README.md

metadata

language:
  - ps
base_model: ylacombe/w2v-bert-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_16_0
  - generated_from_trainer
datasets:
  - common_voice_16_0
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice-ps-demo
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - PS
          type: common_voice_16_0
          config: ps
          split: test
          args: 'Config: ps, Training split: train+validation, Eval split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.9484029484029484

wav2vec2-common_voice-ps-demo

This model is a fine-tuned version of ylacombe/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - PS dataset. It achieves the following results on the evaluation set:

• Loss: 3.0510
• Wer: 0.9484

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: 0.0003
• train_batch_size: 16
• eval_batch_size: 8
• 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: 500
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	3.08	100	10.3691	1.0
No log	6.15	200	3.5670	1.0
No log	9.23	300	3.1139	0.9484
No log	12.31	400	3.0949	0.9484

Framework versions

• Transformers 4.37.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.15.0