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--- |
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license: apache-2.0 |
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language: |
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- pl |
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pipeline_tag: automatic-speech-recognition |
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tags: |
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- audio |
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datasets: |
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- Aspik101/distil-whisper-large-v3-pl |
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library_name: ctranslate2 |
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--- |
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<style> |
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img { |
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display: inline; |
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} |
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</style> |
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# Fine-tuned Polish Aspik101/distil-whisper-large-v3-pl model for CTranslate2 |
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This repository contains the [Aspik101/distil-whisper-large-v3-pl](https://huggingface.co/Aspik101/distil-whisper-large-v3-pl) model converted to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) format. |
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## Usage |
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```python |
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from faster_whisper import WhisperModel |
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from huggingface_hub import snapshot_download |
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downloaded_model_path = snapshot_download(repo_id="mmalyska/distil-whisper-large-v3-pl-ct2") |
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# Run on GPU with FP16 |
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model = WhisperModel(downloaded_model_path, device="cuda", compute_type="float16") |
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# or run on GPU with INT8 |
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# model = WhisperModel(downloaded_model_path, device="cuda", compute_type="int8_float16") |
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# or run on CPU with INT8 |
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# model = WhisperModel(downloaded_model_path, device="cpu", compute_type="int8") |
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segments, info = model.transcribe("./sample.wav", beam_size=1) |
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print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) |
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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 |
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The original model was converted with the following command: |
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```bash |
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ct2-transformers-converter --model Aspik101/distil-whisper-large-v3-pl --output_dir distil-whisper-large-v3-pl-ct2 --copy_files tokenizer.json preprocessor_config.json --quantization float16 |
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``` |
