语音识别器
- 环境
* - 主要包
- 环境展示
- 写作过程
* - 1.导入相关包
- 2.定义类来创建隐马尔科夫模型
- 3.定义一个函数来解析其中的命令
- 4.定义主函数
- 5.运行，这里运行是在终端进行运行的。
- 注意事项
- 参考书籍
* - 代码参考
- 基础知识学习
- 完整代码
环境
这里我用的linux下kali虚拟机。
Anaconda环境，pycharm编译器。python3.6.13
主要包
直接就是清华源下载，清华源是真的好用，谁用谁知道
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple numpy
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple scipy
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple hmmlearn
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple python_speech_features
环境展示
这里使用的单词的音频是我们在上一篇文章中通过文本发音保存的音频。
[En]
The audio of the words used here is the audio we saved through text pronunciation in the previous article.
; 写作过程
1.导入相关包
import os
import argparse
import numpy as np
from scipy.io import wavfile
from hmmlearn import hmm
from python_speech_features import mfcc
2.定义类来创建隐马尔科夫模型
class HMMTrainer(object):
def __init__(self, model_name='GaussianHMM', n_components=4, cov_type='diag', n_iter=1000):
self.model_name = model_name
self.n_components = n_components
self.cov_type = cov_type
self.n_iter = n_iter
self.models = []
if self.model_name == 'GaussianHMM':
self.model = hmm.GaussianHMM(n_components=self.n_components,
covariance_type=self.cov_type, n_iter=self.n_iter)
else:
raise TypeError('Invalid model type')
def train(self, X):
np.seterr(all='ignore')
self.models.append(self.model.fit(X))
def get_score(self, input_data):
return self.model.score(input_data)
3.定义一个函数来解析其中的命令
def build_arg_parse():
parser = argparse.ArgumentParser(description='Trains the HMM classifier')
parser.add_argument("--input-folder", dest="input_folder", required=True,
help="Input folder containing the audio files insubfolders")
return parser
4.定义主函数
if __name__ == '__main__':
args = build_arg_parse().parse_args()
input_folder = args.input_folder
hmm_models = []
for dirname in os.listdir(input_folder):
subfolder = os.path.join(input_folder, dirname)
if not os.path.isdir(subfolder):
continue
label = subfolder[subfolder.rfind('/') + 1:]
X = np.array([])
y_words = []
"""
for x in os.listdir(subfolder):
if x.endswith('.wav'):
print(x)
"""
for filename in [x for x in os.listdir(subfolder) if x.endswith('.wav')]:
print(filename)
filepath = os.path.join(subfolder, filename)
sampling_freq, audio = wavfile.read(filepath)
print(sampling_freq)
mfcc_features = mfcc(audio, sampling_freq)
if len(X) == 0:
X = mfcc_features
else:
X = np.append(X, mfcc_features, axis=0)
y_words.append(label)
hmm_trainer = HMMTrainer()
hmm_trainer.train(X)
hmm_models.append((hmm_trainer, label))
hmm_trainer = None
input_files = ['audio_files/hello/hello.wav',
'audio_files/linux/linux.wav',
'audio_files/python/python.wav',
'audio_files/windows/windows.wav',
'audio_files/你好/你好.wav',
'audio_files/place/place.wav',
'audio_files/variables/variables.wav']
for input_file in input_files:
sampling_freq, audio = wavfile.read(input_file)
mfcc_features = mfcc(audio, sampling_freq)
max_score = 0
output_label = None
for item in hmm_models:
hmm_model, label = item
score = hmm_model.get_score(mfcc_features)
if score > max_score:
max_score = score
output_label = label
print('\nTrue:', input_file[input_file.find('/') + 1:input_file.rfind('/')])
print('Predicted:', output_label)
5.运行，这里运行是在终端进行运行的。
python speech_recognizer.py --input-folder audio_files
注意事项
上面运行的代码有问题：
[En]
There is a problem with the above code running:
这个报错不影响运行，但影响语音识别的准确率。
解决：网上查找资料说是帧率的问题。需要 把22kHz帧率改为16kHz.
原回答：https://github.com/mozilla/DeepSpeech/issues/1888
根据提示，我尝试输出帧率，发现确实是22kHz帧率。
然后是帧速率更改：有两个方面需要更改：
[En]
Then there is the frame rate change: there are two areas to change:
改变帧率后，语音识别率也有了很大的提高。
[En]
After changing the frame rate, the speech recognition rate is also improved a lot.
; 参考书籍
代码参考
本文代码基本仿照《Python 机器学习经典实例》完成。
英文名：《Python Machine Learning Cookbook》
基础知识学习
深度学习基础知识我参考的是：
《Python深度学习-基于TensorFlow》
完整代码
import os
import argparse
import numpy as np
from scipy.io import wavfile
from hmmlearn import hmm
from python_speech_features import mfcc
class HMMTrainer(object):
def __init__(self, model_name='GaussianHMM', n_components=4, cov_type='diag', n_iter=1000):
self.model_name = model_name
self.n_components = n_components
self.cov_type = cov_type
self.n_iter = n_iter
self.models = []
if self.model_name == 'GaussianHMM':
self.model = hmm.GaussianHMM(n_components=self.n_components,
covariance_type=self.cov_type, n_iter=self.n_iter)
else:
raise TypeError('Invalid model type')
def train(self, X):
np.seterr(all='ignore')
self.models.append(self.model.fit(X))
def get_score(self, input_data):
return self.model.score(input_data)
def build_arg_parse():
parser = argparse.ArgumentParser(description='Trains the HMM classifier')
parser.add_argument("--input-folder", dest="input_folder", required=True,
help="Input folder containing the audio files insubfolders")
return parser
if __name__ == '__main__':
args = build_arg_parse().parse_args()
input_folder = args.input_folder
hmm_models = []
for dirname in os.listdir(input_folder):
subfolder = os.path.join(input_folder, dirname)
if not os.path.isdir(subfolder):
continue
label = subfolder[subfolder.rfind('/') + 1:]
X = np.array([])
y_words = []
"""
for x in os.listdir(subfolder):
if x.endswith('.wav'):
print(x)
"""
for filename in [x for x in os.listdir(subfolder) if x.endswith('.wav')]:
print(filename)
filepath = os.path.join(subfolder, filename)
sampling_freq, audio = wavfile.read(filepath)
print(sampling_freq)
mfcc_features = mfcc(audio, 16000)
if len(X) == 0:
X = mfcc_features
else:
X = np.append(X, mfcc_features, axis=0)
y_words.append(label)
hmm_trainer = HMMTrainer()
hmm_trainer.train(X)
hmm_models.append((hmm_trainer, label))
hmm_trainer = None
input_files = ['audio_files/hello/hello.wav',
'audio_files/linux/linux.wav',
'audio_files/python/python.wav',
'audio_files/windows/windows.wav',
'audio_files/你好/你好.wav',
'audio_files/place/place.wav',
'audio_files/variables/variables.wav']
for input_file in input_files:
sampling_freq, audio = wavfile.read(input_file)
mfcc_features = mfcc(audio, 16000)
max_score = 0
output_label = None
for item in hmm_models:
hmm_model, label = item
score = hmm_model.get_score(mfcc_features)
if score > max_score:
max_score = score
output_label = label
print('\nTrue:', input_file[input_file.find('/') + 1:input_file.rfind('/')])
print('Predicted:', output_label)
Original: https://blog.csdn.net/qq_45071353/article/details/123724616
Author: 初冬的早晨
Title: 语音识别器
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