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managed_components/78__esp-opus/dnn/torch/fargan/README.md

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+# Framewise Auto-Regressive GAN (FARGAN)
+
+Implementation of FARGAN, a low-complexity neural vocoder. Pre-trained models
+are provided as C code in the dnn/ directory with the corresponding model in
+dnn/models/ directory (name starts with fargan_). If you don't want to train
+a new FARGAN model, you can skip straight to the Inference section.
+
+## Data preparation
+
+For data preparation you need to build Opus as detailed in the top-level README.
+You will need to use the --enable-deep-plc configure option.
+The build will produce an executable named "dump_data".
+To prepare the training data, run:
+```
+./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm
+```
+Where the in_speech.pcm speech file is a raw 16-bit PCM file sampled at 16 kHz.
+The speech data used for training the model can be found at:
+https://media.xiph.org/lpcnet/speech/tts_speech_negative_16k.sw
+
+## Training
+
+To perform pre-training, run the following command:
+```
+python ./train_fargan.py out_features.f32 out_speech.pcm output_dir --epochs 400 --batch-size 4096 --lr 0.002 --cuda-visible-devices 0
+```
+Once pre-training is complete, run adversarial training using:
+```
+python adv_train_fargan.py out_features.f32 out_speech.pcm output_dir --lr 0.000002 --reg-weight 5 --batch-size 160 --cuda-visible-devices 0 --initial-checkpoint output_dir/checkpoints/fargan_400.pth
+```
+The final model will be in output_dir/checkpoints/fargan_adv_50.pth.
+
+The model can optionally be converted to C using:
+```
+python dump_fargan_weights.py output_dir/checkpoints/fargan_adv_50.pth fargan_c_dir
+```
+which will create a fargan_data.c and a fargan_data.h file in the fargan_c_dir directory.
+Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus.
+
+## Inference
+
+To run the inference, start by generating the features from the audio using:
+```
+./fargan_demo -features test_speech.pcm test_features.f32
+```
+Synthesis can be achieved either using the PyTorch code or the C code.
+To synthesize from PyTorch, run:
+```
+python test_fargan.py output_dir/checkpoints/fargan_adv_50.pth test_features.f32 output_speech.pcm
+```
+To synthesize from the C code, run:
+```
+./fargan_demo -fargan-synthesis test_features.f32 output_speech.pcm
+```