🧠
Wav2vec2 Emotion model by lisha23
⭐ 37.6
🧠
Model

Wav2vec2 Emotion

by lisha23 hf-model--lisha23--wav2vec2-emotion
Nexus Index
37.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 1
R: Recency 89
Q: Quality 65
Tech Context
Vital Performance
7 DL / 30D
0.0%
Source →
Audited 37.6 FNI Score
Tiny - Params
- Context
7 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--lisha23--wav2vec2-emotion
License Apache-2.0
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__lisha23__wav2vec2_emotion,
  author = {lisha23},
  title = {Wav2vec2 Emotion Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/lisha23/wav2vec2-emotion}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
lisha23. (2026). Wav2vec2 Emotion [Model]. Free2AITools. https://huggingface.co/lisha23/wav2vec2-emotion

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download lisha23/wav2vec2-emotion
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 1
Recency (R) 89
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Wav2vec2 Emotion: Semantic (S:50), Authority (A:0), Popularity (P:1), Recency (R:89), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

wav2vec2-emotion

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2219
  • Accuracy: 0.94

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.4852 1.0 75 0.4318 0.8967
0.2804 2.0 150 0.2618 0.9333
0.1609 3.0 225 0.2219 0.94

Framework versions

  • Transformers 4.57.1
  • Pytorch 2.8.0+cu126
  • Datasets 4.4.2
  • Tokenizers 0.22.1

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

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7Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--lisha23--wav2vec2-emotion
slug
lisha23--wav2vec2-emotion
source
huggingface
author
lisha23
license
Apache-2.0
tags
transformers, safetensors, wav2vec2, audio-classification, generated_from_trainer, base_model:facebook/wav2vec2-base, base_model:finetune:facebook/wav2vec2-base, license:apache-2.0, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
audio-classification

📊 Engagement & Metrics

downloads
7
stars
0
forks
null

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