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Fine-tuning and Inferencing the Flowtron Model

Flowtron is an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer.\nFlowtron borrows insights from Autoregressive Flows and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent).\nOur mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training.\nIn quick start guide you can see the steps of fine-tuning and inferencing this model.

Pre-requisites:

\n
\n
Nvidia GPU
\n
\n
\n
\n
Cuda and cuDNN
\n
\n

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You can find a dataset from this address :\n\n\"https://keithito.com/LJ-Speech-Dataset/ \"\n\nEdit the train and validation metadata file in the filelists based on the path to the dataset. Besides, you need to change the name of train and validation metadata text file in config.json file in data-config section.\n\n**Download Model Checkpoints:**\n\nFind flowtron checkpoints in ngc/models/Flowtron at this address: \" https://ngc.nvidia.com/catalog/models/nvidia:adlr:flowtron \". Download them , make a folder inside the Flowtron folder and call it models then put these checkpoints in this folder.\nFind the WaveGlow checkpoints in ngc/models/WaveGlow at this address: \" https://ngc.nvidia.com/catalog/models/nvidia:adlr:waveglow \". Download and put it in the models folder inside the Flowtron folder.\n\n**Build the Container:**\n\nThe next step is building a container using the Dockerfile inside flowtron directory. This will make sure that we have access to all necessary resources for running the model in this container environment. Besides, it installs all models requirements.\n```\nDocker build -t flowtron .\n```\n**Run the Container:**\n\nNow we have a container with a dataset and all requirements inside it. Run the container using following command:\n```\ndocker run -it --runtime=nvidia -e NVIDIA_DRIVER_CAPABILITIES=compute,utility -e NVIDIA_VISIBLE_DEVICES=all flowtron\n```\n\n**Fine-tuning:**\n\nYou can fine tune the model using the downloaded dataset by this command:\n```\npython train.py -c config.json -p train_config.finetune_layers=[\"speaker_embedding.weight\"] train_config.checkpoint_path=\"/workspace/flowtron/models/flowtron_libritts2p3k.pt\"\n```\n\nAfter running the finetune command you can see the saved checkpoints of the mode in a folder called outdir.\n\n**Inference the Model:**\nUse this command to inference the model using weights of fine tuned model:\n```\npython inference.py -c config.json -f /workspace/flowtron/models/model_path\n-w /workspace/flowtron/models/waveglow_256channels_universal_v5.pt -t \"It is well known that deep generative models have a rich latent space!\" -i 0 -n 1000\n```\nAfter running inference you can see the result audio in the results folder. You can change the text in this command to any text that you want to creat.\n\n**Inference Examples:**\nFollowing is some examples of inference the flowtron model using different flowtron checkpoints from https://ngc.nvidia.com/catalog/models/nvidia:adlr:flowtron .\n\n1. LJSpeech \n```\npython inference.py -c config_Flowtron-LJSpeech.json -f models/flowtron_ljs.pt -w models/waveglow_256channels_universal_v5.pt -t \"Speech test\" -i 0 -o results/Flowtron-LJSpeech\n```\n2. 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