Model Implementations for Inference（MII）是一个开源的存储库，通过减轻应用复杂系统优化技术的需求，使所有数据科学家都可以访问低延迟和高吞吐量的推理。开箱即用，MII为数千种广泛使用的DL模型提供支持，使用DeepSpeed-Inference进行优化，可以使用几行代码进行部署，同时与其香草开源版本相比，实现了显着的延迟减少。
图1：所提出的CosyVoice模型的概述。(a)演示了 𝒮3 标记器，其中虚线模块仅在训练阶段使用。(b)是CosyVoice的示意图，由文本到令牌LLM和令牌到语音流匹配模型组成。 S、E和T表示“start of sequence”、“end of sequence”和“turn of speech”标记。虚线表示推理阶段的自回归解码。(c)提供了我们的流匹配模型的放大视图，该模型以概率密度路径上的时间步长 t处的说话者嵌入 𝐯、语义标记 μ、掩蔽语音特征 X~和中间状态 Xt为条件。图2：（a）零触发上下文学习和（B）跨语言语音克隆的序列构建。LID表示目标语言标识符。
S and E denote the start and end of sequence, respectively.T is “turn of speech” tokens. 𝐯 is a speaker embedding vector extracted from the speech X with a pre-trained voice-print model2. The text encodings Y¯={𝐲¯u}u∈[1:U] is obtained by passing the text through a Byte Pair Encoded (BPE) tokenizer and text encoder:
System:
You are a translator proficient in English and {language}. Your task is to translate the following English text into {language}, focusing on a natural and fluent result that avoids “translationese.” Please consider these points:
1. Keep proper nouns, brands, and geographical names in English.
2. Retain technical terms or jargon in English, but feel free to explain in {language} if necessary.
3. Use {language} idiomatic expressions for English idioms or proverbs to ensure cultural relevance.
4. Ensure quotes or direct speech sound natural in {language}, maintaining the original’s tone.
5. For acronyms, provide the full form in {language} with the English acronym in parentheses.
User:
Text for translation: {text}
Assistant:
{translation results}
24年6月，LeCun&谢赛宁团队发布Cambrian-1，关注以视觉为中心的多模态LLM，开源了8B/13B/34B的模型。当前多模态LLM仍存在较大的视觉缺陷，需要增强视觉表征以更好地和语言模态交互，赋予模型在真实场景更强的感知定位能力。这项研究的一大意义在于影响多模态LLM的工作开始重视视觉表征质量的提升，而非一直scale up LLM。
在语音识别中，我们的数据集是音频文件和其对应的文本，不幸的是，音频文件和文本很难在单词的单位上对齐。除了语言识别，在OCR，机器翻译中，都存在类似的Sequence to Sequence结构，同样也需要在预处理操作时进行对齐，但是这种对齐有时候是非常困难的。如果不使用对齐而直接训练模型时，由于人的语速的不同，或者字符间距离的不同，导致模型很难收敛。
当我们主要关注文本和语音模态时，GPT-4o其实就是一个语音语言模型(speech language model, SLM)。该SLM同时具备语音理解能力和语音合成能力，输入端和输出端均支持文本和语音的混合多模态。那么，这一SLM应该如何实现呢？在大语言模型(large language model, LLM)滥觞的今日，不难想到这样一种方法：将连续的语音数据离散化成如同单词(或者称token，词元)一样的表示，并入到LLM的词表中，再走一遍训练LLM的老路。
audio & text tokenizer的实现应该是语音离散化部分所用的技术，例如SoundStream、Encodec、SpeechTokenizer，或者是MEL+VQ最后配合声码器来解码；参考zero-shot TTS、AudioLM/AudioPaLM、SpeechGPT-Gen等工作的结果，LLM中语音token的解码应该是要走层次化或者多步的方法，先解码语义特征，再解码声学特征，或者是先解码MEL，再加一个HIFIGAN这样的声码器。另外，如果做audio/speech/music这样的通用声合成的话，可能也能通过prompt来控制。AudioLDM2虽然做了这方面的工作，但audio/music和speech的参数其实是不一样的，说到底还不是同一个模型。
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Overview of our approach. A sequence-to-sequence Transformer model is trained on many different speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection
目前主流的语音识别方法是先进行大规模的无监督预训练（Wav2Vec 2.0），比如， Wav2Vec 采集了1000000h的无标签训练数据，先用这些数据进行预训练一个编码器（使用对比学习 or 字训练），encoder能够对语音数据做一个很好的编码，然后在面向下游任务时，可以在标准训练集中做微调（只需要几十小时的数据就可），这样比只在标准数据集上训练的结果好很多。
这些预训练好的语音编码器能够学习到语音的一个高质量表示，但是用无监督方法训练的编码器仍然需要训练一个解码器，需要用带标签的数据来微调，微调是一个很复杂的过程，如果不需要微调就好了，这也是本文要做的工作。此外，过去的工作缺乏一个很好的解码器，这是一个巨大的缺陷，而语音识别系统就是应该是“out of box”，也就是拿来即用。
数据部分是本文最核心的贡献。由于数据够多，模型够强，本文模型直接预测原始文本，而不经过任何标准化（standardization）。从而模型的输出就是最终识别结果，而无需经过反向的文本归一化（inverse text normalization）后处理。所谓文本归一化包括如将所有单词变小写，所有简写展开，所有标点去掉等操作，而反向文本归一化就是上述操作的反过程。在 Whisper 中，这些操作统统不用，因为数据足够多，可以覆盖所有的情况。
