metadata

language:
  - bn
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  - openslr
  - crblp
metrics:
  - wer
model-index:
  - name: Whisper Small - Mohammed Rakib
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: bn_in
          split: test
        metrics:
          - type: wer
            value: 10.8
            name: WER
          - type: cer
            value: 6.55
            name: CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: bn
          split: test
        metrics:
          - type: wer
            value: 8.94
            name: WER
          - type: cer
            value: 4.71
            name: CER

Whisper Small - Mohammed Rakib

This model is a fine-tuned version of openai/whisper-small on the common-voice-11, the google-fleurs, the openslr53 and the crblp speech corpus datasets. It achieves the following results on the evaluation set:

Loss: 0.0617
Cer: 5.4436
Wer: 9.6538

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: 4
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 8000
training_steps: 40000

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
0.5361	0.13	1000	0.4043	22.6599	44.0521
0.2881	0.26	2000	0.2217	16.3939	32.4894
0.2265	0.38	3000	0.1728	13.0425	25.9637
0.1974	0.51	4000	0.1430	11.3260	22.3187
0.1591	0.64	5000	0.1255	10.0167	19.5115
0.1504	0.77	6000	0.1102	8.8333	17.1919
0.1259	0.89	7000	0.1003	8.1863	15.8576
0.1184	1.02	8000	0.0940	7.7868	14.9110
0.1099	1.15	9000	0.0885	7.3675	13.9444
0.1075	1.28	10000	0.0830	6.9648	13.2008
0.095	1.41	11000	0.0789	6.6969	12.6776
0.0943	1.53	12000	0.0766	6.3765	11.9896
0.0923	1.66	13000	0.0731	6.1784	11.7203
0.0824	1.79	14000	0.0699	5.9267	11.1632
0.0756	1.92	15000	0.0683	5.6305	10.6327
0.0634	2.04	16000	0.0671	5.6905	10.6947
0.0618	2.17	17000	0.0662	5.5107	10.2926
0.0679	2.3	18000	0.0643	5.4948	10.1792
0.0589	2.43	19000	0.0647	5.5201	10.1881
0.0623	2.56	20000	0.0633	5.2731	9.8449
0.0558	2.68	21000	0.0623	5.4211	10.0267
0.0564	2.81	22000	0.0617	5.4553	9.9893
0.0552	2.94	23000	0.0607	5.3860	9.7778
0.0403	3.07	24000	0.0621	5.7297	10.0382
0.0406	3.19	25000	0.0617	5.4436	9.6538
0.041	3.32	26000	0.0611	6.0867	10.3834
0.0388	3.45	27000	0.0614	6.1641	10.3890
0.0383	3.58	28000	0.0611	6.1460	10.3537
0.0401	3.71	29000	0.0603	6.9576	11.0697
0.0343	3.83	30000	0.0613	7.1918	11.2243
0.0357	3.96	31000	0.0603	7.3128	11.3313
0.0313	4.09	32000	0.0624	7.3871	11.3861
0.0281	4.22	33000	0.0626	7.8705	11.8248
0.0298	4.34	34000	0.0629	8.3360	12.2368
0.0282	4.47	35000	0.0627	8.7840	12.6270

Framework versions

Transformers 4.30.0.dev0
Pytorch 2.0.0+cu117
Datasets 2.10.2.dev0
Tokenizers 0.13.2