metadata

language:
  - pt
license: apache-2.0
tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - pt
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: sew-tiny-portuguese-cv8
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 33.71
          - name: Test CER
            type: cer
            value: 10.69
      - 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: 52.79
          - name: Test CER
            type: cer
            value: 20.98
      - 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: 53.18
      - 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: 55.23

sew-tiny-portuguese-cv8

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: 0.4082
Wer: 0.3053

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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	1.93	1000	2.9134	0.9767
2.9224	3.86	2000	2.8405	0.9789
2.9224	5.79	3000	2.8094	0.9800
2.8531	7.72	4000	2.7439	0.9891
2.8531	9.65	5000	2.7057	1.0159
2.7721	11.58	6000	2.7235	1.0709
2.7721	13.51	7000	2.5931	1.1035
2.6566	15.44	8000	2.2171	0.9884
2.6566	17.37	9000	1.2399	0.8081
1.9558	19.31	10000	0.9045	0.6353
1.9558	21.24	11000	0.7705	0.5533
1.4987	23.17	12000	0.7068	0.5165
1.4987	25.1	13000	0.6641	0.4718
1.3811	27.03	14000	0.6043	0.4470
1.3811	28.96	15000	0.5532	0.4268
1.2897	30.89	16000	0.5371	0.4101
1.2897	32.82	17000	0.5924	0.4150
1.225	34.75	18000	0.4949	0.3894
1.225	36.68	19000	0.5591	0.4045
1.193	38.61	20000	0.4927	0.3731
1.193	40.54	21000	0.4922	0.3712
1.1482	42.47	22000	0.4799	0.3662
1.1482	44.4	23000	0.4846	0.3648
1.1201	46.33	24000	0.4770	0.3623
1.1201	48.26	25000	0.4530	0.3426
1.0892	50.19	26000	0.4523	0.3527
1.0892	52.12	27000	0.4573	0.3443
1.0583	54.05	28000	0.4488	0.3353
1.0583	55.98	29000	0.4295	0.3285
1.0319	57.92	30000	0.4321	0.3220
1.0319	59.85	31000	0.4244	0.3236
1.0076	61.78	32000	0.4197	0.3201
1.0076	63.71	33000	0.4230	0.3208
0.9851	65.64	34000	0.4090	0.3127
0.9851	67.57	35000	0.4088	0.3133
0.9695	69.5	36000	0.4123	0.3088
0.9695	71.43	37000	0.4017	0.3090
0.9514	73.36	38000	0.4184	0.3086
0.9514	75.29	39000	0.4075	0.3043
0.944	77.22	40000	0.4082	0.3053

Framework versions

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