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--- |
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language: |
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- zh |
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base_model: |
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- JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- audio |
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- automatic-speech-recognition |
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library_name: ctranslate2 |
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--- |
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# asadfgglie/faster-whisper-large-v3-zh-TW |
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此模型是將[JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW](https://huggingface.co/JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW) |
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轉換成`CTranslate2`格式的模型，可以在[faster-whisper](https://github.com/systran/faster-whisper)中使用。 |
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## Example |
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```python |
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from faster_whisper import WhisperModel |
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model = WhisperModel("asadfgglie/faster-whisper-large-v3-zh-TW") |
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segments, info = model.transcribe("audio.mp3") |
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for segment in segments: |
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print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) |
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``` |
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## Conversion details |
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原始模型是根據以下指令轉換: |
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``` |
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ct2-transformers-converter --output_dir faster-whisper-large-v3-zh-TW \ |
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--model JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW \ |
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--copy_files preprocessor_config.json |
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``` |
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在轉換完成後，請記得自行到原始模型的[model card](https://huggingface.co/openai/whisper-large-v3)中下載`tokenizer.json`。 |
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(因為`JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW`的repo中沒有，而`faster_whishper`又需要這個酷東東來做tokenizer) |
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如果有需要，你可以在轉換指令中添加`--quantization float16`來指定量化精度。不過在推理時你依舊可以使用[`compute_type`](https://opennmt.net/CTranslate2/quantization.html)參數來進一步量化/去除量化。 |
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