Hugging Face's logo

Akashpb13
/
Kabyle_xlsr

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Kabyle
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
sw
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
3
Kabyle_xlsr / README.md
Akashpb13's picture
Akashpb13
Create README.md
f1e1d8f
preview code
|
raw
history blame
6.69 kB
metadata
language:
  - kab
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - sw
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: Akashpb13/Kabyle_xlsr
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: kab
        metrics:
          - name: Test WER
            type: wer
            value: 0.3188425282720088
          - name: Test CER
            type: cer
            value: 0.09443079928558358

Akashpb13/xlsr_hungarian_new

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):

  • Loss: 0.159032
  • Wer: 0.187934

Model description

"facebook/wav2vec2-xls-r-300m" was finetuned.

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset. Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

Training procedure

For creating the training dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.000096
  • train_batch_size: 8
  • seed: 13
  • gradient_accumulation_steps: 4
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Step Training Loss Validation Loss Wer
500 7.199800 3.130564 1.000000
1000 1.570200 0.718097 0.734682
1500 0.850800 0.524227 0.640532
2000 0.712200 0.468694 0.603454
2500 0.651200 0.413833 0.573025
3000 0.603100 0.403680 0.552847
3500 0.553300 0.372638 0.541719
4000 0.537200 0.353759 0.531191
4500 0.506300 0.359109 0.519601
5000 0.479600 0.343937 0.511336
5500 0.479800 0.338214 0.503948
6000 0.449500 0.332600 0.495221
6500 0.439200 0.323905 0.492635
7000 0.434900 0.310417 0.484555
7500 0.403200 0.311247 0.483262
8000 0.401500 0.295637 0.476566
8500 0.397000 0.301321 0.471672
9000 0.371600 0.295639 0.468440
9500 0.370700 0.294039 0.468902
10000 0.364900 0.291195 0.468440
10500 0.348300 0.284898 0.461098
11000 0.350100 0.281764 0.459805
11500 0.336900 0.291022 0.461606
12000 0.330700 0.280467 0.455234
12500 0.322500 0.271714 0.452694
13000 0.307400 0.289519 0.455465
13500 0.309300 0.281922 0.451217
14000 0.304800 0.271514 0.452186
14500 0.288100 0.286801 0.446830
15000 0.293200 0.276309 0.445399
15500 0.289800 0.287188 0.446230
16000 0.274800 0.286406 0.441243
16500 0.271700 0.284754 0.441520
17000 0.262500 0.275431 0.442167
17500 0.255500 0.276575 0.439858
18000 0.260200 0.269911 0.435425
18500 0.250600 0.270519 0.434686
19000 0.243300 0.267655 0.437826
19500 0.240600 0.277109 0.431731
20000 0.237200 0.266622 0.433994
20500 0.231300 0.273015 0.428868
21000 0.227200 0.263024 0.430161
21500 0.220400 0.272880 0.429607
22000 0.218600 0.272340 0.426883
22500 0.213100 0.277066 0.428407
23000 0.205000 0.278404 0.424020
23500 0.200900 0.270877 0.418987
24000 0.199000 0.289120 0.425821
24500 0.196100 0.275831 0.424066
25000 0.191100 0.282822 0.421850
25500 0.190100 0.275820 0.418248
26000 0.178800 0.279208 0.419125
26500 0.183100 0.271464 0.419218
27000 0.177400 0.280869 0.419680
27500 0.171800 0.279593 0.414924
28000 0.172900 0.276949 0.417648
28500 0.164900 0.283491 0.417786
29000 0.164800 0.283122 0.416078
29500 0.165500 0.281969 0.415801
30000 0.163800 0.283319 0.412753
30500 0.153500 0.285702 0.414046
31000 0.156500 0.285041 0.412615
31500 0.150900 0.284336 0.413723
32000 0.151800 0.285922 0.412292
32500 0.149200 0.289461 0.412153
33000 0.145400 0.291322 0.409567
33500 0.145600 0.294361 0.409614
34000 0.144200 0.290686 0.409059
34500 0.143400 0.289474 0.409844
35000 0.143500 0.290340 0.408367
35500 0.143200 0.289581 0.407351
36000 0.138400 0.292782 0.408736
36500 0.137900 0.289108 0.408044
37000 0.138200 0.292127 0.407166
37500 0.134600 0.291797 0.408413
38000 0.139800 0.290056 0.408090
38500 0.136500 0.291198 0.408090
39000 0.137700 0.289696 0.408044

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 1.18.3
  • Tokenizers 0.10.3

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with split test
python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test