深度有趣 | 29 方言種類分類

摘要： 對於每一個MFCC特徵都輸出一個概率分佈，然後結合CTC演算法即可實現語音識別 相比之下，語音分類要簡單很多，因為對於整個MFCC特徵序列只需要輸出一個分類結果即可 語音分類和語音識別的區別，可以類比一下文字分類和序列標註的區別 具體實現時，只需要稍微修改一下網路結構即可 資料...

對於每一個MFCC特徵都輸出一個概率分佈，然後結合CTC演算法即可實現語音識別

相比之下，語音分類要簡單很多，因為對於整個MFCC特徵序列只需要輸出一個分類結果即可

語音分類和語音識別的區別，可以類比一下文字分類和序列標註的區別

具體實現時，只需要稍微修改一下網路結構即可

資料

使用科大訊飛方言種類識別AI挑戰賽提供的資料，challenge.xfyun.cn/，初賽提供了6種方言，複賽提供了10種方言

每種方言包括30個人每人200條共計6000條訓練資料，以及10個人每人50條共計500條驗證資料

資料以pcm格式提供，可以理解為wav檔案去掉多餘資訊之後，僅保留語音資料的格式

實現

以下以長沙、南昌、上海三種方言資料為例，介紹如何實現語音分類

載入庫

# -*- coding:utf-8 -*-

import numpy as np
import os
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
from sklearn.utils import shuffle
import glob
import pickle
from tqdm import tqdm

from keras.models import Model
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Input, Activation, Conv1D, Add, Multiply, BatchNormalization, GlobalMaxPooling1D, Dropout
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

from python_speech_features import mfcc
import librosa
from IPython.display import Audio
import wave
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載入pcm檔案，共1W8條訓練資料，1.5K條驗證資料

train_files = glob.glob('data/*/train/*/*.pcm')
dev_files = glob.glob('data/*/dev/*/*/*.pcm')

print(len(train_files), len(dev_files), train_files[0])
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整理每條語音資料對應的分類標籤

labels = {'train': [], 'dev': []}

for i in tqdm(range(len(train_files))):
path = train_files[i]
label = path.split('/')[1]
labels['train'].append(label)

for i in tqdm(range(len(dev_files))):
path = dev_files[i]
label = path.split('/')[1]
labels['dev'].append(label)

print(len(labels['train']), len(labels['dev']))
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定義處理語音、pcm轉wav、視覺化語音的三個函式，由於語音片段長短不一，所以去除少於1s的短片段，對於長片段則切分為不超過3s的片段

mfcc_dim = 13
sr = 16000
min_length = 1 * sr
slice_length = 3 * sr

def load_and_trim(path, sr=16000):
audio = np.memmap(path, dtype='h', mode='r')
audio = audio[2000:-2000]
audio = audio.astype(np.float32)
energy = librosa.feature.rmse(audio)
frames = np.nonzero(energy >= np.max(energy) / 5)
indices = librosa.core.frames_to_samples(frames)[1]
audio = audio[indices[0]:indices[-1]] if indices.size else audio[0:0]

slices = []
for i in range(0, audio.shape[0], slice_length):
s = audio[i: i + slice_length]
if s.shape[0] >= min_length:
slices.append(s)

return audio, slices

def pcm2wav(pcm_path, wav_path, channels=1, bits=16, sample_rate=sr):
data = open(pcm_path, 'rb').read()
fw = wave.open(wav_path, 'wb')
fw.setnchannels(channels)
fw.setsampwidth(bits // 8)
fw.setframerate(sample_rate)
fw.writeframes(data)
fw.close()

def visualize(index, source='train'):
if source == 'train':
path = train_files[index]
else:
path = dev_files[index]
print(path)

audio, slices = load_and_trim(path)
print('Duration: %.2f s' % (audio.shape[0] / sr))
plt.figure(figsize=(12, 3))
plt.plot(np.arange(len(audio)), audio)
plt.title('Raw Audio Signal')
plt.xlabel('Time')
plt.ylabel('Audio Amplitude')
plt.show()

feature = mfcc(audio, sr, numcep=mfcc_dim)
print('Shape of MFCC:', feature.shape)
fig = plt.figure(figsize=(12, 5))
ax = fig.add_subplot(111)
im = ax.imshow(feature, cmap=plt.cm.jet, aspect='auto')
plt.title('Normalized MFCC')
plt.ylabel('Time')
plt.xlabel('MFCC Coefficient')
plt.colorbar(im, cax=make_axes_locatable(ax).append_axes('right', size='5%', pad=0.05))
ax.set_xticks(np.arange(0, 13, 2), minor=False);
plt.show()

wav_path = 'example.wav'
pcm2wav(path, wav_path)

return wav_path

Audio(visualize(2))
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一句長沙話對應的波形和MFCC特徵

