44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
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import argparse
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from ruamel import yaml
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import numpy as np
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import librosa
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import paddle
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from paddle import fluid
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from paddle.fluid import layers as F
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from paddle.fluid import dygraph as dg
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from parakeet.utils.io import load_parameters
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from parakeet.models.waveflow.waveflow_modules import WaveFlowModule
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class WaveflowVocoder(object):
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def __init__(self):
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config_path = "waveflow_res128_ljspeech_ckpt_1.0/waveflow_ljspeech.yaml"
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with open(config_path, 'rt') as f:
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config = yaml.safe_load(f)
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ns = argparse.Namespace()
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for k, v in config.items():
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setattr(ns, k, v)
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ns.use_fp16 = False
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self.model = WaveFlowModule(ns)
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checkpoint_path = "waveflow_res128_ljspeech_ckpt_1.0/step-2000000"
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load_parameters(self.model, checkpoint_path=checkpoint_path)
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def __call__(self, mel):
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with dg.no_grad():
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self.model.eval()
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audio = self.model.synthesize(mel)
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self.model.train()
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return audio
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class GriffinLimVocoder(object):
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def __init__(self, sharpening_factor=1.4, win_length=1024, hop_length=256):
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self.sharpening_factor = sharpening_factor
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self.win_length = win_length
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self.hop_length = hop_length
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def __call__(self, spec):
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audio = librosa.core.griffinlim(np.exp(spec * self.sharpening_factor),
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win_length=self.win_length, hop_length=self.hop_length)
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return audio
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