🧠
Ai Light Dance Singing2 Ft Wav2vec2 Large Xlsr 53 model by gary109
⭐ 23.6
🧠
Model

Ai Light Dance Singing2 Ft Wav2vec2 Large Xlsr 53

by gary109 hf-model--gary109--ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53
Nexus Index
23.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 1
R: Recency 6
Q: Quality 50
Tech Context
Vital Performance
8 DL / 30D
0.0%
Source →
Audited 23.6 FNI Score
Tiny - Params
- Context
8 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--gary109--ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53
License Apache-2.0
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__gary109__ai_light_dance_singing2_ft_wav2vec2_large_xlsr_53,
  author = {gary109},
  title = {Ai Light Dance Singing2 Ft Wav2vec2 Large Xlsr 53 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
gary109. (2026). Ai Light Dance Singing2 Ft Wav2vec2 Large Xlsr 53 [Model]. Free2AITools. https://huggingface.co/gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download gary109/ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
23.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 1
Recency (R) 6
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Ai Light Dance Singing2 Ft Wav2vec2 Large Xlsr 53: Semantic (S:50), Authority (A:0), Popularity (P:1), Recency (R:6), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

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🚀 What's Next?

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Technical Deep Dive

ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the /WORKSPACE/ASANTE/AI-LIGHT-DANCE_DATASETS/AI_LIGHT_DANCE.PY - ONSET-SINGING2 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7583
  • Wer: 0.9386

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: 4e-06
  • train_batch_size: 10
  • eval_batch_size: 10
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 160
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 30.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
27.4755 1.0 112 23.2618 1.0
5.5145 2.0 224 5.2213 1.0
4.2211 3.0 336 4.1673 1.0
3.8386 4.0 448 3.8253 1.0
3.5531 5.0 560 3.6286 1.0
3.5215 6.0 672 3.4762 0.9864
3.3493 7.0 784 3.3549 0.9847
3.1264 8.0 896 3.1797 0.9759
2.7557 9.0 1008 2.8703 0.9865
2.6345 10.0 1120 2.6736 0.9970
2.4297 11.0 1232 2.5638 1.0337
2.3057 12.0 1344 2.3680 0.9839
2.1436 13.0 1456 2.2367 0.9648
2.0856 14.0 1568 2.1635 0.9586
2.0035 15.0 1680 2.0945 0.9645
1.9134 16.0 1792 2.0395 0.9630
1.9443 17.0 1904 2.0017 0.9401
1.8988 18.0 2016 1.9514 0.9493
1.8141 19.0 2128 1.9111 0.9475
1.8344 20.0 2240 1.8790 0.9395
1.7775 21.0 2352 1.8616 0.9503
1.7517 22.0 2464 1.8333 0.9433
1.7037 23.0 2576 1.8156 0.9372
1.7158 24.0 2688 1.7961 0.9482
1.7111 25.0 2800 1.7817 0.9422
1.69 26.0 2912 1.7819 0.9430
1.6889 27.0 3024 1.7721 0.9386
1.6546 28.0 3136 1.7647 0.9453
1.6542 29.0 3248 1.7653 0.9375
1.647 30.0 3360 1.7583 0.9386

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.9.1+cu102
  • Datasets 2.3.3.dev0
  • Tokenizers 0.12.1

⚠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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🤗 Data Source: Hugging Face ↗
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--gary109--ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53
slug
gary109--ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53
source
huggingface
author
gary109
license
Apache-2.0
tags
transformers, pytorch, tensorboard, wav2vec2, automatic-speech-recognition, generated_from_trainer, license:apache-2.0, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
automatic-speech-recognition

📊 Engagement & Metrics

downloads
8
stars
0
forks
0

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