README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - automatic-speech-recognition
  - en
  - generated_from_trainer
  - hf-asr-leaderboard
  - librispeech_asr
  - robust-speech-event
datasets:
  - librispeech_asr
base_model: facebook/wav2vec2-xls-r-300m
model-index:
  - name: XLS-R-300M - English
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: LibriSpeech (clean)
          type: librispeech_asr
          config: clean
          split: test
          args:
            language: en
        metrics:
          - type: wer
            value: 12.29
            name: Test WER
          - type: cer
            value: 3.34
            name: Test CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: en
        metrics:
          - type: wer
            value: 36.75
            name: Validation WER
          - type: cer
            value: 14.83
            name: Validation CER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 8.0
          type: mozilla-foundation/common_voice_8_0
          config: en
          split: test
          args:
            language: en
        metrics:
          - type: wer
            value: 37.81
            name: Test WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: en
        metrics:
          - type: wer
            value: 38.8
            name: Test WER

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

• Loss: 0.1444
• Wer: 0.1167

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.9365	4.17	500	2.9398	0.9999
1.5444	8.33	1000	0.5947	0.4289
1.1367	12.5	1500	0.2751	0.2366
0.9972	16.66	2000	0.2032	0.1797
0.9118	20.83	2500	0.1786	0.1479
0.8664	24.99	3000	0.1641	0.1408
0.8251	29.17	3500	0.1537	0.1267
0.793	33.33	4000	0.1525	0.1244
0.785	37.5	4500	0.1470	0.1184
0.7612	41.66	5000	0.1446	0.1177
0.7478	45.83	5500	0.1449	0.1176
0.7443	49.99	6000	0.1444	0.1167

Framework versions

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