This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

Loss: 0.4253
F1 Score: 0.7984
Label F1: 0.8971
Wer: 0.0599

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: 0.0001
train_batch_size: 64
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Score	Label F1	Wer
0.4435	0.36	200	0.4357	0.4513	0.7168	0.0599
0.4309	0.71	400	0.4306	0.6751	0.8354	0.0599
0.4235	1.07	600	0.4282	0.6722	0.8548	0.0599
0.4267	1.43	800	0.4269	0.7073	0.8455	0.0599
0.4254	1.79	1000	0.4264	0.7273	0.8678	0.0599
0.4264	2.14	1200	0.4264	0.7398	0.8780	0.0599
0.4206	2.5	1400	0.4262	0.7206	0.8583	0.0599
0.4232	2.86	1600	0.4260	0.7410	0.8685	0.0599
0.4249	3.22	1800	0.4255	0.7603	0.8926	0.0599
0.4239	3.57	2000	0.4256	0.7631	0.8835	0.0599
0.4213	3.93	2200	0.4255	0.7692	0.8988	0.0599
0.4213	4.29	2400	0.4256	0.7769	0.8926	0.0599
0.4244	4.65	2600	0.4253	0.7711	0.8996	0.0599
0.4234	5.0	2800	0.4254	0.7386	0.8797	0.0599
0.4222	5.36	3000	0.4252	0.7917	0.9	0.0599
0.4239	5.72	3200	0.4254	0.7801	0.8963	0.0599
0.4201	6.08	3400	0.4254	0.7950	0.8954	0.0599
0.4194	6.43	3600	0.4253	0.7851	0.9008	0.0599
0.4203	6.79	3800	0.4252	0.7934	0.9091	0.0599
0.4214	7.15	4000	0.4253	0.8050	0.9046	0.0599
0.4206	7.51	4200	0.4253	0.8	0.9	0.0599
0.4205	7.86	4400	0.4253	0.8050	0.9129	0.0599
0.4207	8.22	4600	0.4253	0.7951	0.9016	0.0599
0.4218	8.58	4800	0.4253	0.7984	0.8971	0.0599
0.4201	8.94	5000	0.4253	0.7984	0.8971	0.0599

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0
Datasets 2.14.6
Tokenizers 0.14.1

qmeeus/whisper-large-multilingual-spoken-ner-pipeline-step-1

WhisperForSpokenNER

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

qmeeus/
whisper-large-multilingual-spoken-ner-pipeline-step-1