Whisper Medium Indonesian

This model is a fine-tuned version of openai/whisper-medium on the Indonesian mozilla-foundation/common_voice_11_0, magic_data, titml and google/fleurs dataset. It achieves the following results:

CV11 test split:

• Loss: 0.0698
• Wer: 3.8274

Google/fleurs test split:

• Wer: 9.74

Usage

from transformers import pipeline
transcriber = pipeline(
  "automatic-speech-recognition", 
  model="cahya/whisper-medium-id"
)
transcriber.model.config.forced_decoder_ids = (
  transcriber.tokenizer.get_decoder_prompt_ids(
    language="id" 
    task="transcribe"
  )
)
transcription = transcriber("my_audio_file.mp3")

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-06
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• 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

Evaluation

We evaluated the model using the test split of two datasets, the Common Voice 11 and the Google Fleurs. As Whisper can transcribe casing and punctuation, we also evaluate its performance using raw and normalized text. (lowercase + removal of punctuations). The results are as follows:

Common Voice 11

Google/Fleurs

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.0+cu117
• Datasets 2.7.1.dev0
• Tokenizers 0.13.2