Whisper在AI人工智能语音识别实时性上的优化探索

    class StreamWhisper:
    def __init__(self, model_name="base"):
        self.model = whisper.load_model(model_name)
        self.mel = whisper.audio.Mel()
        self.state = None  # 用于保存中间状态
    
    def process_chunk(self, audio_chunk):
        # 计算当前块的mel特征
        mel = self.mel(audio_chunk)
    
        # 使用前一个状态进行增量推理
        result, self.state = self.model.transcribe(
            mel,
            init_state=self.state,
            no_context=True  # 不使用完整上下文
        )
        return result
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/ay0Ps12UZ5YSntj8rqROKdiBzNIh.png)

    import torch
    from torch.quantization import quantize_dynamic
    
    # 加载原始模型
    model = whisper.load_model("base")
    
    # 对线性层和注意力层进行动态量化
    quantized_model = quantize_dynamic(
    model,
    {torch.nn.Linear, torch.nn.MultiheadAttention},
    dtype=torch.qint8
    )
    
    # 保存量化模型
    torch.save(quantized_model.state_dict(), "whisper_base_quantized.pt")
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/JbALU0EHoYVcp6nCN2TrM1xqjdQZ.png)

    class KVCache:
    def __init__(self, max_len=512):
        self.cache = {}
        self.max_len = max_len
    
    def update(self, layer_idx, key, value):
        if layer_idx not in self.cache:
            self.cache[layer_idx] = (key, value)
        else:
            # 拼接新KV并截断
            old_key, old_value = self.cache[layer_idx]
            new_key = torch.cat([old_key, key], dim=2)[:, :, -self.max_len:]
            new_value = torch.cat([old_value, value], dim=2)[:, :, -self.max_len:]
            self.cache[layer_idx] = (new_key, new_value)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/u0Jt3V9oe7CFxDaQKZYsG1Lwcvmd.png)

    # 创建conda环境
    conda create -n whisper-rt python=3.9
    conda activate whisper-rt
    
    # 安装依赖
    pip install torch==1.13.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
    pip install openai-whisper
    pip install sounddevice  # 实时音频采集
    
    
    bash

    import whisper
    import sounddevice as sd
    import numpy as np
    from queue import Queue
    from threading import Thread
    
    class RealTimeWhisper:
    def __init__(self, model_size="base.en"):
        self.model = whisper.load_model(model_size)
        self.audio_queue = Queue()
        self.sample_rate = 16000
        self.chunk_size = 2048  # 128ms chunks
    
    def audio_callback(self, indata, frames, time, status):
        """音频采集回调函数"""
        self.audio_queue.put(indata.copy())
    
    def process_audio(self):
        """处理音频线程"""
        audio_buffer = np.zeros((0,1))
    
        while True:
            # 收集足够音频
            while len(audio_buffer) < self.chunk_size:
                chunk = self.audio_queue.get()
                audio_buffer = np.concatenate((audio_buffer, chunk))
    
            # 取出处理块
            process_chunk = audio_buffer[:self.chunk_size]
            audio_buffer = audio_buffer[self.chunk_size:]
    
            # 转换为float32并归一化
            audio = process_chunk.astype(np.float32) / 32768.0
    
            # 语音识别
            result = self.model.transcribe(audio)
            print(f"识别结果: {result['text']}")
    
    def start(self):
        """启动实时识别"""
        # 启动处理线程
        process_thread = Thread(target=self.process_audio)
        process_thread.daemon = True
        process_thread.start()
    
        # 开始音频采集
        with sd.InputStream(callback=self.audio_callback,
                          channels=1,
                          samplerate=self.sample_rate,
                          blocksize=self.chunk_size):
            print("实时识别已启动，请开始说话...")
            while True:
                pass
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/hpL7R2DET6aPB89eQKjqfMG50ndk.png)