[代码重构中]重构model_loader与audio_chunk，全局单例模式管理模型加载与audiobinary数据存储单元类。删除readme中不需要的MIT许可证。
This commit is contained in:
Ziyang.Zhang
parent
040fc57e02
commit
703a40e955
@ -108,6 +108,3 @@ docker-compose up -d
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"is_final": false // 是否是最终结果
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}
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```
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## 许可证
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[MIT](LICENSE)
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194
src/audio_chunk.py
Normal file
194
src/audio_chunk.py
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@ -0,0 +1,194 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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音频数据块管理类 - 用于存储和处理16KHz音频数据
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"""
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from typing import List, Optional, Dict
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from src.models import AudioBinary_Config, AudioBinary_data_list
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# 配置日志
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from src.utils.logger import get_module_logger
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logger = get_module_logger(__name__, level="INFO")
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class AudioBinary:
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"""
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音频数据存储单元
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用于存储二进制数据
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面向Slice, 向Slice提供数据与接口
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self._audio_config: AudioBinary_Config -- 音频参数配置
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self._binary_data_list: AudioBinary_data_list -- 音频数据列表
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self._slice_listener: List[callable] -- 切片监听器
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AudioBinary_Config: Dict -- 音频参数配置
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AudioBinary_data_list: List[bytes] -- 音频数据列表
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"""
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def __init__(self, *args):
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"""
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初始化音频数据块
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参数:
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*args: 可变参数
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"""
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# 音频参数配置
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self._audio_config = AudioBinary_Config()
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# 音频片段
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self._binary_data_list: AudioBinary_data_list = AudioBinary_data_list()
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# 切片监听器
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self._slice_listener: List = []
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if isinstance(args, Dict):
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self._audio_config = AudioBinary_Config.AudioBinary_Config_from_dict(args)
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elif isinstance(args, AudioBinary_Config):
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self._audio_config = args
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else:
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raise ValueError("参数类型错误")
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def add_slice_listener(self, slice_listener: callable):
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"""
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添加切片监听器
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参数:
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slice_listener: callable -- 切片监听器
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"""
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self._slice_listener.append(slice_listener)
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def __add__(self, other: bytes):
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"""
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__add__ 是 "+" 运算符的重载，
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使用方法:
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audio_binary = audio_binary + bytes
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添加音频数据块 与 add_binary_data 等效，
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但可以链式调用, 方便使用
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参数:
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other: bytes --音频数据块
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"""
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self._binary_data_list.append(other)
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return self
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def __iadd__(self, other: bytes):
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"""
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__iadd__ 是 "+=" 运算符的重载，
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使用方法:
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audio_binary += bytes
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添加音频数据块 与 add_binary_data 等效，
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但可以链式调用, 方便使用
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参数:
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other: bytes --音频数据块
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"""
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self._binary_data_list.append(other)
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return self
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def add_binary_data(self, binary_data: bytes):
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"""
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添加音频数据块
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参数:
