| 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 |
|
|
| 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() |
| for i in range(3): |
| print("Run ",i) |
| for x in [y1, y2, y3]: |
| with torch.no_grad(): |
| st = time.time() |
| 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) |
|
|
