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metadata
language:
  - km
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Khmer Whisper Small PhanithLIM
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Google FLEURS
          type: google/fleurs
          config: km_kh
          split: test
        metrics:
          - name: CER
            type: wer
            value: 8.9
library_name: transformers
pipeline_tag: automatic-speech-recognition
base_model:
  - openai/whisper-small

Whisper small model for CTranslate2

PhanithLIM/whisper-small-aug-28-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

πŸš€ Installation

pip install faster-whisper

πŸ“¦ Usage

from faster_whisper import WhisperModel

# Load the model
model = WhisperModel("PhanithLIM/whisper-small-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}")

πŸ”§ 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.