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 CLASSES = ["m_bel", "f_bel", "m_folk", "f_folk"] 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=1.6, topdb=40): os.makedirs("./tmp", exist_ok=True) try: y, sr = librosa.load(audio_path, sr=48000) non_silents = librosa.effects.split(y, top_db=topdb) non_silent = np.concatenate([y[start:end] for start, end in non_silents]) 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 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=1.6, topdb=40): os.makedirs("./tmp", exist_ok=True) try: y, sr = librosa.load(audio_path, sr=48000) non_silents = librosa.effects.split(y, top_db=topdb) non_silent = np.concatenate([y[start:end] for start, end in non_silents]) cqt_spec = librosa.cqt(y=non_silent, sr=sr) log_cqt_spec = librosa.power_to_db(np.abs(cqt_spec) ** 2, ref=np.max) dur = librosa.get_duration(y=non_silent, 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=1.6, topdb=40): os.makedirs("./tmp", exist_ok=True) try: y, sr = librosa.load(audio_path, sr=48000) non_silents = librosa.effects.split(y, top_db=topdb) non_silent = np.concatenate([y[start:end] for start, end in non_silents]) chroma_spec = librosa.feature.chroma_stft(y=non_silent, sr=sr) log_chroma_spec = librosa.power_to_db(np.abs(chroma_spec) ** 2, ref=np.max) dur = librosa.get_duration(y=non_silent, 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: str, log_name: str, folder_path="./tmp"): if os.path.exists(folder_path): shutil.rmtree(folder_path) if not wav_path: wav_path = "./examples/f_bel.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() translate = { "m_bel": "Male bel canto", "m_folk": "Male folk singing", "f_bel": "Female bel canto", "f_folk": "Female folk singing", } 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="Uploading 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 style 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()