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DrishtiSharma
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wav2vec2-xls-r-300m-rm-sursilv-d11

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results
Inference Endpoints
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metadata
language:
  - rm-sursilv
license: apache-2.0
tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-rm-sursilv-d11
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice 8
          args: rm-sursilv
        metrics:
          - type: wer
            value: 0.24094169578811844
            name: Test WER
          - name: Test CER
            type: cer
            value: 0.049832791672554284
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: rm-sursilv
        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 - RM-SURSILV dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2511
  • Wer: 0.2415

Evaluation Commands

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

python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 --dataset mozilla-foundation/common_voice_8_0 --config rm-sursilv --split test --log_outputs

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

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

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 125.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.3958 17.44 1500 0.6808 0.6521
0.9663 34.88 3000 0.3023 0.3718
0.7963 52.33 4500 0.2588 0.3046
0.6893 69.77 6000 0.2436 0.2718
0.6148 87.21 7500 0.2521 0.2572
0.5556 104.65 9000 0.2490 0.2442
0.5258 122.09 10500 0.2515 0.2442

Framework versions

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