1. 准备

在之前的工作上增加了韵律的相关特征提取，之前可见：【语音信号处理】1语音信号可视化——时域、频域、语谱图、MFCC详细思路与计算、差分

安装一下这个库：

pip install praat-parselmouth

还有其他的一些，反正缺啥安啥：

pip install pydub
pip install python_speech_features
pip install librosa==0.9

2. 最终结果

3. 代码

"""
This script contains supporting function for the data processing.
It is used in several other scripts:
for generating bvh files, aligning sequences and calculation of speech features
"""import librosa
import librosa.displayfrom pydub import AudioSegment # TODO(RN) add dependency!
import parselmouth as pm # TODO(RN) add dependency!
from python_speech_features import mfcc
import scipy.io.wavfile as wavimport numpy as np
import scipyNFFT = 1024
MFCC_INPUTS=26 # How many features we will store for each MFCC vector
WINDOW_LENGTH = 0.1 #s
SUBSAMPL_RATE = 9def derivative(x, f):""" Calculate numerical derivative (by FDM) of a 1d arrayArgs:x: input space xf: Function of xReturns:der:  numerical derivative of f wrt x"""x = 1000 * x  # from seconds to milliseconds# Normalization:dx = (x[1] - x[0])cf = np.convolve(f, [1, -1]) / dx# Remove unstable valuesder = cf[:-1].copy()der[0] = 0return derdef create_bvh(filename, prediction, frame_time):"""Create BVH FileArgs:filename:    file, in which motion in bvh format should be writtenprediction:  motion sequences, to be written into fileframe_time:  frame rate of the motionReturns:nothing, writes motion to the file"""with open('hformat.txt', 'r') as ftemp:hformat = ftemp.readlines()with open(filename, 'w') as fo:prediction = np.squeeze(prediction)print("output vector shape: " + str(prediction.shape))offset = [0, 60, 0]offset_line = "\tOFFSET " + " ".join("{:.6f}".format(x) for x in offset) + '\n'fo.write("HIERARCHY\n")fo.write("ROOT Hips\n")fo.write("{\n")fo.write(offset_line)fo.writelines(hformat)fo.write("MOTION\n")fo.write("Frames: " + str(len(prediction)) + '\n')fo.write("Frame Time: " + frame_time + "\n")for row in prediction:row[0:3] = 0legs = np.zeros(24)row = np.concatenate((row, legs))label_line = " ".join("{:.6f}".format(x) for x in row) + " "fo.write(label_line + '\n')print("bvh generated")def shorten(arr1, arr2, min_len=0):if min_len == 0:min_len = min(len(arr1), len(arr2))arr1 = arr1[:min_len]arr2 = arr2[:min_len]return arr1, arr2def average(arr, n):""" Replace every "n" values by their averageArgs:arr: input arrayn:   number of elements to average onReturns:resulting array"""end = n * int(len(arr)/n)return np.mean(arr[:end].reshape(-1, n), 1)def calculate_spectrogram(audio_filename):""" Calculate spectrogram for the audio fileArgs:audio_filename: audio file nameduration: the duration (in seconds) that should be read from the file (can be used to load just a part of the audio file)Returns:log spectrogram values"""DIM = 64audio, sample_rate = librosa.load(audio_filename)# Make stereo audio being monoif len(audio.shape) == 2:audio = (audio[:, 0] + audio[:, 1]) / 2spectr = librosa.feature.melspectrogram(y=audio, sr=sample_rate, window = scipy.signal.hanning,#win_length=int(WINDOW_LENGTH * sample_rate),hop_length = int(WINDOW_LENGTH* sample_rate / 2),fmax=7500, fmin=100, n_mels=DIM)# Shift into the log scaleeps = 1e-10log_spectr = np.log(abs(spectr)+eps)return np.transpose(log_spectr)def calculate_mfcc(audio_filename):"""Calculate MFCC features for the audio in a given fileArgs:audio_filename: file name of the audioReturns:feature_vectors: MFCC feature vector for the