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binary_data: bytes --音频数据块
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"""
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self._binary_data_list.append(binary_data)
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def rewrite_binary_data(self, target_index: int, binary_data: bytes):
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"""
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重写音频数据块
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参数:
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target_index: int -- 目标索引
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binary_data: bytes --音频数据块
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"""
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self._binary_data_list.rewrite(target_index, binary_data)
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def get_binary_data(
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self,
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start: int = 0,
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end: Optional[int] = None,
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) -> Optional[bytes]:
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"""
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获取指定索引的音频数据块
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参数:
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start: 开始索引
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end: 结束索引
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返回:
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List[bytes]: 音频数据块
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"""
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if start >= len(self._binary_data_list):
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return None
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if end is None:
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end = start + 1
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end = min(end, len(self._binary_data_list))
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return self._binary_data_list[start:end]
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class AudioChunk:
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"""
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音频数据块管理类
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管理两部分内容, AudioBinary和Slice。
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AudioBinary用于内部存储字节数据。
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Slice是AudioBinary的切片,用于外部接口。
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此类仅用于AudioBinary与Funtor的交互, 不负责其它逻辑。
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"""
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_instance = None
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def __new__(cls, *args, **kwargs):
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"""
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单例模式
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"""
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if cls._instance is None:
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cls._instance = super(AudioChunk, cls).__new__(cls, *args, **kwargs)
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return cls._instance
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def __init__(self):
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"""
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初始化AudioChunk实例
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"""
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self._audio_binary_list: Dict[str, AudioBinary] = {}
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self._slice_listener: List[callable] = []
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def get_audio_binary(
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self,
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binary_name: Optional[str] = None,
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audio_config: Optional[AudioBinary_Config] = None,
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) -> AudioBinary:
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"""
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获取音频数据块
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参数:
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binary_name: str -- 音频数据块名称
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返回:
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AudioBinary: 音频数据块
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"""
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if binary_name is None:
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binary_name = "default"
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if binary_name not in self._audio_binary_list:
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self._audio_binary_list[binary_name] = AudioBinary(audio_config)
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return self._audio_binary_list[binary_name]
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@staticmethod
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def _time2size(time_ms: int, audio_config: AudioBinary_Config) -> int:
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"""
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将时间(ms)转换为数据大小(字节)
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参数:
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time_ms: int -- 时间(ms)
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audio_config: AudioBinary_Config -- 音频参数配置
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返回:
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int: 数据大小(字节)
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"""
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# 时间(ms)到字节(bytes)计算方法为: 时间(ms) * 采样率(Hz) * 通道数(1 or 2) * 采样位宽(16 or 24) / 1000
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time_s = time_ms / 1000
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bytes_per_sample = audio_config.sample_width * audio_config.channel
