README.md · DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 at main

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

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

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