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  whisper-th-small-ct2 is the CTranslate2 format of [biodatlab/whisper-th-small-combined](https://huggingface.co/biodatlab/whisper-th-small-combined), comparable with [WhisperX](https://github.com/m-bain/whisperX) and [faster-whisper](https://github.com/SYSTRAN/faster-whisper), which enables:
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  - 🤏 **Half the size** of original Huggingface format.
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- - ⚡️ Batched inference for **70x** real-time transcription Whisper large-v2.
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- - 🪶 A faster-whisper backend, requiring **<8GB GPU memory** for large-v2 with beam_size=5.
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  - 🎯 Accurate word-level timestamps using wav2vec2 alignment.
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  - 👯‍♂️ Multispeaker ASR using speaker diarization(includes speaker ID labels).
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  - 🗣️ VAD preprocessing, reducing hallucinations and allowing batching with no WER degradation.
 
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  whisper-th-small-ct2 is the CTranslate2 format of [biodatlab/whisper-th-small-combined](https://huggingface.co/biodatlab/whisper-th-small-combined), comparable with [WhisperX](https://github.com/m-bain/whisperX) and [faster-whisper](https://github.com/SYSTRAN/faster-whisper), which enables:
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  - 🤏 **Half the size** of original Huggingface format.
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+ - ⚡️ Batched inference for **70x** real-time transcription.
17
+ - 🪶 A faster-whisper backend, requiring **<8GB GPU memory** with beam_size=5.
18
  - 🎯 Accurate word-level timestamps using wav2vec2 alignment.
19
  - 👯‍♂️ Multispeaker ASR using speaker diarization(includes speaker ID labels).
20
  - 🗣️ VAD preprocessing, reducing hallucinations and allowing batching with no WER degradation.