mirror of https://github.com/coqui-ai/TTS.git
Add BaseEncoder Class
This commit is contained in:
Edresson Casanova
parent
a9208e9edd
commit
50305215b3
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@ -11,15 +11,14 @@ from torch.utils.data import DataLoader
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from trainer.torch import NoamLR
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from TTS.encoder.dataset import EncoderDataset
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from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
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from TTS.encoder.utils.generic_utils import save_best_model, save_checkpoint, setup_speaker_encoder_model
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from TTS.encoder.utils.samplers import PerfectBatchSampler
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from TTS.encoder.utils.training import init_training
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from TTS.encoder.utils.visual import plot_embeddings
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from TTS.tts.datasets import load_tts_samples
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.generic_utils import count_parameters, remove_experiment_folder, set_init_dict
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from TTS.utils.io import load_fsspec, copy_model_files
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from TTS.utils.generic_utils import count_parameters, remove_experiment_folder
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from TTS.utils.io import copy_model_files
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from trainer.trainer_utils import get_optimizer
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from TTS.utils.training import check_update
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@ -254,40 +253,17 @@ def main(args): # pylint: disable=redefined-outer-name
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eval_data_loader, _, _ = setup_loader(ap, is_val=True, verbose=True)
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else:
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eval_data_loader = None
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num_classes = len(train_classes)
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if c.loss == "ge2e":
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criterion = GE2ELoss(loss_method="softmax")
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elif c.loss == "angleproto":
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criterion = AngleProtoLoss()
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elif c.loss == "softmaxproto":
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criterion = SoftmaxAngleProtoLoss(c.model_params["proj_dim"], num_classes)
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if c.model == "emotion_encoder":
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# update config with the class map
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num_classes = len(train_classes)
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criterion = model.get_criterion(c, num_classes)
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if c.loss == "softmaxproto" and c.model != "speaker_encoder":
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c.map_classid_to_classname = map_classid_to_classname
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copy_model_files(c, OUT_PATH)
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else:
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raise Exception("The %s not is a loss supported" % c.loss)
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if args.restore_path:
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checkpoint = load_fsspec(args.restore_path)
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try:
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model.load_state_dict(checkpoint["model"])
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if "criterion" in checkpoint:
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criterion.load_state_dict(checkpoint["criterion"])
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except (KeyError, RuntimeError):
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print(" > Partial model initialization.")
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model_dict = model.state_dict()
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model_dict = set_init_dict(model_dict, checkpoint["model"], c)
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model.load_state_dict(model_dict)
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del model_dict
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for group in optimizer.param_groups:
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group["lr"] = c.lr
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print(" > Model restored from step %d" % checkpoint["step"], flush=True)
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args.restore_step = checkpoint["step"]
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criterion, args.restore_step = model.load_checkpoint(c, args.restore_path, eval=False, use_cuda=use_cuda, criterion=criterion)
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print(" > Model restored from step %d" % args.restore_step, flush=True)
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else:
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args.restore_step = 0
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@ -0,0 +1,141 @@
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import torch
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import torchaudio
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import numpy as np
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from torch import nn
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from TTS.utils.io import load_fsspec
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from TTS.encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
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from TTS.utils.generic_utils import set_init_dict
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from coqpit import Coqpit
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class PreEmphasis(nn.Module):
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def __init__(self, coefficient=0.97):
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super().__init__()
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self.coefficient = coefficient
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self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
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def forward(self, x):
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assert len(x.size()) == 2
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x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
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return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
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class BaseEncoder(nn.Module):
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"""Base `encoder` class. Every new `encoder` model must inherit this.
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It defines common `encoder` specific functions.
