vocaliz-wav2vec2-speech-emotion-recognition-finetuned

This model is a fine-tuned checkpoint built on top of:

Base model: Wiam/wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-ravdess-v8 (Wav2Vec2 / XLSR-style encoder with an 8-class speech-emotion head, originally associated with RAVDESS-oriented training).

Training used the Hugging Face Trainer with AutoModelForAudioClassification, keeping the same label space and classification head structure as the base checkpoint.

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Intended uses

• Research and prototyping for speech emotion recognition (SER) on English, short utterance-level clips.
• Offline or interactive demos (e.g. file-based or microphone pipelines) where approximate emotion labels are sufficient.

Training data

Source mix (by file count)

Combined Emotions training pool (all classes, all files):

Source	Files	Share (approx.)
CREMA-D	7,442	58.15%
TESS	2,800	21.88%
RAVDESS	2,076	16.22%
SAVEE	480	3.75%
Total	12,798	100%

Training procedure (defaults from training script)

Setting	Value
Train / validation split	Stratified ~85% / ~15% (test_size=0.15, seed=42) → ~10,878 train / ~1,920 validation samples (exact counts depend on per-class stratification)
Epochs	5
Optimizer	AdamW with learning rate 3e-5, warmup ratio 0.1, weight decay 0.01
Batch size	4 per device
Max audio length	8 s after resampling (truncation)
Training metrics (per validation)	Accuracy, macro F1
Model selection	Best checkpoint by validation macro F1 (load_best_model_at_end=True)
Precision	FP16 only if CUDA + --fp16 (default run often CPU)

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Evaluation results (external benchmark)

metrics with six displayed classes: neutral, happy, angry, fearful, disgust, surprised.

Model	Accuracy	Macro F1	Weighted F1	Micro F1
Base: Wiam/wav2vec2-lg-xlsr-en-speech-emotion-recognition-finetuned-ravdess-v8	0.9417	0.9367	0.9414	0.9417
This fine-tune (local export wav2vec2-emotions-finetuned)	0.9868	0.9850	0.9868	0.9868

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How to load

from transformers import AutoModelForAudioClassification, AutoFeatureExtractor

MODEL_ID = "amykon/vocaliz-wav2vec2-speech-emotion-recognition-finetuned"

processor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
model = AutoModelForAudioClassification.from_pretrained(MODEL_ID)

Public repo: no token needed for from_pretrained. Private repo: set HF_TOKEN or huggingface-cli login.

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Install dependencies

pip install -r https://huggingface.co/amykon/vocaliz-wav2vec2-speech-emotion-recognition-finetuned/resolve/main/requirements.txt

requirements.txt lists torch, transformers, safetensors, huggingface_hub, numpy, and sounddevice (optional for mic-only scripts).

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Files in this repository

• README.md — this model card
• requirements.txt — pip dependencies for inference
• config.json — model config and label mapping
• preprocessor_config.json — feature extractor (sample rate, etc.)
• model.safetensors — weights