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DrishtiSharma
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wav2vec2-large-xls-r-300m-mr-v2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Marathi
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
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wav2vec2-large-xls-r-300m-mr-v2

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

  • Loss: 0.8729
  • Wer: 0.4942

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-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs

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

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1

Note: Marathi language not found in speech-recognition-community-v2/dev_data!

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.000333
  • 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
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
8.4934 9.09 200 3.7326 1.0
3.4234 18.18 400 3.3383 0.9996
3.2628 27.27 600 2.7482 0.9992
1.7743 36.36 800 0.6755 0.6787
1.0346 45.45 1000 0.6067 0.6193
0.8137 54.55 1200 0.6228 0.5612
0.6637 63.64 1400 0.5976 0.5495
0.5563 72.73 1600 0.7009 0.5383
0.4844 81.82 1800 0.6662 0.5287
0.4057 90.91 2000 0.6911 0.5303
0.3582 100.0 2200 0.7207 0.5327
0.3163 109.09 2400 0.7107 0.5118
0.2761 118.18 2600 0.7538 0.5118
0.2415 127.27 2800 0.7850 0.5178
0.2127 136.36 3000 0.8016 0.5034
0.1873 145.45 3200 0.8302 0.5187
0.1723 154.55 3400 0.9085 0.5223
0.1498 163.64 3600 0.8396 0.5126
0.1425 172.73 3800 0.8776 0.5094
0.1258 181.82 4000 0.8651 0.5014
0.117 190.91 4200 0.8772 0.4970
0.1093 200.0 4400 0.8729 0.4942

Framework versions

  • Transformers 4.16.1
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0
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Dataset used to train DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2

Evaluation results

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