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return int(time_s * audio_config.sample_rate * bytes_per_sample)
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@staticmethod
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def _size2time(size: int, audio_config: AudioBinary_Config) -> int:
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"""
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将数据大小(字节)转换为时间(ms)
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参数:
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size: int -- 数据大小(字节)
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audio_config: AudioBinary_Config -- 音频参数配置
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返回:
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int: 时间(ms)
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"""
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# 字节(bytes)到时间(ms)计算方法为: 字节(bytes) * 1000 / (采样率(Hz) * 通道数(1 or 2) * 采样位宽(16 or 24))
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bytes_per_sample = audio_config.sample_width * audio_config.channel
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time_ms = size * 1000 // (audio_config.sample_rate * bytes_per_sample)
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return time_ms
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@ -1,178 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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音频数据块管理类 - 用于存储和处理16KHz音频数据
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"""
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import numpy as np
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from src.utils.logger import get_module_logger
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from typing import List, Optional, Union
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from src.models import AudioBinary_Config
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# 配置日志
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logger = get_module_logger(__name__, level="INFO")
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class AudioChunk:
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"""音频数据块管理类，用于存储和处理16KHz音频数据"""
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def __init__(self,
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max_duration_ms: int = 1000*60*60*10,
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audio_config : AudioBinary_Config = None,
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):
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"""
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初始化音频数据块管理器
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参数:
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max_duration_ms: 音频池最大留存时间(ms)，默认10小时
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audio_config: 音频配置信息
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"""
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# 音频参数
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self.sample_rate = audio_config.sample_rate if audio_config is not None else 16000 # 采样率：16KHz
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self.sample_width = audio_config.sample_width if audio_config is not None else 2 # 采样位宽：16bit
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self.channels = audio_config.channels if audio_config is not None else 1 # 通道数：单声道
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# 数据存储
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self._max_duration_ms = max_duration_ms
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self._max_chunk_size = self._time2size(max_duration_ms) # 最大数据大小
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self._chunk = [] # 当前音频数据块列表
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self._chunk_size = 0 # 当前数据总大小
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self._offset = 0 # 当前偏移量
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logger.info(f"初始化AudioChunk: 最大时长={max_duration_ms}ms, 最大数据大小={self._max_chunk_size}字节")
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def add_chunk(self, chunk: Union[bytes, np.ndarray]) -> bool:
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"""
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添加音频数据块
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参数:
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chunk: 音频数据块，可以是bytes或numpy数组
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返回:
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bool: 是否添加成功
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"""
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try:
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# 检查数据格式
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if isinstance(chunk, np.ndarray):
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# 确保是16bit整数格式
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if chunk.dtype != np.int16:
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chunk = chunk.astype(np.int16)
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# 转换为bytes
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chunk = chunk.tobytes()
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# 检查数据大小
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if len(chunk) % (self.sample_width * self.channels) != 0:
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logger.warning(f"音频数据大小不是{self.sample_width * self.channels}的倍数: {len(chunk)}")
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return False
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# 检查是否超过最大限制
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if self._chunk_size + len(chunk) > self._max_chunk_size:
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logger.warning("音频数据超过最大限制，将自动清除旧数据")
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self.clear_chunk()
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# 添加数据
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self._chunk.append(chunk)
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self._chunk_size += len(chunk)
