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import numpy as np
import torch
import torchaudio
from coqpit import Coqpit
from torch import nn
from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
from TTS.utils.generic_utils import set_init_dict
from TTS.utils.io import load_fsspec
class PreEmphasis(nn.Module):
def __init__(self, coefficient=0.97):
super().__init__()
self.coefficient = coefficient
self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
def forward(self, x):
assert len(x.size()) == 2
x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
class BaseEncoder(nn.Module):
"""Base `encoder` class. Every new `encoder` model must inherit this.
It defines common `encoder` specific functions.
"""
# pylint: disable=W0102
def __init__(self):
super(BaseEncoder, self).__init__()
def get_torch_mel_spectrogram_class(self, audio_config):
return torch.nn.Sequential(
PreEmphasis(audio_config["preemphasis"]),
# TorchSTFT(
# n_fft=audio_config["fft_size"],
# hop_length=audio_config["hop_length"],
# win_length=audio_config["win_length"],
# sample_rate=audio_config["sample_rate"],
# window="hamming_window",
# mel_fmin=0.0,
# mel_fmax=None,
# use_htk=True,
# do_amp_to_db=False,
# n_mels=audio_config["num_mels"],
# power=2.0,
# use_mel=True,
# mel_norm=None,
# )
torchaudio.transforms.MelSpectrogram(
sample_rate=audio_config["sample_rate"],
n_fft=audio_config["fft_size"],
win_length=audio_config["win_length"],
hop_length=audio_config["hop_length"],
window_fn=torch.hamming_window,
n_mels=audio_config["num_mels"],
),
)
@torch.no_grad()
def inference(self, x, l2_norm=True):
return self.forward(x, l2_norm)
@torch.no_grad()
def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True, l2_norm=True):
"""
Generate embeddings for a batch of utterances
x: 1xTxD
"""
# map to the waveform size
if self.use_torch_spec:
num_frames = num_frames * self.audio_config["hop_length"]
max_len = x.shape[1]
if max_len < num_frames:
num_frames = max_len
offsets = np.linspace(0, max_len - num_frames, num=num_eval)
frames_batch = []
for offset in offsets:
offset = int(offset)
end_offset = int(offset + num_frames)
frames = x[:, offset:end_offset]
frames_batch.append(frames)
frames_batch = torch.cat(frames_batch, dim=0)
embeddings = self.inference(frames_batch, l2_norm=l2_norm)
if return_mean:
embeddings = torch.mean(embeddings, dim=0, keepdim=True)
return embeddings
def get_criterion(self, c: Coqpit, num_classes=None):
if c.loss == "ge2e":
criterion = GE2ELoss(loss_method="softmax")
elif c.loss == "angleproto":
criterion = AngleProtoLoss()
elif c.loss == "softmaxproto":
criterion = SoftmaxAngleProtoLoss(c.model_params["proj_dim"], num_classes)
else:
raise Exception("The %s not is a loss supported" % c.loss)
return criterion
def load_checkpoint(
self,
config: Coqpit,
checkpoint_path: str,
eval: bool = False,
use_cuda: bool = False,
criterion=None,
cache=False,
):
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
try:
self.load_state_dict(state["model"])
print(" > Model fully restored. ")
except (KeyError, RuntimeError) as error:
# If eval raise the error
if eval:
raise error
print(" > Partial model initialization.")
model_dict = self.state_dict()
model_dict = set_init_dict(model_dict, state["model"], c)
self.load_state_dict(model_dict)
del model_dict
# load the criterion for restore_path
if criterion is not None and "criterion" in state:
try:
criterion.load_state_dict(state["criterion"])
except (KeyError, RuntimeError) as error:
print(" > Criterion load ignored because of:", error)
# instance and load the criterion for the encoder classifier in inference time
if (
eval
and criterion is None
and "criterion" in state
and getattr(config, "map_classid_to_classname", None) is not None
):
criterion = self.get_criterion(config, len(config.map_classid_to_classname))
criterion.load_state_dict(state["criterion"])
if use_cuda:
self.cuda()
if criterion is not None:
criterion = criterion.cuda()
if eval:
self.eval()
assert not self.training
if not eval:
return criterion, state["step"]
return criterion
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