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wav2vec2-large-xls-r-300m-ia

Automatic Speech Recognition
Transformers
PyTorch
Interlingua
wav2vec2
Generated from Trainer
hf-asr-leaderboard
robust-speech-event
mozilla-foundation/common_voice_8_0
Eval Results
Inference Endpoints
Model card Files Community
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metadata
language:
  - id
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-ia
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: ia
        metrics:
          - name: Test WER using LM
            type: wer
            value: 8.6074
          - name: Test CER using LM
            type: cer
            value: 2.4147
          - name: Test WER without LM
            type: wer
            value: 20.1776
          - name: Test CER without LM
            type: cer
            value: 4.7205

wav2vec2-large-xls-r-300m-ia

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

  • Loss: 0.1452
  • Wer: 0.1253

Training Procedure

Training is conducted in Google Colab, the training notebook provided in the repo

Training and evaluation data

Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua)

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 400
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
7.432 1.87 400 2.9636 1.0
2.6922 3.74 800 2.2111 0.9977
1.2581 5.61 1200 0.4864 0.4028
0.6232 7.48 1600 0.2807 0.2413
0.4479 9.35 2000 0.2219 0.1885
0.3654 11.21 2400 0.1886 0.1606
0.323 13.08 2800 0.1716 0.1444
0.2935 14.95 3200 0.1687 0.1443
0.2707 16.82 3600 0.1632 0.1382
0.2559 18.69 4000 0.1507 0.1337
0.2433 20.56 4400 0.1572 0.1358
0.2338 22.43 4800 0.1489 0.1305
0.2258 24.3 5200 0.1485 0.1278
0.2218 26.17 5600 0.1470 0.1272
0.2169 28.04 6000 0.1470 0.1270
0.2117 29.91 6400 0.1452 0.1253

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0