Python初学者ML项目问题

Question

所以我复制了一些代码来尝试在 python 中弄清楚机器学习。总的来说效果很好，但现在我不知道如何使用它（输入我自己的文件并分析它）。

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]))

Answer 1

model.predict()在您的新音频文件上使用。那应该返回您想要的输出。