metadata

language:
  - pt
license: apache-2.0
tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - pt
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: sew-tiny-portuguese-cv7
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 28.9
          - name: Test CER
            type: cer
            value: 9.41
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 47.27
          - name: Test CER
            type: cer
            value: 19.62
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 47.3
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 49.83

sew-tiny-portuguese-cv7

This model is a fine-tuned version of lgris/sew-tiny-pt on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.4232
Wer: 0.2745

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
training_steps: 40000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	2.6	1000	1.0034	0.7308
4.1307	5.19	2000	0.6274	0.4721
4.1307	7.79	3000	0.5541	0.4130
1.3117	10.39	4000	0.5302	0.3880
1.3117	12.99	5000	0.5082	0.3644
1.2047	15.58	6000	0.4818	0.3539
1.2047	18.18	7000	0.4822	0.3477
1.14	20.78	8000	0.4781	0.3428
1.14	23.38	9000	0.4840	0.3401
1.0818	25.97	10000	0.4613	0.3251
1.0818	28.57	11000	0.4569	0.3257
1.0451	31.17	12000	0.4494	0.3132
1.0451	33.77	13000	0.4560	0.3201
1.011	36.36	14000	0.4687	0.3174
1.011	38.96	15000	0.4397	0.3122
0.9785	41.56	16000	0.4605	0.3173
0.9785	44.16	17000	0.4380	0.3064
0.9458	46.75	18000	0.4372	0.3048
0.9458	49.35	19000	0.4426	0.3039
0.9126	51.95	20000	0.4317	0.2962
0.9126	54.54	21000	0.4345	0.2960
0.8926	57.14	22000	0.4365	0.2948
0.8926	59.74	23000	0.4306	0.2940
0.8654	62.34	24000	0.4303	0.2928
0.8654	64.93	25000	0.4351	0.2915
0.8373	67.53	26000	0.4340	0.2909
0.8373	70.13	27000	0.4279	0.2907
0.83	72.73	28000	0.4214	0.2867
0.83	75.32	29000	0.4256	0.2849
0.8062	77.92	30000	0.4281	0.2826
0.8062	80.52	31000	0.4398	0.2865
0.7846	83.12	32000	0.4218	0.2812
0.7846	85.71	33000	0.4227	0.2791
0.7697	88.31	34000	0.4200	0.2767
0.7697	90.91	35000	0.4285	0.2791
0.7539	93.51	36000	0.4238	0.2777
0.7539	96.1	37000	0.4288	0.2757
0.7413	98.7	38000	0.4205	0.2748
0.7413	101.3	39000	0.4241	0.2761
0.7348	103.89	40000	0.4232	0.2745

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0