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Wav2vec Finetuned Tess model by VasilisAsim
⭐ 39
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Model

Wav2vec Finetuned Tess

by VasilisAsim vasilisasim/wav2vec-finetuned-tess
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 6
R: Recency 99
Q: Quality 65
Tech Context
0.09B Params
4.096K Ctx
Vital Performance
61 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Tiny 0.09B Params
4k Context
61 Downloads
8G GPU ~2GB Est. VRAM
Dense WAV2VEC2FORSEQUENCECLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID vasilisasim/wav2vec-finetuned-tess
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{vasilisasim_wav2vec_finetuned_tess,
  author = {VasilisAsim},
  title = {Wav2vec Finetuned Tess Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/VasilisAsim/wav2vec-finetuned-TESS}},
  note = {Accessed via Free2AITools.}
}
APA Style
VasilisAsim. (2026). Wav2vec Finetuned Tess [Model]. Free2AITools. https://huggingface.co/VasilisAsim/wav2vec-finetuned-TESS

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run wav2vec-finetuned-tess
πŸ€— HF Download
huggingface-cli download vasilisasim/wav2vec-finetuned-tess
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 6
Recency (R) 99
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Wav2vec Finetuned Tess: Authority (A:0), Popularity (P:6), Recency (R:99), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ Incomplete Data

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

View Original Source β†’

πŸ“ Limitations & Considerations

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

Social Proof

HuggingFace Hub
61Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--vasilisasim--wav2vec-finetuned-tess
slug
vasilisasim--wav2vec-finetuned-tess
source
huggingface
author
VasilisAsim
tags
transformers, safetensors, wav2vec2, audio-classification, arxiv:1910.09700, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Wav2Vec2ForSequenceClassification
params billions
0.09
context length
4,096
pipeline tag
audio-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
61

Data indexed from public sources. Updated daily.