中文语音识别离线开发开源项目是指使用Python等编程语言，借助开源语音识别库（如TensorFlow、PyTorch、Kaldi等）和深度学习技术，实现对中文语音的识别功能。这些项目通常用于教育、娱乐、智能家居等领域，为用户提供语音交互体验。以下是一个简单的中文语音识别离线开发示例：
1. 首先，安装所需库：
```bash
pip install tensorflow
pip install pyaudio
pip install scipy
pip install numpy
pip install sklearn
pip install opencv-python
```
2. 编写代码：
```python
import cv2
import numpy as np
import pyaudio
import threading
from keras.models import load_model
from collections import deque
import os
from keras.preprocessing import sequence
from skspeech import sr, recognizers
# 加载预训练模型
model = load_model('path/to/your/model.h5')
# 初始化音频输入
input_data = []
def record_audio(file):
"""录音函数"""
cap = cv2.VideoCapture(file)
while True:
ret, frame = cap.read()
if not ret:
break
input_data.append(frame)
cap.release()
return input_data
# 初始化音频输出
output_data = []
def play_sound(sound):
"""播放音频函数"""
os.system('play {}'.format(sound))
# 初始化线程
threads = []
def record():
"""录音线程"""
audio_file = 'recorded_audio.wav'
audio_data = record_audio(audio_file)
sound = 'recorded_audio.wav'
play_sound(sound)
for i in range(len(audio_data)):
input_data.append(np.frombuffer(audio_data[i], dtype=np.int32))
output_data.append(np.frombuffer(audio_data[i], dtype=np.int32))
# 启动录音线程
thread = threading.Thread(target=record)
thread.start()
# 等待录音线程结束
thread.join()
# 识别音频文件
for i in range(len(input_data)):
recognizers.recognize_google(input_data[i], show_all=True, language='zh-CN', max_decoding_delay=0.05)
recognizers.recognize_google(input_data[i], show_all=True, language='en-US', max_decoding_delay=0.05)
```
3. 运行代码：
```bash
python offline_voice_recognition.py
```
4. 编译并运行模型：
```bash
python -m compile_model.py --model_name "path/to/your/model.h5" --output_dir "path/to/your/output_dir" --saved_models_dir "path/to/your/saved_models_dir" --num_classes 96 --checkpoint_dir "path/to/your/checkpoint_dir" --load_checkpoint "path/to/your/checkpoint_dir/checkpoint-00000.pth" --use_cuda "yes" --gpu_id "0" --device_count "1" --batch_size 16 --seq_length 16 --num_epochs 100 --learning_rate 0.001 --weight_decay 0.0005 --seed 1234567890 --log_interval 1000 --save_steps 1000000000 --save_freq 1000000000 --train_dir "path/to/your/train_dir" --validation_dir "path/to/your/validation_dir" --test_dir "path/to/your/test_dir" --overwrite_cache "yes" --evaluate_every "100000" --evaluation_freq "100000" --evaluate_on_best "yes" --evaluate_on_best_freq "100000" --evaluate_on_best_accuracy "yes" --evaluate_on_best_accuracy_freq "100000" --evaluate_on_best_accuracy_freq "100000" --evaluate_on_best_accuracy_frequency "100000" --evaluate_on_best_accuracy_frequency "100000" --evaluate_on_best_accuracy_time "100000" --evaluate_on_best_accuracy_time "100000" --evaluation_on_best_freq "100000" --evaluation_on_best_freq "100000" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation_on_best_accuracy "yes" --evaluation
