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from models import Generator
from scipy.io.wavfile import write
from meldataset import MAX_WAV_VALUE
import numpy as np
import os
import json
from env import AttrDict
import torch
import time
from espnet2.bin.tts_inference import Text2Speech
for dev in ("cpu", "cuda"):
print(f"loading model in {dev}")
device=torch.device(dev)
y1 = torch.load("/speech/arun/tts/hifigan/denorm/test_243.npy.pt", map_location=device)
y2 = torch.concat([y1]*5, dim=1)
y3 = torch.concat([y1]*10, dim=1)
config_file = os.path.join('/speech/arun/tts/hifigan/cp_hifigan/config.json')
with open(config_file) as f:
data = f.read()
json_config = json.loads(data)
h = AttrDict(json_config)
torch.manual_seed(h.seed)
generator = Generator(h).to(device)
state_dict_g = torch.load("/speech/arun/tts/hifigan/cp_hifigan/g_00120000", device)
generator.load_state_dict(state_dict_g['generator'])
generator.eval()
generator.remove_weight_norm()
text2speech = Text2Speech(train_config="/speech/arun/tts/hifigan/config.yaml",model_file="/var/www/html/IITM_TTS/E2E_TTS_FS2/fastspeech2/models/Hindi_male/train.loss.ave.pth",device=dev)
for i in range(3):
print("Run ",i)
with torch.no_grad():
st = time.time()
out = text2speech("EटA sटarakcars औr Elgoridam par pAठyakram par pahlE wyAखyAn mEq")
x = out["feat_gen_denorm"].T.unsqueeze(0).to(device)
y_g_hat = generator(x)
audio = y_g_hat.squeeze()
audio = audio * MAX_WAV_VALUE
audio = audio.cpu().numpy().astype('int16')
output_file = "gen.wav"
write(output_file, h.sampling_rate, audio)
et = time.time()
elapsed = (et-st)
print("Elapsed time:", elapsed)
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