Dafisns/whisper-turbo-multilingual-fleurs

Whisper Turbo Fine-Tuned on FLEURS, Common Voice & EdAcc (Indonesian & English)

This model is a fine-tuned version of openai/whisper-large-v3-turbo. It was trained on a combination of Google FLEURS, Common Voice 22.0, and Edinburgh International Accents (EdAcc) datasets.

The training focuses specifically on Indonesian (`id_id`) and English (`en_us`). A unique feature of this model is the inclusion of the EdAcc dataset to improve performance on Indonesian-accented English.

• —Developed by: Dafis Nadhif Saputra
• —Model type: Automatic Speech Recognition (ASR)
• —Language(s): Indonesian (id), English (en)
• —License: Apache-2.0
• —Finetuned from model: openai/whisper-large-v3-turbo

 

Evaluation Results

The model was evaluated using two different schemes:

1. Internal Training Validation

Measured during the training process on a mixed validation set (all datasets combined).

2. Final Standalone Evaluation

Measured after training on the full concatenated test sets for each language.

Training Details

Data Overview

The model was trained on approximately 15,000 samples combining:

• —Google FLEURS (Indonesian & English)
• —Common Voice 22.0 (Indonesian & English)
• —EdAcc (English with Indonesian Accent)

Hyperparameters (Summary)

The model was trained using PEFT (LoRA) to efficiently adapt the weights.

• —Learning Rate: 5e-5
• —Batch Size: 32 (Effective)
• —Epochs: 2
• —Precision: FP16
• —Optimizer: AdamW
• —LoRA Rank: 32

How to Get Started with the Model

You can use the pipeline from the transformers library to easily transcribe audio.

from transformers import pipeline
import torch

# Replace with your model ID
model_id = "Dafisns/whisper-turbo-multilingual-fleurs"

# Initialize the pipeline
pipe = pipeline(
    "automatic-speech-recognition", 
    model=model_id, 
    device="cuda" if torch.cuda.is_available() else "cpu",
    torch_dtype=torch.float16
)

# Transcribe an audio file
# Ensure you specify the language code ('indonesian' or 'english') for better accuracy

# Example for Indonesian audio:
result = pipe("path_to_your_indonesian_audio.mp3", generate_kwargs={"language": "indonesian"})
print(result["text"])

# Example for English audio:
result_en = pipe("path_to_your_english_audio.mp3", generate_kwargs={"language": "english"})
print(result_en["text"])