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

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

This model is a fine-tuned version of wav2vec2-large-xls-r-1b-cv8-mt-lm on the common_voice 8 dataset. It achieves the following results on the test set:

• Loss: 0.2210
• Wer: 0.1974

Note that the above test results come from the original model without LM (language model) which can be found at https://huggingface.co/RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt. The results with the LM model can be found on the right side of this model card.

Model description

Model RuudVelo/wav2vec2-large-xls-r-1b-cv8-mt which has been improved with a KenLM 3-gram.

Intended uses & limitations

More information needed

Training and evaluation data

Common Voice 8 mt dataset has been used for the model

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")

Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0