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wav2vec2-large-xls-r-300m-romansh-sursilvan

Automatic Speech Recognition Transformers PyTorch
wav2vec2 mozilla-foundation/common_voice_7_0 generated_from_trainer rm-sursilv robust-speech-event model_for_talk hf-asr-leaderboard Eval Results Inference Endpoints
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metadata
language:
  - rm-sursilv
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - rm-sursilv
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Romansh Sursilvan
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: rm-sursilv
        metrics:
          - name: Test WER
            type: wer
            value: 19.816
          - name: Test CER
            type: cer
            value: 4.153

wav2vec2-large-xls-r-300m-romansh-sursilvan

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RM-SURSILV dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2163
  • Wer: 0.1981

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 7e-05
  • train_batch_size: 32
  • eval_batch_size: 1
  • 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: 120.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.1004 23.81 2000 0.3710 0.4191
0.7002 47.62 4000 0.2342 0.2562
0.5573 71.43 6000 0.2175 0.2177
0.4799 95.24 8000 0.2109 0.1987
0.4511 119.05 10000 0.2164 0.1975

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0