given audio file"""fs, audio = wav.read(audio_filename)# Make stereo audio being monoif len(audio.shape) == 2:audio = (audio[:, 0] + audio[:, 1]) / 2# Calculate MFCC feature with the window frame it was designed forinput_vectors = mfcc(audio, winlen=0.02, winstep=0.01, samplerate=fs, numcep=MFCC_INPUTS, nfft=NFFT)input_vectors = [average(input_vectors[:, i], 5) for i in range(MFCC_INPUTS)]feature_vectors = np.transpose(input_vectors)return feature_vectorsdef extract_prosodic_features(audio_filename):"""Extract all 5 prosodic featuresArgs:audio_filename:   file name for the audio to be usedReturns:pros_feature:     energy, energy_der, pitch, pitch_der, pitch_ind"""WINDOW_LENGTH = 5# Read audio from filesound = AudioSegment.from_file(audio_filename, format="wav")# Alternative prosodic featurespitch, energy = compute_prosody(audio_filename, WINDOW_LENGTH / 1000)duration = len(sound) / 1000t = np.arange(0, duration, WINDOW_LENGTH / 1000)energy_der = derivative(t, energy)pitch_der = derivative(t, pitch)# Average everything in order to match the frequencyenergy = average(energy, 10)energy_der = average(energy_der, 10)pitch = average(pitch, 10)pitch_der = average(pitch_der, 10)# Cut them to the same sizemin_size = min(len(energy), len(energy_der), len(pitch_der), len(pitch_der))energy = energy[:min_size]energy_der = energy_der[:min_size]pitch = pitch[:min_size]pitch_der = pitch_der[:min_size]# Stack them all togetherpros_feature = np.stack((energy, energy_der, pitch, pitch_der))#, pitch_ind))# And reshapepros_feature = np.transpose(pros_feature)return pros_featuredef compute_prosody(audio_filename, time_step=0.05):print(pm.__file__)audio = pm.Sound(audio_filename)# Extract pitch and intensitypitch = audio.to_pitch(time_step=time_step)intensity = audio.to_intensity(time_step=time_step)# Evenly spaced time stepstimes = np.arange(0, audio.get_total_duration() - time_step, time_step)# Compute prosodic features at each time steppitch_values = np.nan_to_num(np.asarray([pitch.get_value_at_time(t) for t in times]))intensity_values = np.nan_to_num(np.asarray([intensity.get_value(t) for t in times]))intensity_values = np.clip(intensity_values, np.finfo(intensity_values.dtype).eps, None)# Normalize features [Chiu '11]pitch_norm = np.clip(np.log(pitch_values + 1) - 4, 0, None)intensity_norm = np.clip(np.log(intensity_values) - 3, 0, None)return pitch_norm, intensity_normdef read_wav(path_wav):import wavef = wave.open(path_wav, 'rb')params = f.getparams()nchannels, sampwidth, framerate, nframes = params[:4]  # 通道数、采样字节数、采样率、采样帧数voiceStrData = f.readframes(nframes)waveData = np.frombuffer(voiceStrData, dtype=np.short)  # 将原始字符数据转换为整数waveData = waveData * 1.0 / max(abs(waveData))  # 音频数据归一化, instead of .fromstringwaveData = np.reshape(waveData, [nframes, nchannels]).T  # .T 表示转置, 将音频信号规整乘每行一路通道信号的格式，即该矩阵一行为一个通道的采样点，共nchannels行f.close()return waveData, nframes, framerateimport matplotlib.pyplot as plt
