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Browse files- .ipynb_checkpoints/README-checkpoint.md +1 -1
- README.md +1 -1
    	
        .ipynb_checkpoints/README-checkpoint.md
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    | @@ -15,7 +15,7 @@ model = WhisperModel("distil-whisper/distil-large-v3.5-ct2", device=device, comp | |
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            dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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            sample = dataset[1]["audio"]["path"]
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            segments, info = model.transcribe(sample, beam_size= | 
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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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            dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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            sample = dataset[1]["audio"]["path"]
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            segments, info = model.transcribe(sample, beam_size=5, language="en")
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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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        README.md
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    | @@ -15,7 +15,7 @@ model = WhisperModel("distil-whisper/distil-large-v3.5-ct2", device=device, comp | |
| 15 | 
             
            dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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            sample = dataset[1]["audio"]["path"]
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            segments, info = model.transcribe(sample, beam_size= | 
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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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            dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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            sample = dataset[1]["audio"]["path"]
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            +
            segments, info = model.transcribe(sample, beam_size=5, language="en")
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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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