hyperparams.yaml

# ############################################################################
# Model: WavLM for Emotion Diarization
# ############################################################################


# Hparams NEEDED
HPARAMS_NEEDED: ["window_length", "stride", "encoder_dim", "out_n_neurons", "avg_pool", "label_encoder", "softmax"]
# Modules Needed
MODULES_NEEDED: ["wav2vec2", "output_mlp"]

# Feature parameters
wav2vec2_hub: "microsoft/wavlm-large"

# Pretrain folder (HuggingFace)
pretrained_path: speechbrain/emotion-diarization-wavlm-large

# parameters
window_length: 1 # win_len = 0.02 * 1 = 0.02s
stride: 1 # stride = 0.02 * 1 = 0.02s
encoder_dim: 1024
out_n_neurons: 4

input_norm: !new:speechbrain.processing.features.InputNormalization
    norm_type: sentence
    std_norm: False

wav2vec2: !new:speechbrain.lobes.models.huggingface_transformers.wav2vec2.Wav2Vec2
    source: !ref <wav2vec2_hub>
    output_norm: True
    freeze: False
    freeze_feature_extractor: True
    save_path: wav2vec2_checkpoint

avg_pool: !new:speechbrain.nnet.pooling.Pooling1d
    pool_type: "avg"
    kernel_size: !ref <window_length>
    stride: !ref <stride>
    ceil_mode: True

output_mlp: !new:speechbrain.nnet.linear.Linear
    input_size: !ref <encoder_dim>
    n_neurons: !ref <out_n_neurons>
    bias: False

model: !new:torch.nn.ModuleList
    - [!ref <output_mlp>]

modules:
    input_norm: !ref <input_norm>
    wav2vec2: !ref <wav2vec2>
    output_mlp: !ref <output_mlp>

log_softmax: !new:speechbrain.nnet.activations.Softmax
    apply_log: True

label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder

pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
    loadables:
        input_norm: !ref <input_norm>
        wav2vec2: !ref <wav2vec2>
        model: !ref <model>
        label_encoder: !ref <label_encoder>
    paths:
        input_norm: !ref <pretrained_path>/input_norm.ckpt
        wav2vec2: !ref <pretrained_path>/wav2vec2.ckpt
        model: !ref <pretrained_path>/model.ckpt
        label_encoder: !ref <pretrained_path>/label_encoder.txt