metadata

language:
  - mt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - mt
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-1b-cv8-mt
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: mt
        metrics:
          - name: Test WER
            type: wer
            value: 19.74
          - name: Test CER
            type: cer
            value: 2.89
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: mt
        metrics:
          - name: Test WER
            type: wer
            value: null
          - name: Test CER
            type: cer
            value: null

wav2vec2-large-xls-r-1b-cv8-mt

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

Loss: 0.2210
Wer: 0.1974

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following config and hyperparameters were used during training:

model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-xls-r-1b", attention_dropout=0.05, hidden_dropout=0.05, feat_proj_dropout=0.05, mask_time_prob=0.55, mask_feature_prob=0.10, layerdrop=0.05, ctc_zero_infinity=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer), )

from transformers import TrainingArguments

training_args = TrainingArguments( output_dir=repo_name, group_by_length=True, per_device_train_batch_size=32, gradient_accumulation_steps=2, evaluation_strategy="steps", num_train_epochs=50, gradient_checkpointing=True, fp16=True, save_steps=400, eval_steps=400, logging_steps=400, learning_rate=5.5e-05, warmup_steps=500, save_total_limit=2, push_to_hub=True, report_to="tensorboard")

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.4564	13.33	400	0.3783	0.3981
0.7931	26.66	800	0.2377	0.2298
0.5364	39.98	1200	0.2210	0.1974

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.0