簡介
介紹自動語音識別（Automatic Speech Recognition，ASR）的原理，並用WaveNet實現。
原理
ASR的輸入是語音片段，輸出是對應的文本內容
使用深度神經網絡（Deep Neural Networks，DNN）實現ASR的一般流程如下
- 從原始語音到聲學特徵
- 將聲學特徵輸入到神經網絡，輸出對應的概率
- 根據概率輸出文本序列
一種常用的聲學特徵是梅爾頻率倒譜系數（Mel Frequency Cepstral Coefficents，MFCC），http://www.practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
將原始語音切分成小的片段後，根據每個片段計算對應的MFCC特徵，即可得到一個二維數組
其中第一個維度爲小片段的個數，原始語音越長，第一個維度也越大，第二個維度爲MFCC特徵的維度
得到原始語音的數值表示後，就可以使用WaveNet實現ASR
WaveNet模型結構如下所示，主要使用了多層因果空洞卷積（Causal Dilated Convolution）和Skip Connections
由於MFCC特徵爲一維序列，所以使用Conv1D進行卷積
因果是指，卷積的輸出只和當前位置之前的輸入有關，即不使用未來的特徵，可以理解爲將卷積的位置向前偏移
空洞是指，卷積是跳躍進行的，經過多次堆疊後可以有效地擴大感受野，從而學習到長序列之間的依賴
最後一層卷積的特徵圖個數和字典大小相同，經過softmax處理之後，每一個小片段對應的MFCC都能得到在整個字典上的概率分佈
但小片段的個數一般要大於文本內容中字的個數，即使是同一句話，每個字的持續時間和發音輕重，字之間的停頓時間，也都有無數種可能的變化
在之前的中文分詞中，模型輸出的概率序列和標籤序列的長度一致，而在ASR中則不一樣，類似的問題還有光學字符識別（Optical Character Recognition，OCR）等
這裏使用CTC（Connectionist temporal classification）算法來計算損失函數，https://zhuanlan.zhihu.com/p/36488476
數據
使用以下數據，http://www.openslr.org/18/，包括13388條中文語音文件以及對應的文本標註
實現
用到一些庫，沒有則安裝
pip install python_speech_features librosa
代碼運行過程中如果出現NoBackendError，運行以下命令安裝ffmpeg
conda install -c conda-forge ffmpeg
加載庫
# -*- coding: utf-8 -*-
from keras.models import Model
from keras.layers import Input, Activation, Conv1D, Lambda, Add, Multiply, BatchNormalization
from keras.optimizers import Adam, SGD
from keras import backend as K
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
import random
import pickle
import glob
from tqdm import tqdm
import os
from python_speech_features import mfcc
import scipy.io.wavfile as wav
import librosa
from IPython.display import Audio
加載文本標註路徑並查看
text_paths = glob.glob('data/*.trn')
total = len(text_paths)
print(total)
with open(text_paths[0], 'r', encoding='utf8') as fr:
lines = fr.readlines()
print(lines)
提取文本標註和語音文件路徑，保留中文並去掉空格
texts = []
paths = []
for path in text_paths:
with open(path, 'r', encoding='utf8') as fr:
lines = fr.readlines()
line = lines[0].strip('\n').replace(' ', '')
texts.append(line)
paths.append(path.rstrip('.trn'))
print(paths[0], texts[0])
MFCC特徵保留13維，定義加載語音文件並去掉兩端靜音的函數，以及可視化語音文件的函數
mfcc_dim = 13
def load_and_trim(path):
audio, sr = librosa.load(path)
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]
return audio, sr
def visualize(index):
path = paths[index]
text = texts[index]
print('Audio Text:', text)
audio, sr = load_and_trim(path)
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, nfft=551)
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()
return path
Audio(visualize(0))
第一條語音文件對應的原始波形和MFCC特徵如下所示
獲取全部語音文件對應的MFCC特徵
features = []
for i in tqdm(range(total)):
path = paths[i]
audio, sr = load_and_trim(path)
features.append(mfcc(audio, sr, numcep=mfcc_dim, nfft=551))
print(len(features), features[0].shape)
將MFCC特徵進行歸一化
samples = random.sample(features, 100)
samples = np.vstack(samples)
mfcc_mean = np.mean(samples, axis=0)
mfcc_std = np.std(samples, axis=0)
print(mfcc_mean)
print(mfcc_std)
features = [(feature - mfcc_mean) / (mfcc_std + 1e-14) for feature in features]
建立字典，共2883個不同的字
chars = {}
for text in texts:
for c in text:
chars[c] = chars.get(c, 0) + 1
chars = sorted(chars.items(), key=lambda x: x[1], reverse=True)
chars = [char[0] for char in chars]
print(len(chars), chars[:100])
char2id = {c: i for i, c in enumerate(chars)}
id2char = {i: c for i, c in enumerate(chars)}
劃分訓練數據和測試數據，定義產生批數據的函數
data_index = np.arange(total)
np.random.shuffle(data_index)
train_size = int(0.9 * total)
test_size = total - train_size
train_index = data_index[:train_size]
test_index = data_index[train_size:]
X_train = [features[i] for i in train_index]
Y_train = [texts[i] for i in train_index]
X_test = [features[i] for i in test_index]
Y_test = [texts[i] for i in test_index]
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):
data_index = np.arange(len(x))
np.random.shuffle(data_index)
x = [x[i] for i in data_index]
y = [y[i] for i in data_index]
offset = batch_size
X_data = x[offset - batch_size: offset]
Y_data = y[offset - batch_size: offset]
X_maxlen = max([X_data[i].shape[0] for i in range(batch_size)])
Y_maxlen = max([len(Y_data[i]) for i in range(batch_size)])
X_batch = np.zeros([batch_size, X_maxlen, mfcc_dim])
