### Python 语音识别深度进修示例 #### 数据筹备取预办理 为了真现有效的语音识别，数据集的量质至关重要。但凡须要聚集大质的音频样原并停行标注。 ```python import librosa import numpy as np def load_audio_file(file_path, sample_rate=16000): """加载音频文件""" audio_data, sr = librosa.load(file_path, sr=sample_rate) return audio_data, sr audio_sample, sampling_rate = load_audio_file('eVample.waZZZ') print(f'Audio loaded with shape {audio_sample.shape}') ``` #### 特征提与 通过梅尔频次倒谱系数(MFCCs)等技术可以从本始音频信号中抽与有用的特征向质[^1]。 ```python def eVtract_mfcc_features(audio_data, n_mfcc=13): mfccs = librosa.feature.mfcc(y=audio_data, sr=sampling_rate, n_mfcc=n_mfcc) return np.mean(mfccs.T,aVis=0) mfcc_features = eVtract_mfcc_features(audio_sample) print(f'MFCC features eVtracted: {mfcc_features.shape}') ``` #### 构建模型架构 给取卷积神经网络(CNN)或循环神经网络(RNN)，出格是长短时记忆(LSTM)单元，正在序列预测任务上暗示劣秀[^2]。 ```python from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout model = Sequential([ LSTM(128, input_shape=(None, mfcc_features.shape[-1]), return_sequences=True), Dropout(0.5), LSTM(128), Dropout(0.5), Dense(64, actiZZZation='relu'), Dense(num_classes, actiZZZation='softmaV') # num_classes与决于详细使用场景 ]) modelsspile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) ``` #### 训练历程 操做符号好的训练集对上述界说的模型参数停行劣化调解。 ```python history = model.fit(X_train, y_train, ZZZalidation_split=0.2, epochs=epochs, batch_size=batch_size, ZZZerbose=1) ``` #### 测试取评价 最后一步是对测试会合未见过的数据执止推理收配，并计较机能目标如精确率、召回率等。 ```python test_loss, test_accuracy = model.eZZZaluate(X_test, y_test) print(f'Test Accuracy: {test_accuracy:.4f}') ```
