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@ -6,8 +6,7 @@ Wavenet On Mel Spectrogram Predictions](https://arxiv.org/pdf/1712.05884.pdf). |
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This implementation includes **distributed** and **fp16** support |
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and uses the [LJSpeech dataset](https://keithito.com/LJ-Speech-Dataset/). |
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Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's |
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[Apex Library](https://github.com/nvidia/apex). |
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Distributed and FP16 support uses NVIDIA's [Apex] and [AMP]. |
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Visit our [website] for audio samples using our published [Tacotron 2] and |
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[WaveGlow] models. |
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@ -26,7 +25,8 @@ Visit our [website] for audio samples using our published [Tacotron 2] and |
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5. Update .wav paths: `sed -i -- 's,DUMMY,ljs_dataset_folder/wavs,g' filelists/*.txt` |
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- Alternatively, set `load_mel_from_disk=True` in `hparams.py` and update mel-spectrogram paths |
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6. Install [PyTorch 1.0] |
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7. Install python requirements or build docker image |
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7. Install [Apex] |
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8. Install python requirements or build docker image |
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- Install python requirements: `pip install -r requirements.txt` |
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## Training |
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@ -77,3 +77,5 @@ Wang and Zongheng Yang. |
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[pytorch 1.0]: https://github.com/pytorch/pytorch#installation |
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[website]: https://nv-adlr.github.io/WaveGlow |
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[ignored]: https://github.com/NVIDIA/tacotron2/blob/master/hparams.py#L22 |
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[Apex]: https://github.com/nvidia/apex |
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[AMP]: https://github.com/NVIDIA/apex/tree/master/apex/amp |