inference.py

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import os
from scipy.io.wavfile import write
import torch
from waveglow.mel2samp import files_to_list, MAX_WAV_VALUE
# from denoiser import Denoiser


def inference(mel, waveglow, audio_path=None, sigma=1.0, sampling_rate=22050):
    with torch.no_grad():
        audio = waveglow.infer(mel, sigma=sigma)
        audio = audio * MAX_WAV_VALUE
    audio = audio.squeeze()
    audio = audio.cpu().numpy()
    audio = audio.astype('int16')
    write(audio_path, sampling_rate, audio)


def test_speed(mel, waveglow, sigma=1.0, sampling_rate=22050):
    with torch.no_grad():
        audio = waveglow.infer(mel, sigma=sigma)
        audio = audio * MAX_WAV_VALUE


def get_wav(mel, waveglow, sigma=1.0, sampling_rate=22050):
    with torch.no_grad():
        audio = waveglow.infer(mel, sigma=sigma)
        audio = audio * MAX_WAV_VALUE
    audio = audio.squeeze()
    audio = audio.cpu()

    return audio