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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)
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()
text = "पाइथन में प्रोग्रामिंग, डेटा स्ट्रक्चर्स और एल्गोरिदम पर पाठ्यक्रम पर पहले व्याख्यान में, आपका स्वागत है।"
tmp_dir="tmp"
lang = "Hindi"
timestamp = "1"
preprocess_start = time.time()
# create tmp directory
os.makedirs(tmp_dir, exist_ok = True)
textfile_inp = os.path.abspath(f"{tmp_dir}/input.txt")
textfile = os.path.abspath(f"{tmp_dir}/input_preprocessed.txt")
with open(textfile_inp, "w") as f:
f.write(text)
# preprocess the text
command = f"/var/www/html/IITM_TTS/E2E_TTS_FS2/text_proc/text_proc.sh {textfile_inp} {textfile} {lang} {timestamp} {tmp_dir}"
os.system(command)
# textfile_oneline = os.path.abspath(f"{tmp_dir}/input_preprocessed_oneline.txt")
# command = """awk 'BEGIN{ORS=" "}{for (i=2; i<NF; i++) printf $i " "; print $NF}' """ + textfile + " > " + textfile_oneline
# os.system(command)
preprocessed_text = []
with open(textfile, "r") as f:
for line in f.readlines():
preprocessed_text.append(line.split(" ", 1)[1].strip())
preprocess_end = time.time()
t2s_start = preprocess_end
out = text2speech(" ".join(preprocessed_text))
t2s_end = time.time()
vocoder_start = t2s_end
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)
vocoder_end = time.time()
et = vocoder_end
elapsed = (et-st)
print(f"Total elapsed time: {elapsed}\nText Preprocess: {(preprocess_end-preprocess_start)}\nText-to-mel: {(t2s_end-t2s_start)}\nMel to wave: {(vocoder_end-vocoder_start)}")
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