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# ################################
# ProTeVa Inference Configuration
# Simplified YAML for deployment
# ################################
# Basic settings
seed: 200
device: cpu # Change to cuda if GPU available
sample_rate: 16000
# Output neurons (4 classes: blank, high, low, mid)
# Based on labelencoder.txt: 0=blank, 1=H, 2=B, 3=M
# Space (4) is added via post-processing
output_neurons: 4
blank_index: 0
# Number of prototypes
n_prototypes: 10
# Feature dimension from HuBERT
emb_dim: 768
# Encoder settings
rnn_layers: 2
rnn_neurons: 512
# Decoder settings
dnn_blocks: 2
dnn_neurons: 512
# Pitch decoder settings
dec_dnn_blocks: [1]
dec_dnn_neurons: [128]
# Activation function
activation: !name:torch.nn.LeakyReLU
# ============ MODULES ============
# HuBERT feature extractor
wav2vec2: !new:speechbrain.lobes.models.huggingface_transformers.hubert.HuBERT
source: "Orange/SSA-HuBERT-base-60k"
output_norm: True
freeze: False
save_path: whubert_checkpoint
# F0 extractor (requires custom module)
f0Compute: !new:modules.F0Extractor
device: !ref <device>
sample_rate: !ref <sample_rate>
# BiGRU Encoder
enc: !new:speechbrain.nnet.RNN.GRU
input_shape: [null, null, !ref <emb_dim>]
hidden_size: !ref <rnn_neurons>
num_layers: !ref <rnn_layers>
bidirectional: True
dropout: 0.15
# VanillaNN Decoder
dec: !new:speechbrain.lobes.models.VanillaNN.VanillaNN
input_shape: [null, null, 1024] # 512 * 2 (bidirectional)
activation: !ref <activation>
dnn_blocks: !ref <dnn_blocks>
dnn_neurons: !ref <dnn_neurons>
# Pitch Decoder (requires custom module)
pitch_dec: !new:modules.PitchDecoderLayer
input_shape: [null, null, !ref <dnn_neurons>]
dnn_blocks: !ref <dec_dnn_blocks>
dnn_neurons: !ref <dec_dnn_neurons>
# Prototype Layer (requires custom module)
proto: !new:modules.PrototypeLayer
n_prototypes: !ref <n_prototypes>
latent_dims: !ref <dnn_neurons>
# Output linear layer
output_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <n_prototypes>
n_neurons: !ref <output_neurons>
bias: True
# Log softmax
log_softmax: !new:speechbrain.nnet.activations.Softmax
apply_log: True
# Label encoder
label_encoder: !new:speechbrain.dataio.encoder.CTCTextEncoder
# ============ MODULES DICT ============
modules:
wav2vec2: !ref <wav2vec2>
enc: !ref <enc>
dec: !ref <dec>
pitch_dec: !ref <pitch_dec>
proto: !ref <proto>
output_lin: !ref <output_lin>
# Model container for all modules
model: !new:torch.nn.ModuleList
- [!ref <enc>, !ref <dec>, !ref <proto>, !ref <output_lin>, !ref <pitch_dec>]
# ============ PRETRAINER ============
# This loads the trained checkpoints
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
model: !ref <model>
wav2vec2: !ref <wav2vec2>
tokenizer: !ref <label_encoder>
paths:
model: !ref <save_folder>/model.ckpt
wav2vec2: !ref <save_folder>/wav2vec2.ckpt
tokenizer: !ref <save_folder>/tokenizer.ckpt
# Save folder - Path is loaded from config.py
# To change checkpoint folder, update CHECKPOINT_FOLDER in config.py
save_folder: ./CKPT+2025-10-20+08-19-07+00
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