Spaces:
Sleeping
Sleeping
File size: 1,792 Bytes
bfcd0c0 080259f bfcd0c0 080259f cb82d78 080259f cb82d78 4d5330a 848f2f7 f95a0dc 848f2f7 f95a0dc 848f2f7 cb82d78 f95a0dc cb82d78 c472fbf f95a0dc c472fbf cb82d78 848f2f7 cb82d78 c472fbf 848f2f7 c472fbf 848f2f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import gradio as gr
from hyper_parameters import tacotron_params as hparams
from training import load_model
from text import text_to_sequence
from melgan.model.generator import Generator
from melgan.utils.hparams import load_hparam
import torch
import numpy as np
torch.manual_seed(1234)
MAX_WAV_VALUE = 32768.0
def init_models(hparams):
# load trained tacotron2 + GST model:
model = load_model(hparams)
checkpoint_path = "trained_models/checkpoint_78000.model"
model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
# model.to('cuda')
_ = model.eval()
# load pre trained MelGAN model for mel2audio:
vocoder_checkpoint_path = "trained_models/nvidia_tacotron2_LJ11_epoch6400.pt"
checkpoint = torch.load(vocoder_checkpoint_path)
hp_melgan = load_hparam("melgan/config/default.yaml")
vocoder_model = Generator(80)
vocoder_model.load_state_dict(checkpoint['model_g'])
# vocoder_model = vocoder_model.to('cuda')
vocoder_model.eval(inference=False)
def synthesize(text):
sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
sequence = torch.from_numpy(sequence).to(device='cpu', dtype=torch.int64)
gst_head_scores = np.array([0.5, 0.15, 0.35]) # originally ([0.5, 0.15, 0.35])
gst_scores = torch.from_numpy(gst_head_scores).float()
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence, gst_scores)
# mel2wav inference:
with torch.no_grad():
audio = vocoder_model.inference(mel_outputs_postnet)
audio_numpy = audio.data.cpu().detach().numpy()
return (22050, audio_numpy)
init_models(hparams)
iface = gr.Interface(fn=synthesize, inputs="text", outputs=[gr.Audio(label="Generated Speech", type="numpy"),])
iface.launch()
|