rename doc folder
This commit is contained in:
parent
4fde5c7e64
commit
8ba7eeb1da
|
@ -34,7 +34,7 @@ The code below show how to use a transformer_tts model. After loading the pretra
|
|||
>>> model = TransformerTTS.from_pretrained(
|
||||
>>> frontend, config, checkpoint_path)
|
||||
>>> model.eval()
|
||||
|
||||
>>>
|
||||
>>> # text to spectrogram
|
||||
>>> 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"
|
||||
>>> outputs = model.predict(sentence, verbose=args.verbose)
|
||||
|
@ -47,14 +47,14 @@ Like the example above, after loading the pretrained ConditionalWaveFlow model,
|
|||
|
||||
>>> import soundfile as df
|
||||
>>> from parakeet.models import ConditionalWaveFlow
|
||||
|
||||
>>>
|
||||
>>> # load the pretrained model
|
||||
>>> checkpoint_dir = Path("waveflow_pretrained")
|
||||
>>> config = yacs.config.CfgNode.load_cfg(str(checkpoint_dir / "config.yaml"))
|
||||
>>> checkpoint_path = str(checkpoint_dir / "step-2000000")
|
||||
>>> vocoder = ConditionalWaveFlow.from_pretrained(config, checkpoint_path)
|
||||
>>> vocoder.eval()
|
||||
|
||||
>>>
|
||||
>>> # synthesize
|
||||
>>> audio = vocoder.predict(mel_output)
|
||||
>>> sf.write(audio_path, audio, config.data.sample_rate)
|
Loading…
Reference in New Issue