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58 lines
1.3 KiB
Python
58 lines
1.3 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import glob
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import os
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import argparse
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import json
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import torch
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import numpy as np
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from scipy.io.wavfile import write
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from env import AttrDict
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from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
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from models import Generator
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import soundfile as sf
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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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h = None
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device = None
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with open("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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device = torch.device("cpu")
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generator = Generator(h).to(device)
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state_dict_g = load_checkpoint("../../../TTS/Vocoder/outputs/hifi-gan/models/g_00930000", device)
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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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mel = np.load("./mel-T0055G0184S0349.wav_00.npy")
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mel = torch.FloatTensor(mel.T).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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sf.write("a.wav", audio, samplerate=16000)
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# import IPython.display as ipd
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# ipd.Audio(audio, rate=16000) |