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audio-emotion-pipeline-v22
Sammaiah/audio-emotion-pipeline-v22
audio-emotion-pipeline-v22 is a machine learning model from Sammaiah. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
SOTA multi-modal audio emotion recognition pipeline: 1. Speaker Diarization (pyannote/speaker-diarization-3.1) 2. Text Feature Extraction (microsoft/deberta-v3-large 1024d) 3. Acoustic SER Extraction (iic/emotion2vecp…
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Updated Sep 24, 2026
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- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 24, 2026
- SHA
- 5454dbce
Model card
From the Hugging Face model README
Audio Emotion Pipeline (v22 & v22.2 SOTA)
SOTA multi-modal audio emotion recognition pipeline:
- Speaker Diarization (
pyannote/speaker-diarization-3.1) - Text Feature Extraction (
microsoft/deberta-v3-large1024d) - Acoustic SER Extraction (
iic/emotion2vec_plus_large1024d @ 50fps) - Prosody Extraction (openSMILE 25d eGeMAPSv02 LLD + Speaker Z-norm)
- Classification (Dynamic Sigmoid Gated Fusion + Self-Attention Pooling)
🚀 Quickstart
from emotion_pipeline.pipeline import EmotionPipeline
import json
# Initialize for v22.2 Conversation Level SOTA (or model_version="v22" for Single Utterance)
pipeline = EmotionPipeline(hf_token="your_hf_token", model_version="v22.2", device="cuda")
result = pipeline.analyze("meeting.wav", max_past=3, max_future=3)
print(json.dumps(result, indent=2))
- Created
- Sep 23, 2026
- Updated
- Sep 24, 2026