🧠
Wav2vec2 Base Finetuned Gtzan model by jjsprockel
⭐ 25.6
🧠
Model

Wav2vec2 Base Finetuned Gtzan

by jjsprockel jjsprockel/wav2vec2-base-finetuned-gtzan
Free2AITools Nexus Index
25.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 1
R: Recency 11
Q: Quality 50
Tech Context
2 Params
4.096K Ctx
Vital Performance
8 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 25.6 FNI Score
Tiny 2B Params
4k Context
8 Downloads
8G GPU ~3GB Est. VRAM
Dense WAV2VEC2FORSEQUENCECLASSIFICATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID jjsprockel/wav2vec2-base-finetuned-gtzan
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~2.8GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{jjsprockel_wav2vec2_base_finetuned_gtzan,
  author = {jjsprockel},
  title = {Wav2vec2 Base Finetuned Gtzan Model},
  year = {2023},
  howpublished = {\url{https://huggingface.co/jjsprockel/wav2vec2-base-finetuned-gtzan}},
  note = {Accessed via Free2AITools.}
}
APA Style
jjsprockel. (2023). Wav2vec2 Base Finetuned Gtzan [Model]. Free2AITools. https://huggingface.co/jjsprockel/wav2vec2-base-finetuned-gtzan

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run wav2vec2-base-finetuned-gtzan
πŸ€— HF Download
huggingface-cli download jjsprockel/wav2vec2-base-finetuned-gtzan
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) 1
Recency (R) 11
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Wav2vec2 Base Finetuned Gtzan: Authority (A:0), Popularity (P:1), Recency (R:11), Quality (Q:50). 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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Deployment reference

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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.

Social Proof

HuggingFace Hub
8Downloads
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--jjsprockel--wav2vec2-base-finetuned-gtzan
slug
jjsprockel--wav2vec2-base-finetuned-gtzan
source
huggingface
author
jjsprockel
license
Apache-2.0
tags
transformers, pytorch, wav2vec2, audio-classification, generated_from_trainer, dataset:marsyas/gtzan, base_model:facebook/wav2vec2-base, base_model:finetune:facebook/wav2vec2-base, license:apache-2.0, model-index, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Wav2Vec2ForSequenceClassification
params billions
2
context length
4,096
pipeline tag
audio-classification
vram gb
2.8
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
8
stars
0
forks
0

Data indexed from public sources. Updated daily.