前言

Whisper 是由 OpenAI 开发的一种先进语音识别系统。它采用深度学习技术，能够高效、准确地将语音转换为文本。Whisper 支持多种语言和口音，并且在处理背景噪音和语音变异方面表现出色。其广泛应用于语音助手、翻译服务、字幕生成等领域，为用户提供了更流畅的语音交互体验。作为一个开源项目，Whisper 鼓励开发者和研究人员进一步优化和创新。

作者将解码过程整理成 简单的python代码进行讲解

核心思想

whisper解码核心是 基于自回归解码的token游戏 ，换句话说他的参数读取是通过传入token id的形式，即采用大语言模型的prompt范式（whisper的解码器一定程度上也是个大语言模型，虽然语音训练样本token数远不及纯文本token数）

图中除了识别结果的框框大多数都是prompt工程, 常用的token id 如图：

自回归解码

详细解释放在代码中啦

def main():
    
    """
        解码器须构建Deocder的prompt，序列为【SOT，语种，任务】, 本文中是 model.sot_sequence
        其中SOT：50258
        语种：50332,50309,50333,50335,50273，...
        任务：transcribe 转写 50359， translate 翻译 50358
    """


    """
                加载whisper模型
    """
    encoder_onnx_file = './small-encoder.int8.onnx'
    decoder_onnx_file = './small-decoder.int8.onnx'
    tokenizer_file = './small-tokens.txt'
    model = OnnxModel(encoder_onnx_file, decoder_onnx_file)
    token_table = load_tokenizer(tokenizer_file) # token id to char 


    """
                提取MEL特征
    """
    wav_file = "output.wav"
    mel = compute_features(wav_file)


    """
                计算encoder的K/V编码 
    """
    # 交叉注意力 encoder:K/V, with decoder:Q
    n_layer_cross_k, n_layer_cross_v = model.run_encoder(mel)
    # 自注意力 decoder:K/V, with decoder:Q
    n_layer_self_k_cache, n_layer_self_v_cache = model.get_self_cache()


    """
                检测语种
    """
    lang = model.detect_language(n_layer_cross_k, n_layer_cross_v)
    model.sot_sequence[1] = lang


    """
                任务选择
    """
    # task = model.translate
    task = model.transcribe
    model.sot_sequence[2] = task
    
    
    """
                根据prompt进行首次解码
    """
    tokens = torch.tensor([model.sot_sequence], dtype=torch.int64)
    offset = torch.zeros(1, dtype=torch.int64)
    logits, n_layer_self_k_cache, n_layer_self_v_cache = model.run_decoder(
        tokens=tokens,
        n_layer_self_k_cache=n_layer_self_k_cache,
        n_layer_self_v_cache=n_layer_self_v_cache,
        n_layer_cross_k=n_layer_cross_k,
        n_layer_cross_v=n_layer_cross_v,
        offset=offset,
    )
    offset += len(model.sot_sequence)
    logits = logits[0, -1] # token 声学后验
    model.suppress_tokens(logits, is_initial=True) # 无效token后验抑制



    """
                自回归解码
    """
    max_token_id = logits.argmax(dim=-1) # 选择后验中最大输出的token【贪心解码】
    results = []
    sentence = {'start':0,'end':0,'text':b""} 
    sentences = []
    for i in range(model.n_text_ctx):

        # 打印token属性
        if max_token_id.item() == model.sot:
            print("iter:%8s docode token id:%8s [sot]"%(i,max_token_id.item()))
        elif max_token_id.item() == model.eot:
            print("iter:%8s docode token id:%8s [eot]"%(i,max_token_id.item()))
        elif max_token_id.item() >= model.timestamp_begin:
            print("iter:%8s docode token id:%8s [boundary]"%(i,max_token_id.item()))
        else:
            print("iter:%8s docode token id:%8s [char]"%(i,max_token_id.item()))
        
        # eot 结束
        if max_token_id.item() == model.eot:
            print("Finish !!")
            break

        # 检测到时间戳
        if max_token_id.item()>=model.timestamp_begin:
            timestamp = ((max_token_id.item()-model.timestamp_begin)*model.time_precision)
            # 遇到结束符
            if sentence['text']:
                sentence['end'] = timestamp
                sentence['text'] = sentence['text'].decode().strip()
                print(sentence)
                sentences.append(sentence)
                sentence = {'start':0,'end':0,'text':b""}
            # 遇到开始符
            else:
                sentence['start'] = timestamp
        else:
            decode_token = base64.b64decode(token_table[max_token_id.item()])
            sentence['text'] += decode_token


        results.append(max_token_id.item())
        tokens = torch.tensor([[results[-1]]])
        # deocder 单步解码
        logits, n_layer_self_k_cache, n_layer_self_v_cache = model.run_decoder(
            tokens=tokens,
            n_layer_self_k_cache=n_layer_self_k_cache,
            n_layer_self_v_cache=n_layer_self_v_cache,
            n_layer_cross_k=n_layer_cross_k,
            n_layer_cross_v=n_layer_cross_v,
            offset=offset,
        )
        offset += 1
        logits = logits[0, -1]
        model.suppress_tokens(logits, is_initial=False)
        max_token_id = logits.argmax(dim=-1) # 贪心搜索

没错连时间戳也是token形式~，下面是运行结果感受一下。我们在边界处对句子进行保存

以上就是whisper解码的基本原理，感兴趣的同学关注走一波