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Whisper Medium Swedish dataset by marinone94
⭐ 25.6
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Dataset

Whisper Medium Swedish

by marinone94 hf-model--marinone94--whisper-medium-swedish
Nexus Index
25.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 23
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 25.6 FNI Score
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- Rows
Parquet Format
- Tokens
Dataset Information Summary
Entity Passport
Registry ID hf-model--marinone94--whisper-medium-swedish
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__marinone94__whisper_medium_swedish,
  author = {marinone94},
  title = {Whisper Medium Swedish Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--marinone94--whisper-medium-swedish}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
marinone94. (2026). Whisper Medium Swedish [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--marinone94--whisper-medium-swedish

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
25.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 23

đŸ’Ŧ Index Insight

FNI V2.0 for Whisper Medium Swedish: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:23).

Free2AITools Nexus Index

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Schema structure is shown in the Field Logic panel when available.

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Dataset Specification

Whisper Medium Swedish

This model is a fine-tuned version of Whisper Medium Nordic on the mozilla-foundation/common_voice_11_0 (train+validation), the babelbox/babelbox_voice (NST SV - train split) and the google/fleurs (sv_se - train+validation+test) datasets. It achieves the following results on the evaluation set:

  • eval_loss: 0.2483
  • eval_wer: 9.8914
  • eval_runtime: 2924.8709
  • eval_samples_per_second: 1.733
  • eval_steps_per_second: 0.108

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 250
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2

WandB run

https://wandb.ai/pn-aa/whisper/runs/z2lzjx4x?workspace=user-emilio_marinone

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

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Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--marinone94--whisper-medium-swedish
slug
marinone94--whisper-medium-swedish
source
huggingface
author
marinone94
license
tags

⚙ī¸ Technical Specs

architecture
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params billions
null
context length
null
pipeline tag

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