def draw_time_domain_image(x1, waveData, nframes, framerate):       # 时域特征time = np.arange(0,nframes) * (1.0/framerate)# plt.figure(1)x1.plot(time,waveData[0,:],c='b')plt.xlabel('time')plt.ylabel('am')# plt.show()def draw_frequency_domain_image(x2, waveData):      # 频域特征fftdata = np.fft.fft(waveData[0, :])fftdata = abs(fftdata)hz_axis = np.arange(0, len(fftdata))# plt.figure(2)x2.plot(hz_axis, fftdata, c='b')plt.xlabel('hz')plt.ylabel('am')# plt.show()def draw_Spectrogram(x3, waveData, framerate):     # 语谱图framelength = 0.025     # 帧长20~30msframesize = framelength * framerate       # 每帧点数 N = t*fs,通常情况下值为256或512,要与NFFT相等, 而NFFT最好取2的整数次方,即framesize最好取的整数次方nfftdict = {}lists = [32, 64, 128, 256, 512, 1024]for i in lists:     # 找到与当前framesize最接近的2的正整数次方nfftdict[i] = abs(framesize - i)sortlist = sorted(nfftdict.items(), key=lambda x: x[1])  # 按与当前framesize差值升序排列framesize = int(sortlist[0][0])  # 取最接近当前framesize的那个2的正整数次方值为新的framesizeNFFT = framesize  # NFFT必须与时域的点数framsize相等，即不补零的FFToverlapSize = 1.0 / 3 * framesize  # 重叠部分采样点数overlapSize约为每帧点数的1/3~1/2overlapSize = int(round(overlapSize))  # 取整spectrum, freqs, ts, fig = x3.specgram(waveData[0], NFFT=NFFT, Fs=framerate, window=np.hanning(M=framesize),noverlap=overlapSize, mode='default', scale_by_freq=True, sides='default',scale='dB', xextent=None)  # 绘制频谱图plt.ylabel('Frequency')plt.xlabel('Time(s)')plt.title('Spectrogram')# plt.show()def mfcc_librosa(ax, path):y, sr = librosa.load(path, sr=None)'''librosa.feature.mfcc(y=None, sr=22050, S=None, n_mfcc=20, dct_type=2, norm='ortho', **kwargs)y：声音信号的时域序列sr：采样频率(默认22050)S：对数能量梅尔谱(默认为空)n_mfcc：梅尔倒谱系数的数量（默认取20）dct_type:离散余弦变换(DCT)的类型(默认为类型2)norm：如果DCT的类型为是2或者3，参数设置为"ortho"，使用正交归一化DCT基。归一化并不支持DCT类型为1kwargs：如果处理时间序列输入，参照melspectrogram返回：M：MFCC序列'''mfcc_data = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)ax.matshow(mfcc_data)plt.title('MFCC')# plt.show()from scipy.io import wavfile
from python_speech_features import mfcc, logfbank
def mfcc_python_speech_features(ax, path):sampling_freq, audio = wavfile.read(path)       # 读取输入音频文件mfcc_features = mfcc(audio, sampling_freq)      # 提取MFCC和滤波器组特征filterbank_features = logfbank(audio, sampling_freq)        # numpy.ndarray, (999, 26)print(filterbank_features.shape)        # (200, 26)print('\nMFCC:\n窗口数 =', mfcc_features.shape[0])print('每个特征的长度 =', mfcc_features.shape[1])print('\nFilter bank:\n窗口数 =', filterbank_features.shape[0])print('每个特征的长度 =', filterbank_features.shape[1])mfcc_features = mfcc_features.T     # 画出特征图，将MFCC可视化。转置矩阵，使得时域是水平的ax.matshow(mfcc_features)plt.title('MFCC')filterbank_features = filterbank_features.T     # 将滤波器组特征可视化。转置矩阵，使得时域是水平的ax.matshow(filterbank_features)plt.title('Filter bank')plt.show()if __name__ == "__main__":Debug=1if Debug:audio_filename = "your path.wav"# feature = calculate_spectrogram(audio_filename)waveData, nframes, framerate = read_wav(audio_filename)ax1 = plt.subplot(3, 3, 1)draw_time_domain_image(ax1, waveData, nframes, framerate)ax2 = plt.subplot(3, 3, 2)draw_frequency_domain_image(ax2, waveData)ax3 = plt.subplot(3, 3, 3)draw_Spectrogram(ax3, waveData, framerate)ax4 = plt.subplot(3, 3, 4)mfcc_librosa(ax4, audio_filename)x = calculate_spectrogram(audio_filename)print(x.shape)      # (145, 64)ax5 = plt.subplot(3, 3, 5)ax5.plot(x)x = calculate_mfcc(audio_filename)print(x.shape)      # (143, 26)ax6 = plt.subplot(3, 3, 6)ax6.plot(x)x = extract_prosodic_features(audio_filename)print(x.shape)  # (143, 4)ax7 = plt.subplot(3, 3, 7)ax7.plot(x)x, y = compute_prosody(audio_filename, time_step=0.05)print(x.shape)  # (143,)print(y.shape)  # (143,)ax8 = plt.subplot(3, 3, 8)ax8.plot(x)ax9 = plt.subplot(3, 3, 9)ax9.plot(y)plt.tight_layout()plt.savefig('1.jpg')

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【语音信号处理】3语音信号可视化——prosody

语音特征提取与可视化

1. 准备

2. 最终结果

3. 代码

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