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DrishtiSharma
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wav2vec2-large-xls-r-300m-gn-k1

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Guaraní
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Metrics Community
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metadata
language:
  - gn
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - gn
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-gn-k1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: gn
        metrics:
          - name: Test WER
            type: wer
            value: 0.711890243902439
          - name: Test CER
            type: cer
            value: 0.13311897106109324
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: gn
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-gn-k1

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

  • Loss: 0.9220
  • Wer: 0.6631

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-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs

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

NA

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.00018
  • 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: 600
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
15.9402 8.32 100 6.9185 1.0
4.6367 16.64 200 3.7416 1.0
3.4337 24.96 300 3.2581 1.0
3.2307 33.32 400 2.8008 1.0
1.3182 41.64 500 0.8359 0.8171
0.409 49.96 600 0.8470 0.8323
0.2573 58.32 700 0.7823 0.7576
0.1969 66.64 800 0.8306 0.7424
0.1469 74.96 900 0.9225 0.7713
0.1172 83.32 1000 0.7903 0.6951
0.1017 91.64 1100 0.8519 0.6921
0.0851 99.96 1200 0.8129 0.6646
0.071 108.32 1300 0.8614 0.7043
0.061 116.64 1400 0.8414 0.6921
0.0552 124.96 1500 0.8649 0.6905
0.0465 133.32 1600 0.8575 0.6646
0.0381 141.64 1700 0.8802 0.6723
0.0338 149.96 1800 0.8731 0.6845
0.0306 158.32 1900 0.9003 0.6585
0.0236 166.64 2000 0.9408 0.6616
0.021 174.96 2100 0.9353 0.6723
0.0212 183.32 2200 0.9269 0.6570
0.0191 191.64 2300 0.9277 0.6662
0.0161 199.96 2400 0.9220 0.6631

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0