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Update README.md

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@@ -51,6 +51,19 @@ model = HifiGanModel.restore_from(restore_path=path)
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  ```
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  import soundfile as sf
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  parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
 
 
 
 
 
 
 
 
 
 
 
 
 
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  spectrogram = spec_generator.generate_spectrogram(tokens=parsed,speaker=92)
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  audio = model.convert_spectrogram_to_audio(spec=spectrogram)
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  sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100)
@@ -75,7 +88,7 @@ FastPitch multispeaker is a fully-parallel text-to-speech model based on FastSpe
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  ## Training
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- The NeMo toolkit [3] was used for training the models for 1000 epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/fastpitch.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/fastpitch_align_v1.05.yaml).
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  ## Datasets
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  ```
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  import soundfile as sf
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  parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
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+ """
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+ speaker id:
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+ 92 Cori Samuel
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+ 6097 Phil Benson
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+ 9017 John Van Stan
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+ 6670 Mike Pelton
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+ 6671 Tony Oliva
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+ 8051 Maria Kasper
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+ 9136 Helen Taylor
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+ 11614 Sylviamb
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+ 11697 Celine Major
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+ 12787 LikeManyWaters
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+ """
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  spectrogram = spec_generator.generate_spectrogram(tokens=parsed,speaker=92)
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  audio = model.convert_spectrogram_to_audio(spec=spectrogram)
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  sf.write("speech.wav", audio.to('cpu').detach().numpy()[0], 44100)
 
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  ## Training
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+ The NeMo toolkit [3] was used for training the models for 1000 epochs.
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  ## Datasets
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