adding support for --cpu-run
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@ -340,10 +340,10 @@ and `--waveglow` arguments. Tacotron2 and WaveGlow checkpoints can also be downl
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You can also run inference on CPU with TorchScript by adding flag --cpu-run:
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```bash
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export CUDA_VISIBLE_DEVICES=
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export CUDA_VISIBLE_DEVICES=
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```
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```bash
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python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> --wn-channels 256 --cpu-run -o output/ -i phrases/phrase.txt
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python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> --wn-channels 256 --cpu-run -o output/ -i phrases/phrase.txt
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```
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## Advanced
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@ -385,7 +385,7 @@ WaveGlow models.
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* `--learning-rate` - learning rate (Tacotron 2: 1e-3, WaveGlow: 1e-4)
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* `--batch-size` - batch size (Tacotron 2 FP16/FP32: 104/48, WaveGlow FP16/FP32: 10/4)
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* `--amp-run` - use mixed precision training
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* `--cpu-run` - use CPU with TorchScript inference
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* `--cpu-run` - use CPU with TorchScript for inference
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#### Shared audio/STFT parameters
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@ -494,13 +494,6 @@ mixed precision and FP32 training, respectively.
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You can find all the available options by calling `python inference.py --help`.
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You can also run inference on CPU with TorchScript by adding flag --cpu-run:
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```export CUDA_VISIBLE_DEVICES=
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```
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```bash
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python inference.py --tacotron2 <Tacotron2_checkpoint> --waveglow <WaveGlow_checkpoint> --wn-channels 256 --cpu-run -o output/ -i phrases/phrase.txt
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## Performance
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### Benchmarking
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