🧠
Wav2vec2 Lv 60 Espeak Cv Ft Custom Vocab Ds F9 Et model by zacdan4801
⭐ 37.6
🧠
Model

Wav2vec2 Lv 60 Espeak Cv Ft Custom Vocab Ds F9 Et

by zacdan4801 hf-model--zacdan4801--wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
Nexus Index
37.6 Top 10%
S: Semantic 50
A: Authority 0
P: Popularity 3
R: Recency 94
Q: Quality 65
Tech Context
0.32B Params
4.096K Ctx
Vital Performance
26 DL / 30D
0.0%
Source →
Audited 37.6 FNI Score
Tiny 0.32B Params
4k Context
26 Downloads
8G GPU ~2GB Est. VRAM
Dense WAV2VEC2FORCTC Architecture
Model Information Summary
Entity Passport
Registry ID hf-model--zacdan4801--wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
Provider huggingface
💾

Compute Threshold

~1.5GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__zacdan4801__wav2vec2_lv_60_espeak_cv_ft_custom_vocab_ds_f9_et,
  author = {zacdan4801},
  title = {Wav2vec2 Lv 60 Espeak Cv Ft Custom Vocab Ds F9 Et Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/zacdan4801/wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
zacdan4801. (2026). Wav2vec2 Lv 60 Espeak Cv Ft Custom Vocab Ds F9 Et [Model]. Free2AITools. https://huggingface.co/zacdan4801/wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
🤗 HF Download
huggingface-cli download zacdan4801/wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.6
TOP 10% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 3
Recency (R) 94
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Wav2vec2 Lv 60 Espeak Cv Ft Custom Vocab Ds F9 Et: Semantic (S:50), Authority (A:0), Popularity (P:3), Recency (R:94), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3430
  • Per: 0.2633

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Per
1.2926 14.8513 1455 0.3368 0.2592
1.229 29.6974 2910 0.3430 0.2633

Framework versions

  • Transformers 4.57.6
  • Pytorch 2.9.1+cu128
  • Datasets 4.5.0
  • Tokenizers 0.22.2

⚠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.

View Original Source →

📝 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.
Top Tier

Social Proof

HuggingFace Hub
26Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

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--zacdan4801--wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
slug
zacdan4801--wav2vec2-lv-60-espeak-cv-ft-custom_vocab-ds-f9-et
source
huggingface
author
zacdan4801
license
tags
transformers, tensorboard, safetensors, wav2vec2, automatic-speech-recognition, generated_from_trainer, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
Wav2Vec2ForCTC
params billions
0.32
context length
4,096
pipeline tag
automatic-speech-recognition
vram gb
1.5
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
26
stars
0
forks
0

Data indexed from public sources. Updated daily.