metadata

language:
  - rm-vallader
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - rm-vallader
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Romansh Vallader
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: rm-vallader
        metrics:
          - name: Test WER
            type: wer
            value: 31.689
          - name: Test CER
            type: cer
            value: 7.202

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

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

Loss: 0.3155
Wer: 0.3162

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: 500
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9556	15.62	500	2.9300	1.0
1.7874	31.25	1000	0.7566	0.6509
1.0131	46.88	1500	0.3671	0.3828
0.8439	62.5	2000	0.3350	0.3416
0.7502	78.12	2500	0.3155	0.3296
0.7093	93.75	3000	0.3182	0.3186

Framework versions

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