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import logging
from typing import List, Tuple
import pytorch_lightning as pl
from omegaconf import DictConfig
from pytorch_lightning.utilities import rank_zero_only
from frechet_audio_distance import FrechetAudioDistance
import numpy as np
import torch
import torchaudio
def get_logger(name=__name__) -> logging.Logger:
"""Initializes multi-GPU-friendly python command line logger."""
logger = logging.getLogger(name)
# this ensures all logging levels get marked with the rank zero decorator
# otherwise logs would get multiplied for each GPU process in multi-GPU setup
for level in (
"debug",
"info",
"warning",
"error",
"exception",
"fatal",
"critical",
):
setattr(logger, level, rank_zero_only(getattr(logger, level)))
return logger
log = get_logger(__name__)
@rank_zero_only
def log_hyperparameters(
config: DictConfig,
model: pl.LightningModule,
datamodule: pl.LightningDataModule,
trainer: pl.Trainer,
callbacks: List[pl.Callback],
logger: pl.loggers.logger.Logger,
) -> None:
"""Controls which config parts are saved by Lightning loggers.
Additionaly saves:
- number of model parameters
"""
if not trainer.logger:
return
hparams = {}
# choose which parts of hydra config will be saved to loggers
hparams["model"] = config["model"]
# save number of model parameters
hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
hparams["model/params/trainable"] = sum(
p.numel() for p in model.parameters() if p.requires_grad
)
hparams["model/params/non_trainable"] = sum(
p.numel() for p in model.parameters() if not p.requires_grad
)
hparams["datamodule"] = config["datamodule"]
hparams["trainer"] = config["trainer"]
if "seed" in config:
hparams["seed"] = config["seed"]
if "callbacks" in config:
hparams["callbacks"] = config["callbacks"]
logger.experiment.config.update(hparams)
class FADLoss(torch.nn.Module):
def __init__(self, sample_rate: float):
super().__init__()
self.fad = FrechetAudioDistance(
use_pca=False, use_activation=False, verbose=False
)
self.fad.model = self.fad.model.to("cpu")
self.sr = sample_rate
def forward(self, audio_background, audio_eval):
embds_background = []
embds_eval = []
for sample in audio_background:
embd = self.fad.model.forward(sample.T.cpu().detach().numpy(), self.sr)
embds_background.append(embd.cpu().detach().numpy())
for sample in audio_eval:
embd = self.fad.model.forward(sample.T.cpu().detach().numpy(), self.sr)
embds_eval.append(embd.cpu().detach().numpy())
embds_background = np.concatenate(embds_background, axis=0)
embds_eval = np.concatenate(embds_eval, axis=0)
mu_background, sigma_background = self.fad.calculate_embd_statistics(
embds_background
)
mu_eval, sigma_eval = self.fad.calculate_embd_statistics(embds_eval)
fad_score = self.fad.calculate_frechet_distance(
mu_background, sigma_background, mu_eval, sigma_eval
)
return fad_score
def create_random_chunks(
audio_file: str, chunk_size: int, num_chunks: int
) -> Tuple[List[Tuple[int, int]], int]:
"""Create num_chunks random chunks of size chunk_size (seconds)
from an audio file.
Return sample_index of start of each chunk and original sr
"""
audio, sr = torchaudio.load(audio_file)
chunk_size_in_samples = chunk_size * sr
if chunk_size_in_samples >= audio.shape[-1]:
chunk_size_in_samples = audio.shape[-1] - 1
chunks = []
for i in range(num_chunks):
start = torch.randint(0, audio.shape[-1] - chunk_size_in_samples, (1,)).item()
chunks.append(start)
return chunks, sr
def create_sequential_chunks(
audio_file: str, chunk_size: int
) -> Tuple[List[Tuple[int, int]], int]:
"""Create sequential chunks of size chunk_size (seconds) from an audio file.
Return sample_index of start of each chunk and original sr
"""
chunks = []
audio, sr = torchaudio.load(audio_file)
chunk_starts = torch.arange(0, audio.shape[-1], chunk_size)
for start in chunk_starts:
if start + chunk_size > audio.shape[-1]:
break
chunks.append(audio[:, start : start + chunk_size])
return chunks, sr
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