Implementation:Speechbrain Speechbrain Train IEMOCAP

Knowledge Sources	SpeechBrain
Domains	ASR, Training
Last Updated	2026-02-09 00:00 GMT

Overview

Concrete tool for training an emotion recognition system on the IEMOCAP dataset provided by the SpeechBrain library.

Description

This recipe defines the EmoIdBrain class (subclass of sb.Brain) for speech emotion recognition classifying four emotions (anger, happiness, sadness, neutrality). The pipeline uses an ECAPA-TDNN embedding model: features are extracted and normalized, then passed through the embedding model to produce utterance-level representations, which are classified into emotion categories. NLL loss is used for training. The learning rate annealing is applied on a per-batch basis during training.

Usage

Use this recipe to train an emotion recognition model using the ECAPA-TDNN architecture on the IEMOCAP dataset. Requires the corresponding hyperparameter YAML file and data preparation script.

Code Reference

Source Location

• Repository: SpeechBrain
• File: recipes/IEMOCAP/emotion_recognition/train.py

Signature

class EmoIdBrain(sb.Brain):
    def compute_forward(self, batch, stage):
        """Computation pipeline based on a encoder + emotion classifier."""
        ...
    def compute_objectives(self, predictions, batch, stage):
        """Computes the loss using speaker-id as label."""
        ...

Import

# Run as recipe script
python recipes/IEMOCAP/emotion_recognition/train.py hparams/train.yaml --data_folder /path/to/IEMOCAP

I/O Contract

Inputs

Name	Type	Required	Description
batch.sig	torch.Tensor	Yes	Input waveform signal
batch.emo_encoded	torch.Tensor	Yes	Encoded emotion label

Outputs

Name	Type	Description
outputs	torch.Tensor	Class posterior probabilities over 4 emotion categories

Usage Examples

python train.py hparams/train.yaml --data_folder /path/to/IEMOCAP

Related Pages

• Principle:Speechbrain_Speechbrain_Sound_Classification_Training