Hugging Face's logo

dmusingu
/
WHISPER-MEDIUM-SWAHILI-ASR-CV-14

Automatic Speech Recognition
Transformers
Safetensors
Swahili
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
WHISPER-MEDIUM-SWAHILI-ASR-CV-14 / README.md
dmusingu's picture
dmusingu
End of training
d599aa1 verified
preview code
|
raw
history blame contribute delete
2.75 kB
metadata
language:
  - sw
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_14_0
metrics:
  - wer
model-index:
  - name: Whisper Medium  - Denis Musinguzi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 14.0
          type: mozilla-foundation/common_voice_14_0
          config: lg
          split: None
          args: 'config: sw, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.2354584169666847

Whisper Medium - Denis Musinguzi

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 14.0 dataset. It achieves the following results on the evaluation set:

  • Cer: 0.0622
  • Loss: 0.2969
  • Wer: 0.2355

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Cer Validation Loss Wer
0.9513 0.3 800 0.0998 0.4428 0.4067
0.313 0.61 1600 0.0913 0.3519 0.3427
0.2593 0.91 2400 0.0628 0.3160 0.2689
0.1887 1.22 3200 0.0633 0.3049 0.2574
0.1642 1.52 4000 0.0752 0.2906 0.2655
0.1595 1.82 4800 0.0737 0.2807 0.2617
0.1288 2.13 5600 0.0643 0.2889 0.2416
0.0928 2.43 6400 0.0629 0.2860 0.2387
0.0887 2.74 7200 0.0572 0.2838 0.2309
0.0836 3.04 8000 0.0575 0.2897 0.2338
0.0466 3.34 8800 0.0572 0.2968 0.2322
0.045 3.65 9600 0.0622 0.2969 0.2355

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.2.1
  • Datasets 2.17.0
  • Tokenizers 0.15.2