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You can visualize your data set after preprocessing the data. Below is a good example of a model that output a good alignment for a sample.

To have a good alignment, the alignment chart should generally be linear. You may modify to your use case.Įval.py will also output two things, the wavfile, and alignment chart. If you set the -hparams flag when training, set the same value here.Įval.py will output sampels define in the sentence list found here. Python3 eval.py -checkpoint ~/tacotron/logs-tacotron/model.ckpt-185000 This code has been tested on tensorflow 1.8. Performance, install with GPU support if it's available.

Install the latest version of TensorFlow for your platform. Gpu: nvidia-docker run -it -p 3000:3000 mycroft/mimic2:gpuĬpu: docker run -it -p 3000:3000 mycroft/mimic2:cpu Ĭpu: docker build -t mycroft/mimic2:cpu -f cpu.Dockerfile. Gpu: docker build -t mycroft/mimic2:gpu -f gpu.Dockerfile.

If you want to use the GPU in docker make sure you have nvidia-docker installed The Dockerfile comes with a GPU option or CPU option. Quick Start Installing dependencies using docker (recommended) For real time conversations, join our mattermost chat and enter the machinelearning channel. ContributionsĬontributions are accepted! We'd love the communities help in building a better speech synthesis engine weather it be code, or, update on the README, bug reports, etc. Sample were generated using non training data. The data had 16 hours of training data recorded by a single english speaker. Here you can find audio samples on a model trained using this repo. The quality isn't as good as Google's demo yet, but hopefully, it will get there someday :-). This is anĪttempt to provide an open-source implementation of the model described in their paper. However, they didn't release their source code or training data. Where they present a neural text-to-speech model that learns to synthesize speech directly from Google published a paper, Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model, This repo is actively developed on by the Mycroft AI team and community.