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DrishtiSharma
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wav2vec2-large-xls-r-300m-pa-IN-dx1

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
mozilla-foundation/common_voice_8_0
generated_from_trainer
pa-IN
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language:
  - pa-IN
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - pa-IN
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-pa-IN-dx1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: pa-IN
        metrics:
          - name: Test WER
            type: wer
            value: 0.48725989807918463
          - name: Test CER
            type: cer
            value: 0.1687305197540224
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: pa-IN
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0855
  • Wer: 0.4755

Evaluation Commands

  1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs

  1. To evaluate on speech-recognition-community-v2/dev_data

Punjabi language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1200
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.4607 9.26 500 2.7746 1.0416
0.3442 18.52 1000 0.9114 0.5911
0.2213 27.78 1500 0.9687 0.5751
0.1242 37.04 2000 1.0204 0.5461
0.0998 46.3 2500 1.0250 0.5233
0.0727 55.56 3000 1.1072 0.5382
0.0605 64.81 3500 1.0588 0.5073
0.0458 74.07 4000 1.0818 0.5069
0.0338 83.33 4500 1.0948 0.5108
0.0223 92.59 5000 1.0986 0.4775

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0