Spaces:
Running
Running
File size: 4,382 Bytes
ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 ef94b92 45381f2 8b09a36 45381f2 ef94b92 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
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
import torchvision.transforms as transforms
from collections import Counter
from PIL import Image
from tqdm import tqdm
from model import net, _L, MODEL_DIR, TMP_DIR
def most_common_element(input_list):
counter = Counter(input_list)
mce, _ = counter.most_common(1)[0]
return mce
def wav_to_mel(audio_path: str, width=0.18):
os.makedirs(TMP_DIR, exist_ok=True)
y, sr = librosa.load(audio_path, sr=48000)
non_silent = y
mel_spec = librosa.feature.melspectrogram(y=non_silent, sr=sr)
log_mel_spec = librosa.power_to_db(mel_spec, ref=np.max)
dur = librosa.get_duration(y=non_silent, 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 tqdm(range(begin, end, step), desc="转换 wav 至 jpgs..."):
librosa.display.specshow(log_mel_spec[:, i : i + step])
plt.axis("off")
plt.savefig(
f"{TMP_DIR}/{os.path.basename(audio_path)[:-4]}_{i}.jpg",
bbox_inches="tight",
pad_inches=0.0,
)
plt.close()
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 infer(wav_path, folder_path=TMP_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!")
wav_to_mel(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: torch.Tensor = net()(input)
pred_id = torch.max(output.data, 1)[1]
outputs.append(pred_id)
max_count_item = most_common_element(outputs)
filename = os.path.basename(wav_path)
result = translate[classes[max_count_item]]
except Exception as e:
status = f"{e}"
return status, filename, result
if __name__ == "__main__":
warnings.filterwarnings("ignore")
translate = {
"PearlRiver": _L("珠江"),
"YoungChang": _L("英昌"),
"Steinway-T": _L("施坦威剧场"),
"Hsinghai": _L("星海"),
"Kawai": _L("卡瓦依"),
"Steinway": _L("施坦威"),
"Kawai-G": _L("卡瓦依三角"),
"Yamaha": _L("雅马哈"),
}
classes = list(translate.keys())
example_wavs = []
for cls in classes:
example_wavs.append(f"{MODEL_DIR}/examples/{cls}.wav")
with gr.Blocks() as demo:
gr.Interface(
fn=infer,
inputs=gr.Audio(type="filepath", label=_L("上传钢琴录音")),
outputs=[
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("钢琴分类结果"), show_copy_button=True),
],
examples=example_wavs,
cache_examples=False,
allow_flagging="never",
title=_L("建议录音时长保持在 3s 左右, 过长会影响识别效率"),
)
gr.Markdown(
f"# {_L('引用')}"
+ """
```bibtex
@inproceedings{zhou2023holistic,
title = {A Holistic Evaluation of Piano Sound Quality},
author = {Monan Zhou and Shangda Wu and Shaohua Ji and Zijin Li and Wei Li},
booktitle = {National Conference on Sound and Music Technology},
pages = {3--17},
year = {2023},
organization = {Springer}
}
```"""
)
demo.launch()
|