Whisper small model for CTranslate2

PhanithLIM/whisper-base-aug-20-april-lightning-v1 is a fine-tuned version of OpenAI's Whisper ASR model adapted specifically for the Khmer language. Built on the small variant of Whisper and optimized using FasterWhisper, this model provides efficient and accurate speech-to-text transcription for Khmer audio.

🧠 Model Details

Base Model: Whisper Base
Framework: FasterWhisper
Language: Khmer (Central Khmer)
Use Case: Real-time and batch audio transcription in Khmer
Optimization: Lightweight model for low-latency inference

🚀 Installation

pip install faster-whisper

📦 Usage

from faster_whisper import WhisperModel

# Load the model
model = WhisperModel("PhanithLIM/whisper-base-khmer-ct2", compute_type="int8", local_files_only=False, beam_size=5)

# Transcribe Khmer audio
segments, info = model.transcribe("your_audio_file.wav")

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

🔧 Real-Time Transcription

This model can be integrated into real-time systems using tools such as:

FastAPI
FastRTC (WebRTC wrapper for real-time audio streaming)
Gradio (for demo UI)

CTranslate2

CTranslate2 is a fast inference engine for transformer models, optimized for CPU and GPU deployment, especially in production environments. It's developed by the team behind OpenNMT, and it's widely used in speech and machine translation systems, including FasterWhisper, which is a CTranslate2 port of OpenAI’s Whisper.