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import logging
import time
from functools import partial
import torch
import torch.nn.functional as F
from torch.nn.utils.parametrize import remove_parametrizations
from torchaudio.functional import resample
from torchaudio.transforms import MelSpectrogram
from modules import config
from modules.devices import devices
from .hparams import HParams
logger = logging.getLogger(__name__)
@torch.inference_mode()
def inference_chunk(
model,
dwav: torch.Tensor,
sr: int,
device: torch.device,
dtype: torch.dtype,
npad=441,
) -> torch.Tensor:
assert model.hp.wav_rate == sr, f"Expected {model.hp.wav_rate} Hz, got {sr} Hz"
del sr
length = dwav.shape[-1]
abs_max = dwav.abs().max().clamp(min=1e-7)
assert dwav.dim() == 1, f"Expected 1D waveform, got {dwav.dim()}D"
dwav = dwav.to(device=device, dtype=dtype)
dwav = dwav / abs_max # Normalize
dwav = F.pad(dwav, (0, npad))
hwav: torch.Tensor = model(dwav[None])[0].cpu() # (T,)
hwav = hwav[:length] # Trim padding
hwav = hwav * abs_max # Unnormalize
return hwav
def compute_corr(x, y):
return torch.fft.ifft(torch.fft.fft(x) * torch.fft.fft(y).conj()).abs()
def compute_offset(chunk1, chunk2, sr=44100):
"""
Args:
chunk1: (T,)
chunk2: (T,)
Returns:
offset: int, offset in samples such that chunk1 ~= chunk2.roll(-offset)
"""
hop_length = sr // 200 # 5 ms resolution
win_length = hop_length * 4
n_fft = 2 ** (win_length - 1).bit_length()
mel_fn = MelSpectrogram(
sample_rate=sr,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
n_mels=80,
f_min=0.0,
f_max=sr // 2,
)
chunk1 = chunk1.float()
chunk2 = chunk2.float()
spec1 = mel_fn(chunk1).log1p()
spec2 = mel_fn(chunk2).log1p()
corr = compute_corr(spec1, spec2) # (F, T)
corr = corr.mean(dim=0) # (T,)
argmax = corr.argmax().item()
if argmax > len(corr) // 2:
argmax -= len(corr)
offset = -argmax * hop_length
return offset
def merge_chunks(chunks, chunk_length, hop_length, sr=44100, length=None):
signal_length = (len(chunks) - 1) * hop_length + chunk_length
overlap_length = chunk_length - hop_length
signal = torch.zeros(signal_length, device=chunks[0].device)
fadein = torch.linspace(0, 1, overlap_length, device=chunks[0].device)
fadein = torch.cat([fadein, torch.ones(hop_length, device=chunks[0].device)])
fadeout = torch.linspace(1, 0, overlap_length, device=chunks[0].device)
fadeout = torch.cat([torch.ones(hop_length, device=chunks[0].device), fadeout])
for i, chunk in enumerate(chunks):
start = i * hop_length
end = start + chunk_length
if len(chunk) < chunk_length:
chunk = F.pad(chunk, (0, chunk_length - len(chunk)))
if i > 0:
pre_region = chunks[i - 1][-overlap_length:]
cur_region = chunk[:overlap_length]
offset = compute_offset(pre_region, cur_region, sr=sr)
start -= offset
end -= offset
if i == 0:
chunk = chunk * fadeout
elif i == len(chunks) - 1:
chunk = chunk * fadein
else:
chunk = chunk * fadein * fadeout
signal[start:end] += chunk[: len(signal[start:end])]
signal = signal[:length]
return signal
def remove_weight_norm_recursively(module):
for _, module in module.named_modules():
try:
remove_parametrizations(module, "weight")
except Exception:
pass
def inference(
model,
dwav,
sr,
device,
dtype,
chunk_seconds: float = 30.0,
overlap_seconds: float = 1.0,
):
from tqdm import trange
if config.runtime_env_vars.off_tqdm:
trange = partial(trange, disable=True)
remove_weight_norm_recursively(model)
hp: HParams = model.hp
dwav = resample(
dwav,
orig_freq=sr,
new_freq=hp.wav_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method="sinc_interp_kaiser",
beta=14.769656459379492,
)
del sr # Everything is in hp.wav_rate now
sr = hp.wav_rate
if torch.cuda.is_available():
torch.cuda.synchronize()
start_time = time.perf_counter()
chunk_length = int(sr * chunk_seconds)
overlap_length = int(sr * overlap_seconds)
hop_length = chunk_length - overlap_length
chunks = []
for start in trange(0, dwav.shape[-1], hop_length):
chunk_dwav = inference_chunk(
model, dwav[start : start + chunk_length], sr, device, dtype
)
chunks.append(chunk_dwav.cpu())
devices.torch_gc()
hwav = merge_chunks(chunks, chunk_length, hop_length, sr=sr, length=dwav.shape[-1])
if torch.cuda.is_available():
torch.cuda.synchronize()
elapsed_time = time.perf_counter() - start_time
logger.debug(
f"Elapsed time: {elapsed_time:.3f} s, {hwav.shape[-1] / elapsed_time / 1000:.3f} kHz"
)
devices.torch_gc()
return hwav, sr
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