mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import os
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import json
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import torch
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from scipy.io.wavfile import write
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from vocoder.hifigan.env import AttrDict
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from vocoder.hifigan.meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
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from vocoder.hifigan.models import Generator
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import soundfile as sf
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generator = None # type: Generator
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_device = None
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def load_checkpoint(filepath, device):
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assert os.path.isfile(filepath)
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print("Loading '{}'".format(filepath))
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checkpoint_dict = torch.load(filepath, map_location=device)
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print("Complete.")
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return checkpoint_dict
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def load_model(weights_fpath, verbose=True):
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global generator, _device
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if verbose:
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print("Building hifigan")
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with open("./vocoder/hifigan/config_16k_.json") as f:
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data = f.read()
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json_config = json.loads(data)
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h = AttrDict(json_config)
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torch.manual_seed(h.seed)
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if torch.cuda.is_available():
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# _model = _model.cuda()
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_device = torch.device('cuda')
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else:
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_device = torch.device('cpu')
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generator = Generator(h).to(_device)
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state_dict_g = load_checkpoint(
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weights_fpath, _device
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)
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generator.load_state_dict(state_dict_g['generator'])
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generator.eval()
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generator.remove_weight_norm()
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def is_loaded():
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return generator is not None
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def infer_waveform(mel, progress_callback=None):
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if generator is None:
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raise Exception("Please load hifi-gan in memory before using it")
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mel = torch.FloatTensor(mel).to(_device)
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mel = mel.unsqueeze(0)
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with torch.no_grad():
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y_g_hat = generator(mel)
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audio = y_g_hat.squeeze()
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audio = audio.cpu().numpy()
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return audio
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