mirror of https://github.com/coqui-ai/TTS.git
config update for r=2
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{
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"model_name": "embed_init_loss_weight",
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"model_description": "Lower loss freq is weight 0.0, higher weight decay, ref_level_db: 40->20, sample_rate:16000 -> 22050",
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"model_name": "queue",
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"model_description": "Queue memory and change lower r incrementatlly",
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"audio":{
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"audio_processor": "audio", // to use dictate different audio processors, if available.
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"embedding_size": 256, // Character embedding vector length. You don't need to change it in general.
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"text_cleaner": "phoneme_cleaners",
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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"loss_weight": 0.0, // loss weight to emphasize lower frequencies. Lower frequencies are in general more important for speech signals.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"memory_size": 5, // memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
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"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
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"eval_batch_size":32,
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"r": 5, // Number of frames to predict for step.
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"r": 2, // Number of frames to predict for step.
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"wd": 0.00001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 5000, // Number of training steps expected to save traning stats and checkpoints.
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@ -0,0 +1 @@
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print("Python is running!!")
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