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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from tqdm import tqdm
from utils.util import pad_mels_to_tensors, pad_f0_to_tensors
def vocoder_inference(cfg, model, mels, f0s=None, device=None, fast_inference=False):
"""Inference the vocoder
Args:
mels: A tensor of mel-specs with the shape (batch_size, num_mels, frames)
Returns:
audios: A tensor of audios with the shape (batch_size, seq_len)
"""
model.eval()
with torch.no_grad():
training_noise_schedule = np.array(cfg.model.diffwave.noise_schedule)
inference_noise_schedule = (
np.array(cfg.model.diffwave.inference_noise_schedule)
if fast_inference
else np.array(cfg.model.diffwave.noise_schedule)
)
talpha = 1 - training_noise_schedule
talpha_cum = np.cumprod(talpha)
beta = inference_noise_schedule
alpha = 1 - beta
alpha_cum = np.cumprod(alpha)
T = []
for s in range(len(inference_noise_schedule)):
for t in range(len(training_noise_schedule) - 1):
if talpha_cum[t + 1] <= alpha_cum[s] <= talpha_cum[t]:
twiddle = (talpha_cum[t] ** 0.5 - alpha_cum[s] ** 0.5) / (
talpha_cum[t] ** 0.5 - talpha_cum[t + 1] ** 0.5
)
T.append(t + twiddle)
break
T = np.array(T, dtype=np.float32)
mels = mels.to(device)
audio = torch.randn(
mels.shape[0],
cfg.preprocess.hop_size * mels.shape[-1],
device=device,
)
for n in tqdm(range(len(alpha) - 1, -1, -1)):
c1 = 1 / alpha[n] ** 0.5
c2 = beta[n] / (1 - alpha_cum[n]) ** 0.5
audio = c1 * (
audio
- c2
* model(audio, torch.tensor([T[n]], device=audio.device), mels).squeeze(
1
)
)
if n > 0:
noise = torch.randn_like(audio)
sigma = (
(1.0 - alpha_cum[n - 1]) / (1.0 - alpha_cum[n]) * beta[n]
) ** 0.5
audio += sigma * noise
audio = torch.clamp(audio, -1.0, 1.0)
return audio.detach().cpu()
def synthesis_audios(cfg, model, mels, f0s=None, batch_size=None, fast_inference=False):
"""Inference the vocoder
Args:
mels: A list of mel-specs
Returns:
audios: A list of audios
"""
# Get the device
device = next(model.parameters()).device
audios = []
# Pad the given list into tensors
mel_batches, mel_frames = pad_mels_to_tensors(mels, batch_size)
if f0s != None:
f0_batches = pad_f0_to_tensors(f0s, batch_size)
if f0s == None:
for mel_batch, mel_frame in zip(mel_batches, mel_frames):
for i in range(mel_batch.shape[0]):
mel = mel_batch[i]
frame = mel_frame[i]
audio = vocoder_inference(
cfg,
model,
mel.unsqueeze(0),
device=device,
fast_inference=fast_inference,
).squeeze(0)
# calculate the audio length
audio_length = frame * cfg.preprocess.hop_size
audio = audio[:audio_length]
audios.append(audio)
else:
for mel_batch, f0_batch, mel_frame in zip(mel_batches, f0_batches, mel_frames):
for i in range(mel_batch.shape[0]):
mel = mel_batch[i]
f0 = f0_batch[i]
frame = mel_frame[i]
audio = vocoder_inference(
cfg,
model,
mel.unsqueeze(0),
f0s=f0.unsqueeze(0),
device=device,
fast_inference=fast_inference,
).squeeze(0)
# calculate the audio length
audio_length = frame * cfg.preprocess.hop_size
audio = audio[:audio_length]
audios.append(audio)
return audios
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