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return True
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except Exception as e:
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logger.error(f"添加音频数据块时出错: {e}")
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return False
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def get_chunk_binary(self, start: int = 0, end: Optional[int] = None) -> Optional[bytes]:
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"""
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获取指定索引的音频数据块
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"""
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print("[AudioChunk] get_chunk_binary", start, end)
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if start >= len(self._chunk):
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return None
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if end is None or end > len(self._chunk):
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end = len(self._chunk)
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data = b''.join(self._chunk)
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return data[start:end]
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def get_chunk(self, start_ms: int = 0, end_ms: Optional[int] = None) -> Optional[bytes]:
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"""
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获取指定时间范围的音频数据
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参数:
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start_ms: 开始时间(ms)
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end_ms: 结束时间(ms)，None表示到末尾
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返回:
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Optional[bytes]: 音频数据，如果获取失败则返回None
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"""
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try:
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if not self._chunk:
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return None
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# 计算字节偏移
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start_byte = self._time2size(start_ms)
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end_byte = self._time2size(end_ms) if end_ms is not None else self._chunk_size
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# 检查范围是否有效
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if start_byte >= self._chunk_size or start_byte >= end_byte:
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return None
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# 获取数据
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data = b''.join(self._chunk)
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return data[start_byte:end_byte]
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except Exception as e:
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logger.error(f"获取音频数据块时出错: {e}")
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return None
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def get_duration(self) -> int:
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"""
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获取当前音频总时长(ms)
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返回:
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int: 音频时长(ms)
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"""
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return self._size2time(self._chunk_size)
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def clear_chunk(self) -> None:
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"""清除所有音频数据"""
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self._chunk = []
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self._chunk_size = 0
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self._offset = 0
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logger.info("已清除所有音频数据")
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def _time2size(self, time_ms: int) -> int:
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"""
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将时间(ms)转换为数据大小(字节)
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参数:
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time_ms: 时间(ms)
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返回:
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int: 数据大小(字节)
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"""
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return int(time_ms * self.sample_rate * self.sample_width * self.channels / 1000)
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def _size2time(self, size: int) -> int:
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"""
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将数据大小(字节)转换为时间(ms)
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参数:
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size: 数据大小(字节)
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返回:
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int: 时间(ms)
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"""
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return int(size * 1000 / (self.sample_rate * self.sample_width * self.channels))
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# instance(start_ms, end_ms, use_offset=True)
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def __call__(self, start_ms: int = 0, end_ms: Optional[int] = None, use_offset: bool = True) -> Optional[bytes]:
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"""
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获取指定时间范围的音频数据
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"""
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if use_offset:
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start_ms += self._offset
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end_ms += self._offset
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return self.get_chunk(start_ms, end_ms)
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def __len__(self) -> int:
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"""
