Python 初學者 ML 項目問題

Question

所以我復制了一些代碼來嘗試在 python 中弄清楚機器學習（鏈接 = https://data-flair.training/blogs/python-mini-project-speech-emotion-recognition ）。 總的來說效果很好，但現在我不知道如何使用它（輸入我自己的文件並分析它）。

import librosa
import numpy as np
import soundfile
import sklearn
import os, glob, pickle
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score

def extract_feture(filepath,mfcc,chroma,mel):
    with soundfile.SoundFile(filepath) as sound_file:
        X = sound_file.read(dtype="float32")
        sample_rate=sound_file.samplerate
        if chroma:
            stft = np.abs(librosa.stft(X))
        result = np.array([])
        if mfcc:
            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40) .T, axis=0)
            result = np.hstack((result, mfccs))
        if chroma:
            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)
            result = np.hstack((result, chroma))
        if mel:
            mel = np.mean(librosa.feature.melspectrogram(X, sr= sample_rate).T,axis=0)
            result = np.hstack((result, mel))
    return result

emotions = {
    '01':'neutral',
    '02':'calm',
    '03':'happy',
    '04':'sad',
    '05': 'angry',
    '06': 'fearful',
    '07': 'disgust',
    '08': 'surprised'
}

observed_emotions =['calm', 'happy', 'fearful', 'disgust']

def load_data(test_size=0.2):
    x, y = [], []
    for file in glob.glob("/home/adobug2/Documents/ravdess-data/Actor_*/*.wav"):
        file_name = os.path.basename(file)
        emotion = emotions[file_name.split("-")[2]]
        if emotion not in observed_emotions:
            continue
        feature = extract_feture(file, mfcc=True, chroma=True, mel=True)
        x.append(feature)
        y.append(emotion)
    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)

x_train,x_test,y_train,y_test=load_data(test_size=0.25)

print((x_train.shape[0], x_test.shape[0]))

print(f'Features extracted: {x_train.shape[1]}')

model=MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)
model.fit(x_train,y_train)
y_pred=model.predict(x_test)

accuracy=accuracy_score(y_true=y_test, y_pred=y_pred)
print("Accuracy: {:.2f}%".format(accuracy*100))

Answer 1

在您的新音頻文件上使用model.predict() 。 那應該會返回您想要的 output。