整理資料，檢視語音片段的長度分佈，最後得到了18890個訓練片段，1632個驗證片段

X_train = []
X_dev = []
Y_train = []
Y_dev = []
lengths = []

for i in tqdm(range(len(train_files))):
path = train_files[i]
audio, slices = load_and_trim(path)
lengths.append(audio.shape[0] / sr)
for s in slices:
X_train.append(mfcc(s, sr, numcep=mfcc_dim))
Y_train.append(labels['train'][i])

for i in tqdm(range(len(dev_files))):
path = dev_files[i]
audio, slices = load_and_trim(path)
lengths.append(audio.shape[0] / sr)
for s in slices:
X_dev.append(mfcc(s, sr, numcep=mfcc_dim))
Y_dev.append(labels['dev'][i])

print(len(X_train), len(X_dev))
plt.hist(lengths, bins=100)
plt.show()
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將MFCC特徵進行歸一化

samples = np.vstack(X_train)
mfcc_mean = np.mean(samples, axis=0)
mfcc_std = np.std(samples, axis=0)
print(mfcc_mean)
print(mfcc_std)

X_train = [(x - mfcc_mean) / (mfcc_std + 1e-14) for x in X_train]
X_dev = [(x - mfcc_mean) / (mfcc_std + 1e-14) for x in X_dev]

maxlen = np.max([x.shape[0] for x in X_train + X_dev])
X_train = pad_sequences(X_train, maxlen, 'float32', padding='post', value=0.0)
X_dev = pad_sequences(X_dev, maxlen, 'float32', padding='post', value=0.0)
print(X_train.shape, X_dev.shape)
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對分類標籤進行處理

from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical

le = LabelEncoder()
Y_train = le.fit_transform(Y_train)
Y_dev = le.transform(Y_dev)
print(le.classes_)

class2id = {c: i for i, c in enumerate(le.classes_)}
id2class = {i: c for i, c in enumerate(le.classes_)}

num_class = len(le.classes_)
Y_train = to_categorical(Y_train, num_class)
Y_dev = to_categorical(Y_dev, num_class)
print(Y_train.shape, Y_dev.shape)
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定義產生批資料的迭代器

batch_size = 16

def batch_generator(x, y, batch_size=batch_size): 
offset = 0
while True:
offset += batch_size

if offset == batch_size or offset >= len(x):
x, y = shuffle(x, y)
offset = batch_size

X_batch = x[offset - batch_size: offset]
Y_batch = y[offset - batch_size: offset]

yield (X_batch, Y_batch)
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定義模型並訓練，通過GlobalMaxPooling1D對整個序列的輸出進行降維，從而變成標準的分類任務

epochs = 10
num_blocks = 3
filters = 128
drop_rate = 0.25

X = Input(shape=(None, mfcc_dim,), dtype='float32')

def conv1d(inputs, filters, kernel_size, dilation_rate):
return Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='causal', activation=None, dilation_rate=dilation_rate)(inputs)

def batchnorm(inputs):
return BatchNormalization()(inputs)

def activation(inputs, activation):
return Activation(activation)(inputs)

def res_block(inputs, filters, kernel_size, dilation_rate):
hf = activation(batchnorm(conv1d(inputs, filters, kernel_size, dilation_rate)), 'tanh')
hg = activation(batchnorm(conv1d(inputs, filters, kernel_size, dilation_rate)), 'sigmoid')
h0 = Multiply()([hf, hg])

ha = activation(batchnorm(conv1d(h0, filters, 1, 1)), 'tanh')
hs = activation(batchnorm(conv1d(h0, filters, 1, 1)), 'tanh')

