faster-whisper-small-tr

Faster Whisper Small Turkish (INT8 Quantized)

This model is an optimized CTranslate2 (INT8) conversion of the robust Turkish ASR model ogulcanakca/whisper-small-tr.

Performance Benchmarks

The model was benchmarked against the original Hugging Face Transformers implementation on an NVIDIA A100 GPU.

Note: Benchmarks were conducted on a standard Common Voice audio sample.

Usage

To use this model, you need the faster-whisper library.

pip install faster-whisper

from faster_whisper import WhisperModel

model_id = "ogulcanakca/faster-whisper-small-tr"

# Run on GPU with INT8
model = WhisperModel(model_id, device="cuda", compute_type="int8")

# or Run on CPU with INT8 (High performance on CPU too!)
# model = WhisperModel(model_id, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio.mp3", beam_size=5, language="tr")

print(f"Detected language '{info.language}' with probability {info.language_probability}")

for segment in segments:
    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")

Model Details

• Base Model: ogulcanakca/whisper-small-tr (Fine-tuned on Common Voice 23.0 with JIT Augmentation)
• Quantization: 8-bit Integer (INT8)
• Backend: CTranslate2
• Objective: Low-latency real-time streaming and high-throughput batch processing.