metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - f1
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_with_wer_cer_f1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: as
          split: test
          args: as
        metrics:
          - name: F1
            type: f1
            value: 0.032467532467532464
          - name: Wer
            type: wer
            value: 0.9675324675324676

wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_with_wer_cer_f1

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

Loss: 0.8170
Exact Match: 0.0325
F1: 0.0325
Wer: 0.9675
Cer: 0.1450

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: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	F1	Wer	Cer
4.6801	9.8765	400	0.8184	0.0032	0.0032	0.9968	0.2199
0.2678	19.7531	800	0.7753	0.0227	0.0227	0.9773	0.1628
0.1009	29.6296	1200	0.8270	0.0292	0.0292	0.9708	0.1504
0.0619	39.5062	1600	0.8170	0.0325	0.0325	0.9675	0.1450

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1