githubczy/VoiceReviewModule

语音审核模块设计 语音审核模块采用Wav2Vec2模型对语音信号进行分析和分类。 基于Wav2Vec2的语音识别模型 在语音内容处理模块中，我们选用了Wav2Vec2作为语音识别的基础模型。Wav2Vec2之所以备受青睐，主要得益于其通过自监督学习技术，从海量未标注语音数据中捕获丰富的时间和频谱特征的能力。基于这一强大的预训练模型，我们在其顶部添加了全连接层和Softmax分类器，用以实现具体的语音分类任务。 首先，在微调过程中，为了适应我们的具体应用场景，我们调整了部分模型参数，例如优化器、学习率和正则化参数。这不仅提高了模型的收敛速度，还进一步提升了其在目标语音任务中的表现。 此外，为了增强模型的鲁棒性，我们特别引入了噪声数据进行数据增强。具体而言，我们在训练样本中添加了背景噪声、信号干扰和音频剪切等常见噪声场景，模拟实际环境中的复杂条件。这一策略有效地提升了模型在嘈杂环境下的语音识别能力，并使其更加适用于真实应用场景。 语音特征分析与分类 使用Wav2Vec2提取语音的声学特征，生成特征向量。将特征向量输入分类器，判断语音内容是否违规。

Voice audit module design The voice audit module uses the Wav2Vec2 model to analyze and classify speech signals. Speech recognition model based on Wav2Vec2 In the speech content processing module, we chose Wav2Vec2 as the basic model for speech recognition. Wav2Vec2 is favored by its ability to capture rich temporal and spectral features from massive amounts of unlabeled speech data through self-supervised learning. Based on this powerful pre-trained model, we add a fully connected layer and a Softmax classifier on top of it to implement specific speech classification tasks. First, during the fine-tuning process, we adjusted some model parameters, such as optimizer, learning rate, and regularization parameters, to suit our specific application scenarios. This not only improves the convergence speed of the model, but also further improves its performance in the target speech task. In addition, in order to enhance the robustness of the model, we specially introduce noise data for data augmentation. Specifically, we added common noise scenarios such as background noise, signal interference, and audio clipping to the training samples to simulate complex conditions in the real-world environment. This strategy effectively improves the speech recognition ability of the model in noisy environments and makes it more suitable for real-world application scenarios. Speech feature analysis and classification Wav2Vec2 is used to extract the acoustic features of speech and generate feature vectors. The feature vector is input into the classifier to determine whether the speech content violates the rules.