We propose TalkNet, a non-autoregressive convolutional neural model for speech synthesis with explicit pitch and duration prediction. The model consists of three feed-forward convolutional networks. The first network predicts grapheme durations. An input text is expanded by repeating each symbol according to the predicted duration. The second network predicts pitch value for every mel frame. The third network generates a mel-spectrogram from the expanded text conditioned on predicted pitch. All networks are based on 1D depth-wise separable convolutional architecture. The explicit duration prediction eliminates word skipping and repeating. The quality of the generated speech nearly matches the best auto-regressive models - TalkNet trained on the LJSpeech dataset got MOS 4.08. The model has only 13.2M parameters, almost 2x less than the present state-of-the-art text-to-speech models. The non-autoregressive architecture allows for fast training and inference. The small model size and fast inference make the TalkNet an attractive candidate for embedded speech synthesis.
翻译:我们提议TalkNet, 这是一种非航空进化神经变异模型, 用于语音合成, 并配有清晰的音频和持续时间预测。 模型由三个进化前进进进化网络组成。 第一个网络预测了图形化持续时间。 一个输入文本根据预测的持续时间通过重复每个符号而扩大。 第二个网络预测每个模子框架的音值。 第三网络根据预测音频的扩大文本生成一个中位光谱。 所有网络都基于 1D 深度的相分离共变结构。 明确的持续时间预测消除了跳过和重复的单词。 生成的语音质量几乎与最佳自动递进化模型( LJSpeech 数据集培训的TalkNet) 的质量几乎匹配了 MOS 4. 08 。 这个模型只有13.2M 参数, 几乎比目前最先进的文本到语音模型的模型少2x。 非向导结构允许快速的培训和推断。 小型模型和快速推导使语音网成为了嵌式语音合成的有吸引力的候选者。