68 lines
2.3 KiB
ReStructuredText
68 lines
2.3 KiB
ReStructuredText
===========
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Tutorials
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===========
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Basic Usage
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-------------------
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Pretrained models are provided in a archive. Extract it to get a folder like this::
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checkpoint_name/
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├──config.yaml
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└──step-310000.pdparams
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The ``config.yaml`` stores the config used to train the model, the ``step-N.pdparams`` is the parameter file, where N is the steps it has been trained.
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The example code below shows how to use the models for prediction.
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text to spectrogram
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^^^^^^^^^^^^^^^^^^^^^^
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The code below show how to use a transformer_tts model. After loading the pretrained model, use ``model.predict(sentence)`` to generate spectrogram (in numpy.ndarray format), which can be further used to synthesize waveflow.
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>>> import parakeet
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>>> from parakeet.frontend import English
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>>> from parakeet.models import TransformerTTS
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>>> from pathlib import Path
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>>> import yacs
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>>>
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>>> # load the pretrained model
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>>> frontend = English()
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>>> checkpoint_dir = Path("transformer_tts_pretrained")
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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-310000")
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>>> model = TransformerTTS.from_pretrained(
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>>> frontend, config, checkpoint_path)
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>>> model.eval()
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>>>
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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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>>> outputs = model.predict(sentence, verbose=args.verbose)
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>>> mel_output = outputs["mel_output"]
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vocoder
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^^^^^^^^^^
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Like the example above, after loading the pretrained ConditionalWaveFlow model, call ``model.predict(mel)`` to synthesize waveflow (in numpy.ndarray format).
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>>> import soundfile as df
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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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>>> checkpoint_dir = Path("waveflow_pretrained")
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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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>>> vocoder = ConditionalWaveFlow.from_pretrained(config, checkpoint_path)
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>>> vocoder.eval()
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>>>
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>>> # synthesize
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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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For more details on how to use the model, please refer the documentation.
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