We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. You can use them to create audiobooks, podcasts, announcements, and more with voice pitch controls and free online tool. Generate realistic voice-overs online for free or with a paid plan. Narakeet offers more than 700 AI voice generators in 90 languages, including deep voices with a naturally low pitch. You can use it for work, videos, ads, social media, e-learning, and more. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. SpeechGen.io lets you convert text to speech with AI voices in American English and other languages. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. The notebook is supposed to be executed on Google colab so you dont have to setup your machines locally. In this notebook, you can try DeepVoice3-based single-speaker text-to-speech (en) using a model trained on LJSpeech dataset. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. DeepVoice3: Single-speaker text-to-speech demo. Next, let’s create a function to collect all the frames that contain voice. Collecting Voice Activated Frames for Speech to Text with DeepSpeech. Download a PDF of the paper titled Deep Voice 2: Multi-Speaker Neural Text-to-Speech, by Sercan Arik and 7 other authors Download PDF Abstract:We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. It is based on Baidu’s 2014 paper titled Deep Speech: Scaling up end-to-end speech recognition.
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