vllm-project/whisper-small-mandarin-lora

This is a copy of chengyili2005/whisper-small-mandarin-lora at commit 892d846, made on 2026-10-30. vLLM CI uses this checkpoint and the original is on a personal account, so we keep a copy here to reduce supply-chain risk.

Whisper Small Mandarin - Chengyi Li

This model is a fine-tuned version of openai/whisper-small on the Common Voice 24.0 - Mandarin dataset. It achieves the following results on the evaluation set:

• Loss: 0.4541
• Cer: 12.8966

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH 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
• training_steps: 40000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.4032	0.5411	1000	0.3531	17.9573
0.2965	1.0823	2000	0.3232	14.1834
0.2964	1.6234	3000	0.3165	14.0472
0.1811	2.1645	4000	0.3155	14.5741
0.1665	2.7056	5000	0.3166	13.7639
0.097	3.2468	6000	0.3293	13.4425
0.1235	3.7879	7000	0.3343	13.7206
0.0801	4.3290	8000	0.3432	13.6987
0.0567	4.8701	9000	0.3471	13.6069
0.0258	5.4113	10000	0.3568	13.4482
0.0374	5.9524	11000	0.3621	13.8458
0.0222	6.4935	12000	0.3677	13.1159
0.0078	7.0346	13000	0.3809	13.1915
0.0104	7.5758	14000	0.3765	13.2965
0.0083	8.1169	15000	0.3826	13.7172
0.0071	8.6580	16000	0.3888	13.1649
0.0036	9.1991	17000	0.3906	13.0962
0.0047	9.7403	18000	0.3964	13.2278
0.0013	10.2814	19000	0.4014	13.0939
0.0026	10.8225	20000	0.4034	13.0489
0.0039	11.3636	21000	0.4064	13.1903
0.0021	11.9048	22000	0.4081	13.1568
0.0009	12.4459	23000	0.4113	13.0016
0.0014	12.9870	24000	0.4138	13.0714
0.0015	13.5281	25000	0.4203	13.1482
0.0009	14.0693	26000	0.4177	13.3778
0.001	14.6104	27000	0.4233	12.9970
0.0004	15.1515	28000	0.4278	12.8977
0.0005	15.6926	29000	0.4298	13.1297
0.0002	16.2338	30000	0.4328	12.9272
0.0002	16.7749	31000	0.4322	12.9641
0.0001	17.3160	32000	0.4368	12.9924
0.0015	17.8571	33000	0.4414	13.1222
0.0001	18.3983	34000	0.4429	12.9358
0.0001	18.9394	35000	0.4459	12.9451
0.0001	19.4805	36000	0.4510	12.9658
0.0001	20.0216	37000	0.4505	12.8741
0.0001	20.5628	38000	0.4511	12.8960
0.0001	21.1039	39000	0.4533	12.8735
0.0003	21.6450	40000	0.4541	12.8966

Framework versions

• PEFT 0.18.1
• Transformers 4.52.0
• Pytorch 2.9.1+cu128
• Datasets 4.5.0
• Tokenizers 0.21.4