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获取当前音频数据块大小
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"""
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return self._chunk_size
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@ -1,103 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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模型加载模块 - 负责加载各种语音识别相关模型
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"""
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from typing import List, Optional
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class ModelLoader:
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def __init__(self):
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pass
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def __call__(self, args):
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return self.load_models(args)
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def load_models(self, args):
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"""
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加载所有需要的模型
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参数:
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args: 命令行参数，包含模型配置
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返回:
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dict: 包含所有加载的模型的字典
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"""
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def load_models(args):
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"""
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加载所有需要的模型
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参数:
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args: 命令行参数，包含模型配置
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返回:
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dict: 包含所有加载的模型的字典
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"""
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try:
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# 导入FunASR库
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from funasr import AutoModel
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except ImportError:
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raise ImportError("未找到funasr库，请先安装: pip install funasr")
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# 初始化模型字典
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models = {}
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# 1. 加载离线ASR模型
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print(f"正在加载ASR离线模型: {args.asr_model}")
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models["asr"] = AutoModel(
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model=args.asr_model,
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model_revision=args.asr_model_revision,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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device=args.device,
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disable_pbar=True,
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disable_log=True,
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disable_update=True,
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)
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# 2. 加载在线ASR模型
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print(f"正在加载ASR在线模型: {args.asr_model_online}")
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models["asr_streaming"] = AutoModel(
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model=args.asr_model_online,
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model_revision=args.asr_model_online_revision,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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device=args.device,
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disable_pbar=True,
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disable_log=True,
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disable_update=True,
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)
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# 3. 加载VAD模型
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print(f"正在加载VAD模型: {args.vad_model}")
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models["vad"] = AutoModel(
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model=args.vad_model,
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model_revision=args.vad_model_revision,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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device=args.device,
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disable_pbar=True,
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disable_log=True,
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||||
disable_update=True,
|
||||
)
|
||||
|
||||
# 4. 加载标点符号模型（如果指定）
|
||||
if args.punc_model:
|
||||
print(f"正在加载标点符号模型: {args.punc_model}")
|
||||
models["punc"] = AutoModel(
|
||||
model=args.punc_model,
|
||||
model_revision=args.punc_model_revision,
|
||||
ngpu=args.ngpu,
|
||||
ncpu=args.ncpu,
|
||||
device=args.device,
|
||||
disable_pbar=True,
|
||||
disable_log=True,
|
||||
disable_update=True,
|
||||
)
|
||||
else:
|
||||
models["punc"] = None
|
||||
print("未指定标点符号模型，将不使用标点符号")
|
||||
|
||||
print("所有模型加载完成")
|
||||
return models
|
||||
124
src/model_loader.py
Normal file
124
src/model_loader.py
Normal file
@ -0,0 +1,124 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
模型加载模块 - 负责加载各种语音识别相关模型
|
||||
"""
|
||||
try:
|
||||
# 导入FunASR库
|
||||
from funasr import AutoModel
|
||||
except ImportError as exc:
|
||||
raise ImportError("未找到funasr库, 请先安装: pip install funasr") from exc
|
||||
|
||||
# 日志模块
|
||||
from src.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
# 单例模式
|
||||
class ModelLoader:
|
||||
"""
|
||||
ModelLoader类是单例模式, 程序生命周期全局唯一, 负责加载模型到字典中。
|
||||
一般的, 可以直接call ModelLoader()来获取加载的模型。
|
||||
也可以通过ModelLoader实例(args)或ModelloaderInstance.load_models(args)来初始化, 并加载模型。
|
||||
"""
|
||||
|
||||
_instance = None
|
||||
|
||||
def __new__(cls, *args, **kwargs):
|
||||
"""
|
||||
单例模式
|
||||
"""
|
||||
if cls._instance is None:
