背景真是日新月异了,前有谷歌刚不久搞出来了USM, 迷途小书僮:[论文尝鲜]谷歌的USM-一次搞定100种语言的语音识别也就是能覆盖100个语言,这不,meta直接搞出来面向1000个语言的asr模型。。。 简直是,太刺激了:…
More specifically, we use Model Agnostic Meta-Learning (MAML) as the training algorithm of a multi-speaker TTS model, which aims to find a great meta-initialization to adapt the model to any few-shot speaker adaptation tasks quickly. Therefore, we can also adapt the meta-trained TTS model ...
Meta-TTS Requirements This is how I build my environment, which is not exactly needed to be the same: Sign up forComet.ml, find out your workspace and API key viawww.comet.ml/api/my/settingsand fill them inconfig/comet.py. Comet logger is used throughout train/val/test stages. ...
MMS在语音合成（TTS）任务上的效果 MetaAI也在语音合成任务上做了比较 从TTS和人类话语之间的CER的微小差异可以看出，MMS系统保留了大部分原始内容。MOS分数也表明，与人类话语相比，MMS的系统声音质量较低，但在领域内数据上的差异并不是很大。不幸的是，正如前面提到的，由于FLEURS音频中的嘈杂语音，领域外的MOS分数...
KAIST(韩国科学技术院,近年势头很猛、做的工作也都挺有影响力)发表的Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation (ICML 2021)核心:提出一种风格自适应TTS模型StyleSpeech,并基于元学习策略扩展到Meta-StyleSpeech。论文地址:arxiv.org/abs/2106.0315demo网址:stylespeech.github.io/ ...
开源TTS质量的可变性突显了Meta在匹配AudioPaLM等模型的细微功能方面面临的挑战。随着人工智能社区继续寻求高质量、开源、多峰值语言模型,AudioPaLM的性能树立了一个高基准,推动了对能够提供类似自然和复杂通信体验的模型的追求。 . -像Blip-2、Fromage、Prismer和PALM-E这样的多峰值模型通常从文本LLM开始,然后在视觉...
MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities: Emotional speech rhythm and tone in English. Zero-shot cloning for American & British voices, with 30s reference audio. Support for (cros...
🛠️本期视频提到的工具:【Gemini更新】https://blog.google/technology/google-deepmind/gemini-model-updates-february-2025/【Hika搜索引擎】https://hika.fyi/【PlayHT模型】https://play.ai/【VideoJam】https://hila-chefer.github.io/vi, 视频播放量 496、弹幕量
【天娱数科:革新AIGC智能内容创作工具 元境科技元趣V1.0新版本将发布】 据天娱数科消息,天娱数科子公司元境科技自研的“MetaSurfing-元享智能云平台”接入预训练大模型,在原有基础上升级AIGC功能模块,孵化出了以AIGC技术为核心的“元趣”。“元趣”是一款创新性的应用,为用户提供一站式的内容解决方案,实现文生...
speaker adaptation baseline and outperforms the speaker encoding baseline under the same training scheme. When the speaker encoder of the baseline is pre-trained with extra 8371 speakers of data, Meta-TTS can still outperform the baseline on LibriTTS dataset and achieve comparable results on VCTK ...