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README.md
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datasets:
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- Cnam-LMSSC/vibravox
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# Model Card
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- **Developed by:** [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC)
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from vibravox import EBENGenerator
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from datasets import load_dataset
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cut_audio_16kHz = model.cut_to_valid_length(audio_16kHz)
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enhanced_audio_16kHz = model(cut_audio_16kHz)
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datasets:
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- Cnam-LMSSC/vibravox
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---
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/65302a613ecbe51d6a6ddcec/zhB1fh-c0pjlj-Tr4Vpmr.png" style="object-fit:contain; width:280px; height:280px;" >
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</p>
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# Model Card
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- **Developed by:** [Cnam-LMSSC](https://huggingface.co/Cnam-LMSSC)
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from vibravox import EBENGenerator
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from datasets import load_dataset
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model = EBENGenerator.from_pretrained("Cnam-LMSSC/EBEN_throat_microphone")
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test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True)
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audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.throat_microphone"]["array"])
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audio_16kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=16_000)
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cut_audio_16kHz = model.cut_to_valid_length(audio_16kHz)
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enhanced_audio_16kHz = model(cut_audio_16kHz)
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