update urls
Browse files
app.py
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@@ -216,7 +216,7 @@ css = """
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######################## script for loading the models ########################
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model_path = "
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list_model_name = [
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"bigvgan_24khz_100band",
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@@ -301,7 +301,7 @@ with iface:
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<li>Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.</li>
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<li>Improved discriminator and loss: BigVGAN-v2 is trained using a <a href="https://arxiv.org/abs/2311.14957" target="_blank">multi-scale sub-band CQT discriminator</a> and a <a href="https://arxiv.org/abs/2306.06546" target="_blank">multi-scale mel spectrogram loss</a>.</li>
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<li>Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.</li>
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<li>We provide <a href="https://huggingface.co/
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</ul>
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</div>
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"""
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######################## script for loading the models ########################
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model_path = "nvidia/BigVGAN"
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list_model_name = [
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"bigvgan_24khz_100band",
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<li>Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.</li>
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<li>Improved discriminator and loss: BigVGAN-v2 is trained using a <a href="https://arxiv.org/abs/2311.14957" target="_blank">multi-scale sub-band CQT discriminator</a> and a <a href="https://arxiv.org/abs/2306.06546" target="_blank">multi-scale mel spectrogram loss</a>.</li>
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<li>Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.</li>
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<li>We provide <a href="https://huggingface.co/nvidia/BigVGAN" target="_blank">pretrained checkpoints</a> of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.</li>
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</ul>
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</div>
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"""
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