|
||||
cls._instance = super(ModelLoader, cls).__new__(cls, *args, **kwargs)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self, args=None):
|
||||
"""
|
||||
初始化ModelLoader实例
|
||||
"""
|
||||
self.models = {}
|
||||
logger.info("初始化ModelLoader")
|
||||
if args is not None:
|
||||
self.__call__(args)
|
||||
|
||||
def __call__(self, args=None):
|
||||
"""
|
||||
调用ModelLoader实例时, 如果模型字典为空, 则加载模型
|
||||
"""
|
||||
# 如果模型字典为空, 则加载模型
|
||||
if self.models == {} or self.models is None:
|
||||
if args.asr_model is not None:
|
||||
self.models = self.load_models(args)
|
||||
# 直接调用等于调用self.models
|
||||
return self.models
|
||||
|
||||
def _load_model(self, args, model_type):
|
||||
"""
|
||||
加载单个模型
|
||||
|
||||
参数:
|
||||
args: 命令行参数, 包含模型配置
|
||||
model_type: 模型类型, 用于确定使用哪个模型参数
|
||||
|
||||
返回:
|
||||
AutoModel: 加载的模型实例
|
||||
"""
|
||||
# 默认配置
|
||||
default_config = {
|
||||
"model": None,
|
||||
"model_revision": None,
|
||||
"ngpu": 0,
|
||||
"ncpu": 1,
|
||||
"device": "cpu",
|
||||
"disable_pbar": True,
|
||||
"disable_log": True,
|
||||
"disable_update": True,
|
||||
}
|
||||
# 从args中获取配置, 如果存在则覆盖默认值
|
||||
model_args = default_config.copy()
|
||||
for key, value in default_config.items():
|
||||
if key in ["model", "model_revision"]:
|
||||
# 特殊处理model和model_revision, 因为它们需要model_type前缀
|
||||
if key == "model":
|
||||
value = getattr(args, f"{model_type}_model", None)
|
||||
else:
|
||||
value = getattr(args, f"{model_type}_model_revision", None)
|
||||
else:
|
||||
value = getattr(args, key, None)
|
||||
if value is not None:
|
||||
model_args[key] = value
|
||||
# 验证必要参数
|
||||
if not model_args["model"]:
|
||||
raise ValueError(f"未指定{model_type}模型路径")
|
||||
try:
|
||||
# 使用 % 格式化替代 f-string,避免不必要的字符串格式化开销
|
||||
logger.info("正在加载%s模型: %s", model_type, model_args["model"])
|
||||
model = AutoModel(**model_args)
|
||||
return model
|
||||
except Exception as e:
|
||||
logger.error("加载%s模型失败: %s", model_type, str(e))
|
||||
raise
|
||||
|
||||
def load_models(self, args):
|
||||
"""
|
||||
加载所有需要的模型
|
||||
参数:
|
||||
args: 命令行参数, 包含模型配置
|
||||
|
||||
返回:
|
||||
dict: 包含所有加载的模型的字典
|
||||
"""
|
||||
logger.info("ModelLoader加载模型")
|
||||
# 初始化模型字典
|
||||
self.models = {}
|
||||
# 加载离线ASR模型
|
||||
self.models["asr"] = self._load_model(args, "asr")
|
||||
# 2. 加载在线ASR模型
|
||||
self.models["asr_streaming"] = self._load_model(args, "asr_online")
|
||||
# 3. 加载VAD模型
|
||||
self.models["vad"] = self._load_model(args, "vad")
|
||||
# 4. 加载标点符号模型（如果指定）
|
||||
self.models["punc"] = self._load_model(args, "punc")
|
||||
logger.info("所有模型加载完成")
|
||||
return self.models
|
||||
@ -1,3 +1,3 @@
|
||||
from .audiobinary import AudioBinary_Config, AudioBinary_Chunk
|
||||
from .audio import AudioBinary_Config, AudioBinary_data_list, AudioBinary_Slice
|
||||
from .vad import VADResponse
|
||||
__all__ = ["AudioBinary_Config", "AudioBinary_Chunk", "VADResponse"]
|
||||
__all__ = ["AudioBinary_Config", "AudioBinary_data_list", "AudioBinary_Slice", "VADResponse"]
|
||||
36
src/models/audio.py
Normal file
36
src/models/audio.py
Normal file
@ -0,0 +1,36 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from src.audiochunk import AudioBinary
|
||||
|
||||
class AudioBinary_Config(BaseModel):
|
||||
"""二进制音频块配置信息"""
|
||||
audio_data: bytes = Field(description="音频数据", default=None)
|
||||
chunk_size: int = Field(description="块大小", default=100)
|
||||
chunk_stride: int = Field(description="块步长", default=1600)
|
||||
sample_rate: int = Field(description="采样率", default=16000)
|
||||
sample_width: int = Field(description="采样位宽", default=2)
|
||||
channels: int = Field(description="通道数", default=1)
|
||||
|
||||
# 从Dict中加载
|
||||
@classmethod
|
||||
def AudioBinary_Config_from_dict(cls, data: dict):
|
||||
return cls(**data)
|
||||
|
||||
class AudioBinary_Slice(BaseModel):
|
||||
"""音频块切片"""
|
||||
target_Binary: AudioBinary = Field(description="目标音频块", default=None)
|
||||
start_index: int = Field(description="开始位置", default=0)
|
||||
end_index: int = Field(description="结束位置", default=0)
|
||||
|
||||
def __call__(self):
|
||||
return self.target_Binary(self.start_index, self.end_index)
|
||||
|
||||
class _AudioBinary_data(BaseModel):
|
||||
"""音频二进制数据"""
|
||||
binary_data: bytes = Field(description="音频二进制数据", default=None)
|
||||
|
||||
class AudioBinary_data_list(BaseModel):
|
||||
"""音频二进制数据列表"""
|
||||
binary_data_list: List[_AudioBinary_data] = Field(description="音频二进制数据列表", default=[])
|
||||
|
||||
def __call__(self):
|
||||
return self.binary_data_list
|
||||
@ -1,16 +0,0 @@
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class AudioBinary_Config(BaseModel):
|
||||
"""二进制音频块配置信息"""
|
||||
audio_data: bytes = Field(description="音频数据", default=None)
|
||||
chunk_size: int = Field(description="块大小", default=100)
|
||||
chunk_stride: int = Field(description="块步长", default=1600)
|
||||
sample_rate: int = Field(description="采样率", default=16000)
|
||||
sample_width: int = Field(description="采样位宽", default=2)
|
||||
channels: int = Field(description="通道数", default=1)
|
||||
|
||||
class AudioBinary_Chunk(BaseModel):
|
||||
"""音频块"""
|
||||
start_time: int = Field(description="开始时间", default=0)
|
||||
end_time: int = Field(description="结束时间", default=0)
|
||||
chunk: bytes = Field(description="音频块", default=None)
|
||||
0
src/runner.py
Normal file
0
src/runner.py
Normal file
3
src/utils/__init__.py
Normal file
3
src/utils/__init__.py
Normal file
@ -0,0 +1,3 @@
|
||||
from .logger import get_module_logger, setup_root_logger
|
||||
|
||||
__all__ = ["get_module_logger", "setup_root_logger"]
|
||||
@ -39,8 +39,10 @@ def vad_model_use_online_logic(file_path: str) -> List[Dict[str, Any]]:
|
||||
from src.config import parse_args
|
||||
args = parse_args()
|
||||
|
||||
from src.functor.model_loader import load_models
|
||||
models = load_models(args)
|
||||
# from src.functor.model_loader import load_models
|
||||
# models = load_models(args)
|
||||
from src.model_loader import ModelLoader
|
||||
models = ModelLoader(args)
|
||||
|
||||
chunk_size = 200 # ms
|
||||
from src.models import AudioBinary_Config
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user