README.md

metadata

language:
  - uk
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_8_0
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-xls-r-1b-hy-cv
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice uk
          args: uk
        metrics:
          - type: wer
            value: 12.246920571994902
            name: WER LM
          - type: cer
            value: 2.513653497966816
            name: CER LM
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 46.56
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: uk
        metrics:
          - name: Test WER
            type: wer
            value: 35.98

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UK dataset. It achieves the following results on the evaluation set:

• Loss: 0.1747
• Wer: 0.2107
• Cer: 0.0408

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: 8e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 8000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.3719	4.35	500	0.3389	0.4236	0.0833
1.1361	8.7	1000	0.2309	0.3162	0.0630
1.0517	13.04	1500	0.2166	0.3056	0.0597
1.0118	17.39	2000	0.2141	0.2784	0.0557
0.9922	21.74	2500	0.2231	0.2941	0.0594
0.9929	26.09	3000	0.2171	0.2892	0.0587
0.9485	30.43	3500	0.2236	0.2956	0.0599
0.9573	34.78	4000	0.2314	0.3043	0.0616
0.9195	39.13	4500	0.2169	0.2812	0.0580
0.8915	43.48	5000	0.2109	0.2780	0.0560
0.8449	47.83	5500	0.2050	0.2534	0.0514
0.8028	52.17	6000	0.2032	0.2456	0.0492
0.7881	56.52	6500	0.1890	0.2380	0.0469
0.7423	60.87	7000	0.1816	0.2245	0.0442
0.7248	65.22	7500	0.1789	0.2165	0.0422
0.6993	69.57	8000	0.1747	0.2107	0.0408

Framework versions

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