forked from extern/Thorsten-Voice
27 lines
1.9 KiB
Markdown
27 lines
1.9 KiB
Markdown
# Short collection of helpful scripts for dataset creation and/or TTS training stuff
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## MRS2LJSpeech
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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.
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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 :-)
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See more details here: https://gist.github.com/manifestinteractive/6fd9be62d0ede934d4e1171e5e751aba#file-mrs2ljspeech-py
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## Dockerfile.Jetson-Coqui
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> Add your user to `docker` group to not require sudo on all operations.
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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.
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> You can use any branch or tag as COQUI_BRANCH argument. v0.1.3 is just the current stable version.
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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.
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### Mapped volumes
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We need to bring your dataset and configuration file into our container so we should map a volume on running container
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`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.
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### Jupyter notebook
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Coqui provides lots of useful Jupyter notebooks for dataset analysis. Once your container is up and running you should be able to call
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### Running bash into container
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`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. |