return Add()([ha, inputs]), hs

h0 = activation(batchnorm(conv1d(X, filters, 1, 1)), 'tanh')
shortcut = []
for i in range(num_blocks):
for r in [1, 2, 4, 8, 16]:
h0, s = res_block(h0, filters, 7, r)
shortcut.append(s)

h1 = activation(Add()(shortcut), 'relu')
h1 = activation(batchnorm(conv1d(h1, filters, 1, 1)), 'relu') # batch_size, seq_len, filters
h1 = batchnorm(conv1d(h1, num_class, 1, 1)) # batch_size, seq_len, num_class
h1 = GlobalMaxPooling1D()(h1) # batch_size, num_class
Y = activation(h1, 'softmax')

optimizer = Adam(lr=0.01, clipnorm=5)
model = Model(inputs=X, outputs=Y)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath='fangyan.h5', verbose=0)
lr_decay = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=1, min_lr=0.000)

history = model.fit_generator(
generator=batch_generator(X_train, Y_train), 
steps_per_epoch=len(X_train) // batch_size,
epochs=epochs, 
validation_data=batch_generator(X_dev, Y_dev), 
validation_steps=len(X_dev) // batch_size, 
callbacks=[checkpointer, lr_decay])
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繪製損失函式曲線和正確率曲線，經過10輪的訓練後，訓練集的正確率已經將近100%，而驗證集則不太穩定，大概在89%左右

train_loss = history.history['loss']
valid_loss = history.history['val_loss']
plt.plot(train_loss, label='train')
plt.plot(valid_loss, label='valid')
plt.legend(loc='upper right')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()

train_acc = history.history['acc']
valid_acc = history.history['val_acc']
plt.plot(train_acc, label='train')
plt.plot(valid_acc, label='valid')
plt.legend(loc='upper right')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()
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驗證集結果不夠好的原因可能是訓練資料不足，雖然一共有1W8條訓練資料，但實際上只有90個說話人

如果說話人更多一些、聲音更多樣一些，模型應該能夠學到各種方言所對應的更為通用的特徵

儲存分類和方言名稱之間的對映，以便後續使用

with open('resources.pkl', 'wb') as fw:
pickle.dump([class2id, id2class, mfcc_mean, mfcc_std], fw)
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在單機上載入訓練好的模型，隨機選擇一條語音進行分類

# -*- coding:utf-8 -*-

import numpy as np
from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
import librosa
from python_speech_features import mfcc
import pickle
import wave
import glob

with open('resources.pkl', 'rb') as fr:
[class2id, id2class, mfcc_mean, mfcc_std] = pickle.load(fr)

model = load_model('fangyan.h5')

paths = glob.glob('data/*/dev/*/*/*.pcm')
path = np.random.choice(paths, 1)[0]
label = path.split('/')[1]
print(label, path)

mfcc_dim = 13
sr = 16000
min_length = 1 * sr
slice_length = 3 * sr

def load_and_trim(path, sr=16000):
audio = np.memmap(path, dtype='h', mode='r')
audio = audio[2000:-2000]
audio = audio.astype(np.float32)
energy = librosa.feature.rmse(audio)
frames = np.nonzero(energy >= np.max(energy) / 5)
indices = librosa.core.frames_to_samples(frames)[1]
audio = audio[indices[0]:indices[-1]] if indices.size else audio[0:0]

slices = []
for i in range(0, audio.shape[0], slice_length):
s = audio[i: i + slice_length]
slices.append(s)

return audio, slices

audio, slices = load_and_trim(path)
X_data = [mfcc(s, sr, numcep=mfcc_dim) for s in slices]
X_data = [(x - mfcc_mean) / (mfcc_std + 1e-14) for x in X_data]
maxlen = np.max([x.shape[0] for x in X_data])
X_data = pad_sequences(X_data, maxlen, 'float32', padding='post', value=0.0)
print(X_data.shape)

prob = model.predict(X_data)
prob = np.mean(prob, axis=0)
pred = np.argmax(prob)
prob = prob[pred]
pred = id2class[pred]
print('True:', label)
print('Pred:', pred, 'Confidence:', prob)
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最後再提一下，既然是對三維tensor做分類，那麼就和文字分類問題極其相似，所以也可以考慮使用BiLSTM之類的其他模型