Whisper Medium Id

🚀 Whisper Medium Indonesian

This model is a fine - tuned version of openai/whisper-medium on Indonesian datasets, which can achieve better performance in automatic speech recognition tasks.

🚀 Quick Start

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 Examples

Basic 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")

📚 Documentation

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](https://huggingface.co/datasets/mozilla - foundation/common_voice_11_0) 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

📄 License

This model is licensed under the apache - 2.0 license.

📋 Model Information