Fork of https://github.com/alokprasad/fastspeech_squeezewave to also fix denoising in squeezewave
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93 lines
2.8 KiB
93 lines
2.8 KiB
import torch
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import torch.nn as nn
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import time
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import os
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from fastspeech import FastSpeech
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from text import text_to_sequence
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import hparams as hp
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import utils
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import audio as Audio
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import glow
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import waveglow
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import time
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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def get_FastSpeech(num):
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checkpoint_path = "checkpoint_" + str(num) + ".pth.tar"
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model = nn.DataParallel(FastSpeech()).to(device)
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model.load_state_dict(torch.load(os.path.join(
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hp.checkpoint_path, checkpoint_path),map_location=device)['model'])
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model.eval()
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return model
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def synthesis(model, text, alpha=1.0):
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text = np.array(text_to_sequence(text, hp.text_cleaners))
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text = np.stack([text])
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src_pos = np.array([i+1 for i in range(text.shape[1])])
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src_pos = np.stack([src_pos])
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with torch.no_grad():
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sequence = torch.autograd.Variable(
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torch.from_numpy(text)).long()
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src_pos = torch.autograd.Variable(
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torch.from_numpy(src_pos)).long()
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mel, mel_postnet = model.module.forward(sequence, src_pos, alpha=alpha)
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#script for generating torch script
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#traced_script_module = torch.jit.trace(model,(sequence,src_pos))
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#traced_script_module.save("traced_fastspeech_model.pt")
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return mel[0].cpu().transpose(0, 1), \
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mel_postnet[0].cpu().transpose(0, 1), \
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mel.transpose(1, 2), \
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mel_postnet.transpose(1, 2)
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if __name__ == "__main__":
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# Test
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num = 112000
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alpha = 1.0
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model = get_FastSpeech(num)
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words = "Let’s go out to the airport. The plane landed ten minutes ago."
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start = time.time()
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mel, mel_postnet, mel_torch, mel_postnet_torch = synthesis(
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model, words, alpha=alpha)
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if not os.path.exists("results"):
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os.mkdir("results")
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#do not use any vocoder , mel file generated will be passed to squeezewave vocoder.
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"""
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Audio.tools.inv_mel_spec(mel_postnet, os.path.join(
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"results", words + "_" + str(num) + "_griffin_lim.wav"))
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wave_glow = utils.get_WaveGlow()
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waveglow.inference.inference(mel_postnet_torch, wave_glow, os.path.join(
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"results", words + "_" + str(num) + "_waveglow.wav"))
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tacotron2 = utils.get_Tacotron2()
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mel_tac2, _, _ = utils.load_data_from_tacotron2(words, tacotron2)
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waveglow.inference.inference(torch.stack([torch.from_numpy(
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mel_tac2).cuda()]), wave_glow, os.path.join("results", "tacotron2.wav"))
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utils.plot_data([mel.numpy(), mel_postnet.numpy(), mel_tac2])
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"""
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#melspec = torch.squeeze(mel_postnet_torch, 0)
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torch.save(mel_postnet_torch, "../SqueezeWave/mel_spectrograms/test.pt")
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end = time.time()
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print("MEL Calculation:")
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print(end-start)
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