readme: ---
  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
  - hf-asr-leaderboard
  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: 17.57
      - name: Test CER
        type: cer
        value: 3.86
    - 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
  ---
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xls-r-1b-cv8-mt
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/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
  
  Note: another version of this model is available with a KenLM 3gram model. This model performs better than this model. See https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt-lm
  
  ## 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 |
  
  Note that the test WER of 19.74 is different than the above reported 17.57. This was due to a bug which was found while processing files with an older version of the datasets library. The right library is listed below.
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  