README.md

metadata

language:
  - rm-vallader
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - rm-vallader
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-xls-r-300m-rm-vallader-d1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: rm-vallader
        metrics:
          - name: Test WER
            type: wer
            value: 0.26472007722007723
          - name: Test CER
            type: cer
            value: 0.05860608074430969
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: vot
        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-VALLADER dataset. It achieves the following results on the evaluation set:

• Loss: 0.2754
• Wer: 0.2831

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-vallader-d1 --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test --log_outputs

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

Romansh-Vallader language not found in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 7.5e-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: 500
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.927	15.15	500	2.9196	1.0
1.3835	30.3	1000	0.5879	0.5866
0.7415	45.45	1500	0.3077	0.3316
0.5575	60.61	2000	0.2735	0.2954
0.4581	75.76	2500	0.2707	0.2802
0.3977	90.91	3000	0.2785	0.2809

Framework versions

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