Neural codec language model (LM) has demonstrated strong capability in
zero-shot text-to-speech (TTS) synthesis. However, the codec LM often suffers
from limitations in inference speed and stability, due to its auto-regressive
nature and implicit alignment between text and audio. In this work, to handle
these challenges, we introduce a new variant of neural codec LM, namely TacoLM.
Specifically, TacoLM introduces a gated attention mechanism to improve the
training and inference efficiency and reduce the model size. Meanwhile, an
additional gated cross-attention layer is included for each decoder layer,
which improves the efficiency and content accuracy of the synthesized speech.
In the evaluation of the Librispeech corpus, the proposed TacoLM achieves a
better word error rate, speaker similarity, and mean opinion score, with 90%
fewer parameters and 5.2 times speed up, compared with VALL-E. Demo and code is
available at this https URL

通过引入 TacoLM，一种新型神经编解码语言模型，改进了训练和推理效率，降低模型规模，并通过在每个解码器层引入额外的门控交叉注意力层，提高合成语音的效率和内容准确性。在 Librispeech 资料库的评估中，TacoLM 比 VALL-E 在单词错误率、说话人相似度和平均意见分数方面都取得更好的结果，同时参数减少 90%，提速 5.2 倍。

TacoLM：具有门控注意力的编解码语言模型是高效的零 - shot 文本转语音合成器

TacoLM: GaTed Attention Equipped Codec Language Model are Efficient  Zero-Shot Text to Speech Synthesizers

Temporal common sense (e.g., duration and frequency of events) is crucial for
understanding natural language. However, its acquisition is challenging, partly
because such information is often not expressed explicitly in text, and human
annotation on such concepts is costly. This work proposes a novel sequence
modeling approach that exploits explicit and implicit mentions of temporal
common sense, extracted from a large corpus, to build TACOLM, a temporal common
sense language model. Our method is shown to give quality predictions of
various dimensions of temporal common sense (on UDST and a newly collected
dataset from RealNews). It also produces representations of events for relevant
tasks such as duration comparison, parent-child relations, event coreference
and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the
standard BERT. Thus, it will be an important component of temporal NLP.

本论文提出了一种基于序列建模的方法，利用大型语料库中显式和隐含的时间常识，构建了一个新颖的时间常识语言模型 TACOLM。该模型可以有效预测各种维度的时间常识，并且在时态 NLP 相关任务中产生了比标准 BERT 更好的事件表示。