Short collection of helpful scripts for dataset creation and/or TTS training stuff

MRS2LJSpeech

Python script which takes recordings (filesystem and sqlite db) done with Mycroft Mimic-Recording-Studio (https://github.com/MycroftAI/mimic-recording-studio) and creates an audio optimized dataset in widely supported LJSpeech directory structure.

Peter Schmalfeldt (https://github.com/manifestinteractive) did an amazing job as he optimized my originally (quick'n dirty) version of that script, so thank you Peter :-) See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba#file-mrs2ljspeech-py

Dockerfile.Jetson-Coqui

Add your user to docker group to not require sudo on all operations.

Thanks to NVIDIA for providing docker images for Jetson platform. I use the "machine learning (ML)" image as baseimage for setting up a Coqui environment.

You can use any branch or tag as COQUI_BRANCH argument. v0.1.3 is just the current stable version.

Switch to directory where Dockerfile is in and run nvidia-docker build . -f Dockerfile.Jetson-Coqui --build-arg COQUI_BRANCH=v0.1.3 -t jetson-coqui to build your container image. When build process is finished you can start a container on that image.

Mapped volumes

We need to bring your dataset and configuration file into our container so we should map a volume on running container nvidia-docker run -p 8888:8888 -d --shm-size 32g --gpus all -v [host path with dataset and config.json]:/coqui/TTS/data jetson-coqui. Now we have a running container ready for Coqui TTS magic.

Jupyter notebook

Coqui provides lots of useful Jupyter notebooks for dataset analysis. Once your container is up and running you should be able to call

Running bash into container

nvidia-docker exec -it jetson-coqui /bin/bash now you're inside the container and an ls /coqui/TTS/data should show your dataset files.

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