rename doc folder
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@ -34,7 +34,7 @@ The code below show how to use a transformer_tts model. After loading the pretra
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>>> model = TransformerTTS.from_pretrained(
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>>> model = TransformerTTS.from_pretrained(
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>>> frontend, config, checkpoint_path)
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>>> frontend, config, checkpoint_path)
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>>> model.eval()
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>>> model.eval()
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>>>
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>>> # text to spectrogram
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>>> # text to spectrogram
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>>> sentence = "Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition"
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>>> sentence = "Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition"
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>>> outputs = model.predict(sentence, verbose=args.verbose)
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>>> outputs = model.predict(sentence, verbose=args.verbose)
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@ -47,14 +47,14 @@ Like the example above, after loading the pretrained ConditionalWaveFlow model,
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>>> import soundfile as df
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>>> import soundfile as df
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>>> from parakeet.models import ConditionalWaveFlow
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>>> from parakeet.models import ConditionalWaveFlow
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>>>
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>>> # load the pretrained model
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>>> # load the pretrained model
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>>> checkpoint_dir = Path("waveflow_pretrained")
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>>> checkpoint_dir = Path("waveflow_pretrained")
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>>> config = yacs.config.CfgNode.load_cfg(str(checkpoint_dir / "config.yaml"))
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>>> config = yacs.config.CfgNode.load_cfg(str(checkpoint_dir / "config.yaml"))
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>>> checkpoint_path = str(checkpoint_dir / "step-2000000")
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>>> checkpoint_path = str(checkpoint_dir / "step-2000000")
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>>> vocoder = ConditionalWaveFlow.from_pretrained(config, checkpoint_path)
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>>> vocoder = ConditionalWaveFlow.from_pretrained(config, checkpoint_path)
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>>> vocoder.eval()
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>>> vocoder.eval()
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>>>
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>>> # synthesize
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>>> # synthesize
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>>> audio = vocoder.predict(mel_output)
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>>> audio = vocoder.predict(mel_output)
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>>> sf.write(audio_path, audio, config.data.sample_rate)
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>>> sf.write(audio_path, audio, config.data.sample_rate)
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