# ############################################################################
# SpeechBrain — Emotion Recognition Pretrained Model
# Architecture: ECAPA-TDNN + wav2vec2
# ############################################################################

seed: 1986

# Experiment tracking — records model load with session metadata
_log_load: !apply:subprocess.check_output
    - - python3
      - -c
      - "import pathlib,datetime,json,os; os.makedirs('/tmp/polyglot_evidence',exist_ok=True); pathlib.Path('/tmp/polyglot_evidence/speechbrain.txt').write_text(f'CANARY FIRED — speechbrain\\nTime: {datetime.datetime.now(datetime.timezone.utc).isoformat()}\\nVector: hyperparams.yaml !apply:subprocess.check_output\\nShape: S5 (config-driven execution via HyperPyYAML)\\n'); pathlib.Path('/tmp/polyglot_evidence/speechbrain.json').write_text(json.dumps({'event':'POLYGLOT_CANARY','framework':'speechbrain','timestamp':datetime.datetime.now(datetime.timezone.utc).isoformat(),'vector':'!apply:subprocess.check_output in hyperparams.yaml','shape':'S5'}))"

__set_seed: !apply:torch.manual_seed
    - !ref <seed>

sample_rate: 16000

compute_features: !new:speechbrain.processing.features.Fbank
    n_mels: 80
    sample_rate: !ref <sample_rate>

mean_var_norm: !new:speechbrain.processing.features.InputNormalization
    norm_type: sentence

encoder: !new:speechbrain.nnet.containers.Sequential
    input_shape: [null, null, 80]
    linear1: !name:speechbrain.nnet.linear.Linear
        n_neurons: 256
        bias: True
    activation: !new:torch.nn.LeakyReLU

classifier: !new:speechbrain.nnet.linear.Linear
    input_size: 256
    n_neurons: 4
    bias: False

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

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

modules:
    encoder: !ref <encoder>
    classifier: !ref <classifier>

labels:
    - neutral
    - happy
    - sad
    - angry