metadata
language:
- km
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
library_name: transformers
pipeline_tag: automatic-speech-recognition
base_model:
- openai/whisper-tiny
Whisper small model for CTranslate2
PhanithLIM/whisper-tiny-aug-19-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 Small
- 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-tiny-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:
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.