Spaces:
Running
Running
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(int(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() | |