nn.ReLU(inplace=True),
+                nn.Linear(l1, cls_num),
+            )
+
+    def _set_outsize(self):
+        for name, module in self.model.named_modules():
+            if (
+                str(name).__contains__("classifier")
+                or str(name).__eq__("fc")
+                or str(name).__contains__("head")
+                or hasattr(module, "classifier")
+            ):
+                if isinstance(module, torch.nn.Linear):
+                    self.output_size = module.in_features
+                    return True
+
+                if isinstance(module, torch.nn.Conv2d):
+                    self.output_size = module.in_channels
+                    return False
+
+        return False
+
+    def _set_classifier(self, cls_num: int, linear_output: bool):
+        if self.m_type == "convnext":
+            del self.model.classifier[2]
+            self.model.classifier = nn.Sequential(
+                *list(self.model.classifier)
+                + list(self._classifier(cls_num, self.output_size, linear_output))
+            )
+            return
+
+        elif self.m_type == "maxvit":
+            del self.model.classifier[5]
+            self.model.classifier = nn.Sequential(
+                *list(self.model.classifier)
+                + list(self._classifier(cls_num, self.output_size, linear_output))
+            )
+            return
+
+        if hasattr(self.model, "classifier"):
+            self.model.classifier = self._classifier(
+                cls_num, self.output_size, linear_output
+            )
+            return
+
+        elif hasattr(self.model, "fc"):
+            self.model.fc = self._classifier(cls_num, self.output_size, linear_output)
+            return
+
+        elif hasattr(self.model, "head"):
+            self.model.head = self._classifier(cls_num, self.output_size, linear_output)
+            return
+
+        self.model.heads.head = self._classifier(
+            cls_num, self.output_size, linear_output
+        )
+
+    def forward(self, x: torch.Tensor):
+        if torch.cuda.is_available():
+            x = x.cuda()
+            self.model = self.model.cuda()
+
+        if self.m_type == "googlenet":
+            return self.model(x)[0]
+        else:
+            return self.model(x)

requirements.txt

@@ -0,0 +1,6 @@
+librosa
+torch
+matplotlib
+torchvision
+pillow
+modelscope

utils.py

@@ -0,0 +1,67 @@
+import os
+import torch
+import torchvision.transforms as transforms
+from modelscope import snapshot_download
+from PIL import Image
+
+MODEL_DIR = snapshot_download(
+    f"ccmusic-database/CTIS",
+    cache_dir=f"{os.getcwd()}/__pycache__",
+)
+TEMP_DIR = f"{os.getcwd()}/flagged"
+
+
+def toCUDA(x):
+    if hasattr(x, "cuda"):
+        if torch.cuda.is_available():
+            return x.cuda()
+
+    return x
+
+
+def find_files(folder_path=f"{MODEL_DIR}/examples", ext=".wav"):
+    wav_files = []
+    for root, _, files in os.walk(folder_path):
+        for file in files:
+            if file.endswith(ext):
+                file_path = os.path.join(root, file)
+                wav_files.append(file_path)
+
+    return wav_files
+
+
+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}")
+        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}")
+            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
+
+
+def embed_img(img_path: str, 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)