Y_batch = np.ones([batch_size, Y_maxlen]) * len(char2id)
X_length = np.zeros([batch_size, 1], dtype='int32')
Y_length = np.zeros([batch_size, 1], dtype='int32')
for i in range(batch_size):
X_length[i, 0] = X_data[i].shape[0]
X_batch[i, :X_length[i, 0], :] = X_data[i]
Y_length[i, 0] = len(Y_data[i])
Y_batch[i, :Y_length[i, 0]] = [char2id[c] for c in Y_data[i]]
inputs = {'X': X_batch, 'Y': Y_batch, 'X_length': X_length, 'Y_length': Y_length}
outputs = {'ctc': np.zeros([batch_size])}
yield (inputs, outputs)
定義訓練參數和模型結構並開始訓練
epochs = 50
num_blocks = 3
filters = 128
X = Input(shape=(None, mfcc_dim,), dtype='float32', name='X')
Y = Input(shape=(None,), dtype='float32', name='Y')
X_length = Input(shape=(1,), dtype='int32', name='X_length')
Y_length = Input(shape=(1,), dtype='int32', name='Y_length')
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')
Y_pred = activation(batchnorm(conv1d(h1, len(char2id) + 1, 1, 1)), 'softmax')
sub_model = Model(inputs=X, outputs=Y_pred)
def calc_ctc_loss(args):
y, yp, ypl, yl = args
return K.ctc_batch_cost(y, yp, ypl, yl)
ctc_loss = Lambda(calc_ctc_loss, output_shape=(1,), name='ctc')([Y, Y_pred, X_length, Y_length])
model = Model(inputs=[X, Y, X_length, Y_length], outputs=ctc_loss)
optimizer = SGD(lr=0.02, momentum=0.9, nesterov=True, clipnorm=5)
model.compile(loss={'ctc': lambda ctc_true, ctc_pred: ctc_pred}, optimizer=optimizer)
checkpointer = ModelCheckpoint(filepath='asr.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_test, Y_test),
validation_steps=len(X_test) // batch_size,
callbacks=[checkpointer, lr_decay])
保存模型和字典
sub_model.save('asr.h5')
with open('dictionary.pkl', 'wb') as fw:
pickle.dump([char2id, id2char, mfcc_mean, mfcc_std], fw)
繪製訓練過程中的損失函數曲線
train_loss = history.history['loss']
valid_loss = history.history['val_loss']
plt.plot(np.linspace(1, epochs, epochs), train_loss, label='train')
plt.plot(np.linspace(1, epochs, epochs), valid_loss, label='valid')
plt.legend(loc='upper right')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()
加載模型，隨機對訓練集和測試集中的語音進行識別
from keras.models import load_model
import pickle
with open('dictionary.pkl', 'rb') as fr:
[char2id, id2char, mfcc_mean, mfcc_std] = pickle.load(fr)
sub_model = load_model('asr.h5')
def random_predict(x, y):
index = np.random.randint(len(x))
feature = x[index]
text = y[index]
pred = sub_model.predict(np.expand_dims(feature, axis=0))
pred_ids = K.eval(K.ctc_decode(pred, [feature.shape[0]], greedy=False, beam_width=10, top_paths=1)[0][0])
pred_ids = pred_ids.flatten().tolist()
print('True transcription:\n-- ', text, '\n')
print('Predicted transcription:\n-- ' + ''.join([id2char[i] for i in pred_ids]), '\n')
random_predict(X_train, Y_train)
random_predict(X_test, Y_test)
訓練集中隨機選擇一條語音，正確文本和識別文本分別爲
- 而此時正趕上咸陽地市機構變化原咸陽市改爲秦都區咸陽地區改爲咸陽市
- 而此時正趕上咸陽地市機構變化原咸陽市改爲秦都區咸陽地區改爲咸陽市
測試集中隨機選擇一條語音，正確文本和識別文本分別爲
- 全黨必須緊緊團結在以江澤民同志爲核心的黨中央周圍一心一意穩紮穩打共創未來
- 人南必須經緊團結在以江澤民同志威核心的黨中央州圍一心一穩教扎穩打共創未
在本地上加載訓練好的模型，隨機選擇錄音文件並識別
# -*- coding: utf-8 -*-
from keras.models import load_model
from keras import backend as K
import numpy as np
import librosa
from python_speech_features import mfcc
import pickle
import glob
wavs = glob.glob('data/*.wav')
with open('dictionary.pkl', 'rb') as fr:
[char2id, id2char, mfcc_mean, mfcc_std] = pickle.load(fr)
mfcc_dim = 13
model = load_model('asr.h5')
index = np.random.randint(len(wavs))
print(wavs[index])
audio, sr = librosa.load(wavs[index])
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]
X_data = mfcc(audio, sr, numcep=mfcc_dim, nfft=551)
X_data = (X_data - mfcc_mean) / (mfcc_std + 1e-14)
print(X_data.shape)
with open(wavs[index] + '.trn', 'r', encoding='utf8') as fr:
label = fr.readlines()[0]
print(label)
pred = model.predict(np.expand_dims(X_data, axis=0))
pred_ids = K.eval(K.ctc_decode(pred, [X_data.shape[0]], greedy=False, beam_width=10, top_paths=1)[0][0])
pred_ids = pred_ids.flatten().tolist()
print(''.join([id2char[i] for i in pred_ids]))
參考
- WaveNet: A Generative Model for Raw Audio：https://arxiv.org/abs/1609.03499
- VUI Capstone Project - Udacity：https://github.com/udacity/AIND-VUI-Capstone
- Speech-to-Text-WaveNet：https://github.com/buriburisuri/speech-to-text-wavenet