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Configuration error
Configuration error
| from sklearn.cluster import * | |
| from sklearn import metrics | |
| from sklearn.mixture import GaussianMixture # 高斯混合模型 | |
| import os | |
| import numpy as np | |
| import librosa | |
| import IPython.display as ipd | |
| from sklearn.cluster import KMeans | |
| from sklearn.cluster import Birch | |
| from sklearn.cluster import SpectralClustering | |
| from sklearn.cluster import AgglomerativeClustering | |
| # 读取音频文件 | |
| embs = [] | |
| wavnames = [] | |
| speaker_wav_root_path = "na" | |
| for idx, wavname in enumerate(os.listdir(speaker_wav_root_path)): | |
| if wavname.endswith(".wav"): | |
| embs.append(np.expand_dims(np.load(f"{speaker_wav_root_path}/{wavname}.emo.npy"), axis=0)) | |
| wavnames.append(wavname) | |
| x = np.concatenate(embs,axis=0) | |
| # 聚类算法类的数量 | |
| n_clusters = 15 | |
| #model = KMeans(n_clusters=n_clusters, random_state=10) | |
| # 可以自行尝试各种不同的聚类算法 | |
| model = Birch(n_clusters= n_clusters, threshold= 0.2) | |
| #model = SpectralClustering(n_clusters=n_clusters) | |
| #model = AgglomerativeClustering(n_clusters= n_clusters) | |
| y_predict = model.fit_predict(x) | |
| def disp(rootpath, wavname): | |
| wav, sr =librosa.load(f"{rootpath}/{wavname}", sr=None) | |
| ipd.display(ipd.Audio(wav, rate=sr)) | |
| classes=[[] for i in range(y_predict.max()+1)] | |
| for idx, wavname in enumerate(wavnames): | |
| classes[y_predict[idx]].append(wavname) | |
| #write to file | |
| for idx, c in enumerate(classes): | |
| with open(f"{speaker_wav_root_path}/cluster{idx}.txt", "w") as f: | |
| for wavname in c: | |
| f.write(f"{wavname}\n") | |
| disp(speaker_wav_root_path, wavname) | |