dtw语音识别python

以下是一个基于MFCC特征提取、DTW算法的语音识别Python代码示例：

import numpy as np
import scipy.io.wavfile as wav
from python_speech_features import mfcc
from scipy.spatial.distance import euclidean
from fastdtw import fastdtw

# 1. 数据准备和特征提取

# 设置录音参数
fs = 16000  # 采样率
duration = 1  # 录音时长（秒）

# 读取录音文件
filename = 'input.wav'
rate, voiceData = wav.read(filename)

# 提取MFCC特征
mfccFeatures = mfcc(voiceData, samplerate=fs)

# 2. 训练模型

# 准备训练数据和标签
trainingData = []  # 存储训练数据
trainingLabels = []  # 存储标签

# 添加0到9的训练数据和标签
for i in range(10):
    # 读取训练数据（假设已经准备好了对应的语音文件）
    filename = f'digit_{i}.wav'
    rate, trainingVoice = wav.read(filename)
    
    # 提取MFCC特征
    trainingFeatures = mfcc(trainingVoice, samplerate=fs)
    
    # 添加到训练数据和标签中
    trainingData.extend(trainingFeatures)
    trainingLabels.extend([i] * len(trainingFeatures))

# 转换为NumPy数组
trainingData = np.array(trainingData)
trainingLabels = np.array(trainingLabels)

# 3. 识别输入语音

# 使用DTW算法计算距离和路径
distances = []

for i in range(10):
    # 获取当前数字的模型特征
    filename = f'digit_{i}.wav'
    rate, digitVoice = wav.read(filename)
    digitFeatures = mfcc(digitVoice, samplerate=fs)
    
    # 计算DTW距离和路径
    distance, _ = fastdtw(mfccFeatures, digitFeatures, dist=euclidean)
    distances.append(distance)

# 找到最小距离对应的数字
recognizedDigit = np.argmin(distances)

# 显示识别结果
print(f"识别结果：{recognizedDigit}")

请确保已安装所需的库，例如`scipy`、`numpy`、`python_speech_features`和`fastdtw`。此示例使用了`wav`库来读取和写入音频文件。你需要将录制的语音文件命名为`input.wav`，并准备好0到9的训练数据文件，命名为`digit_0.wav`到`digit_9.wav`。该代码示例使用了MFCC特征提取和DTW算法来计算输入语音与训练数据之间的距离，并找到最匹配的数字作为识别结果。