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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Utils/JDC/bst.t7 filter=lfs diff=lfs merge=lfs -text
+Utils/PLBERT/step_1000000.t7 filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- luo
+tags:
+- text-to-speech
+- luhya
+- lunyore
+- african-languages
+- styletts2
+license: mit
+---
+
+# Luhya (Lunyore) TTS
+
+Text-to-speech model for the **Luhya Lunyore** language, fine-tuned from
+[StyleTTS2](https://github.com/yl4579/StyleTTS2) on Luhya audio data.
+
+## About
+- Base: StyleTTS2 LibriTTS pretrained
+- Language: Luhya / Lunyore (Bantu, Western Kenya)
+- Training: 20 epochs on single-speaker Luhya audio
+- Output: 24kHz mono WAV

Utils/ASR/config.yml

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+log_dir: "logs/20201006"
+save_freq: 5
+device: "cuda"
+epochs: 180
+batch_size: 64
+pretrained_model: ""
+train_data: "ASRDataset/train_list.txt"
+val_data: "ASRDataset/val_list.txt"
+
+dataset_params:
+  data_augmentation: false
+
+preprocess_parasm:
+  sr: 24000
+  spect_params:
+    n_fft: 2048
+    win_length: 1200
+    hop_length: 300
+  mel_params:
+    n_mels: 80
+
+model_params:
+   input_dim: 80
+   hidden_dim: 256
+   n_token: 178
+   token_embedding_dim: 512
+
+optimizer_params:
+  lr: 0.0005

Utils/ASR/epoch_00080.pth

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+size 94552811

Utils/JDC/bst.t7

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Utils/PLBERT/config.yml

@@ -0,0 +1,30 @@
+log_dir: "Checkpoint"
+mixed_precision: "fp16"
+data_folder: "wikipedia_20220301.en.processed"
+batch_size: 192
+save_interval: 5000
+log_interval: 10
+num_process: 1 # number of GPUs
+num_steps: 1000000
+
+dataset_params:
+    tokenizer: "transfo-xl-wt103"
+    token_separator: " " # token used for phoneme separator (space)
+    token_mask: "M" # token used for phoneme mask (M)
+    word_separator: 3039 # token used for word separator (<formula>)
+    token_maps: "token_maps.pkl" # token map path
+    
+    max_mel_length: 512 # max phoneme length
+    
+    word_mask_prob: 0.15 # probability to mask the entire word
+    phoneme_mask_prob: 0.1 # probability to mask each phoneme
+    replace_prob: 0.2 # probablity to replace phonemes
+    
+model_params:
+    vocab_size: 178
+    hidden_size: 768
+    num_attention_heads: 12
+    intermediate_size: 2048
+    max_position_embeddings: 512
+    num_hidden_layers: 12
+    dropout: 0.1

Utils/PLBERT/step_1000000.t7

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0714ff85804db43e06b3b0ac5749bf90cf206257c6c5916e8a98c5933b4c21e0
+size 25185187

config.yml

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+log_dir: "Models/LJSpeech"
+save_freq: 5
+log_interval: 10
+device: "cuda"
+epochs: 50 # number of finetuning epoch (1 hour of data)
+batch_size: 8
+max_len: 400 # maximum number of frames
+pretrained_model: "Models/LibriTTS/epochs_2nd_00020.pth"
+second_stage_load_pretrained: true # set to true if the pre-trained model is for 2nd stage
+load_only_params: true # set to true if do not want to load epoch numbers and optimizer parameters
+
+F0_path: "Utils/JDC/bst.t7"
+ASR_config: "Utils/ASR/config.yml"
+ASR_path: "Utils/ASR/epoch_00080.pth"
+PLBERT_dir: 'Utils/PLBERT/'
+
+data_params:
+  train_data: "Data/train_list.txt"
+  val_data: "Data/val_list.txt"
+  root_path: "/local/LJSpeech-1.1/wavs"
+  OOD_data: "Data/OOD_texts.txt"
+  min_length: 50 # sample until texts with this size are obtained for OOD texts
+
+preprocess_params:
+  sr: 24000
+  spect_params:
+    n_fft: 2048
+    win_length: 1200
+    hop_length: 300
+
+model_params:
+  multispeaker: true
+
+  dim_in: 64 
+  hidden_dim: 512
+  max_conv_dim: 512
+  n_layer: 3
+  n_mels: 80
+
+  n_token: 178 # number of phoneme tokens
+  max_dur: 50 # maximum duration of a single phoneme
+  style_dim: 128 # style vector size
+  
+  dropout: 0.2
+
+  # config for decoder
+  decoder: 
+      type: 'hifigan' # either hifigan or istftnet
+      resblock_kernel_sizes: [3,7,11]
+      upsample_rates :  [10,5,3,2]
+      upsample_initial_channel: 512
+      resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
+      upsample_kernel_sizes: [20,10,6,4]
+      
+  # speech language model config
+  slm:
+      model: 'microsoft/wavlm-base-plus'
+      sr: 16000 # sampling rate of SLM
+      hidden: 768 # hidden size of SLM
+      nlayers: 13 # number of layers of SLM
+      initial_channel: 64 # initial channels of SLM discriminator head
+  
+  # style diffusion model config
+  diffusion:
+    embedding_mask_proba: 0.1
+    # transformer config
+    transformer:
+      num_layers: 3
+      num_heads: 8
+      head_features: 64
+      multiplier: 2
+
+    # diffusion distribution config
+    dist:
+      sigma_data: 0.2 # placeholder for estimate_sigma_data set to false
+      estimate_sigma_data: true # estimate sigma_data from the current batch if set to true
+      mean: -3.0
+      std: 1.0
+  
+loss_params:
+    lambda_mel: 5. # mel reconstruction loss
+    lambda_gen: 1. # generator loss
+    lambda_slm: 1. # slm feature matching loss
+    
+    lambda_mono: 1. # monotonic alignment loss (TMA)
+    lambda_s2s: 1. # sequence-to-sequence loss (TMA)
+
+    lambda_F0: 1. # F0 reconstruction loss
+    lambda_norm: 1. # norm reconstruction loss
+    lambda_dur: 1. # duration loss
+    lambda_ce: 20. # duration predictor probability output CE loss
+    lambda_sty: 1. # style reconstruction loss
+    lambda_diff: 1. # score matching loss
+    
+    diff_epoch: 10 # style diffusion starting epoch
+    joint_epoch: 30 # joint training starting epoch
+
+optimizer_params:
+  lr: 0.0001 # general learning rate
+  bert_lr: 0.00001 # learning rate for PLBERT
+  ft_lr: 0.0001 # learning rate for acoustic modules
+  
+slmadv_params:
+  min_len: 400 # minimum length of samples
+  max_len: 500 # maximum length of samples
+  batch_percentage: 0.5 # to prevent out of memory, only use half of the original batch size
+  iter: 10 # update the discriminator every this iterations of generator update
+  thresh: 5 # gradient norm above which the gradient is scaled
+  scale: 0.01 # gradient scaling factor for predictors from SLM discriminators
+  sig: 1.5 # sigma for differentiable duration modeling
+  

model.pth

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