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"""
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# pylint: disable=W0102
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def __init__(self):
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super(BaseEncoder, self).__init__()
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def get_torch_mel_spectrogram_class(audio_config):
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return torch.nn.Sequential(
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PreEmphasis(audio_config["preemphasis"]),
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# TorchSTFT(
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# n_fft=audio_config["fft_size"],
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# hop_length=audio_config["hop_length"],
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# win_length=audio_config["win_length"],
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# sample_rate=audio_config["sample_rate"],
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# window="hamming_window",
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# mel_fmin=0.0,
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# mel_fmax=None,
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# use_htk=True,
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# do_amp_to_db=False,
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# n_mels=audio_config["num_mels"],
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# power=2.0,
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# use_mel=True,
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# mel_norm=None,
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# )
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torchaudio.transforms.MelSpectrogram(
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sample_rate=audio_config["sample_rate"],
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n_fft=audio_config["fft_size"],
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win_length=audio_config["win_length"],
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hop_length=audio_config["hop_length"],
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window_fn=torch.hamming_window,
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n_mels=audio_config["num_mels"],
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)
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)
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@torch.no_grad()
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def inference(self, x, l2_norm=False):
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return self.forward(x, l2_norm)
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@torch.no_grad()
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def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True, l2_norm=True):
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"""
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Generate embeddings for a batch of utterances
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x: 1xTxD
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"""
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# map to the waveform size
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if self.use_torch_spec:
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num_frames = num_frames * self.audio_config["hop_length"]
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max_len = x.shape[1]
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if max_len < num_frames:
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num_frames = max_len
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offsets = np.linspace(0, max_len - num_frames, num=num_eval)
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frames_batch = []
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for offset in offsets:
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offset = int(offset)
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end_offset = int(offset + num_frames)
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frames = x[:, offset:end_offset]
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frames_batch.append(frames)
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frames_batch = torch.cat(frames_batch, dim=0)
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embeddings = self.inference(frames_batch, l2_norm=l2_norm)
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if return_mean:
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embeddings = torch.mean(embeddings, dim=0, keepdim=True)
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return embeddings
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def get_criterion(self, c: Coqpit, num_classes=None):
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if c.loss == "ge2e":
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criterion = GE2ELoss(loss_method="softmax")
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elif c.loss == "angleproto":
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criterion = AngleProtoLoss()
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elif c.loss == "softmaxproto":
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criterion = SoftmaxAngleProtoLoss(c.model_params["proj_dim"], num_classes)
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else:
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raise Exception("The %s not is a loss supported" % c.loss)
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return criterion
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def load_checkpoint(self, config: Coqpit, checkpoint_path: str, eval: bool = False, use_cuda: bool = False, criterion=None):
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state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
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try:
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self.load_state_dict(state["model"])
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except (KeyError, RuntimeError) as error:
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# If eval raise the error
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if eval:
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raise error
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print(" > Partial model initialization.")
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model_dict = self.state_dict()
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model_dict = set_init_dict(model_dict, state["model"], c)
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self.load_state_dict(model_dict)
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del model_dict
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# load the criterion for restore_path
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if criterion is not None and "criterion" in state:
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criterion.load_state_dict(state["criterion"])
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# instance and load the criterion for the encoder classifier in inference time
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if eval and criterion is None and "criterion" in state and config.map_classid_to_classname is not None:
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criterion = self.get_criterion(config, len(config.map_classid_to_classname))
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criterion.load_state_dict(state["criterion"])
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if use_cuda:
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self.cuda()
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if criterion is not None:
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criterion = criterion.cuda()
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if eval:
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self.eval()
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assert not self.training
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if not eval:
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return criterion, state["step"]
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return criterion
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@ -1,10 +1,7 @@
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import numpy as np
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import torch
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import torchaudio
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from torch import nn
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from TTS.encoder.models.resnet import PreEmphasis
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from TTS.utils.io import load_fsspec
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from TTS.encoder.models.base_encoder import BaseEncoder
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class LSTMWithProjection(nn.Module):
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@ -34,7 +31,7 @@ class LSTMWithoutProjection(nn.Module):
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return self.relu(self.linear(hidden[-1]))
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class LSTMSpeakerEncoder(nn.Module):
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class LSTMSpeakerEncoder(BaseEncoder):
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def __init__(
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self,
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input_dim,
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@ -64,32 +61,7 @@ class LSTMSpeakerEncoder(nn.Module):
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self.instancenorm = nn.InstanceNorm1d(input_dim)
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if self.use_torch_spec:
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self.torch_spec = torch.nn.Sequential(
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PreEmphasis(audio_config["preemphasis"]),
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# TorchSTFT(
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# n_fft=audio_config["fft_size"],
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# hop_length=audio_config["hop_length"],
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# win_length=audio_config["win_length"],
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# sample_rate=audio_config["sample_rate"],
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# window="hamming_window",
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# mel_fmin=0.0,
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# mel_fmax=None,
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# use_htk=True,
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# do_amp_to_db=False,
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# n_mels=audio_config["num_mels"],
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# power=2.0,
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# use_mel=True,
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# mel_norm=None,
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# )
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torchaudio.transforms.MelSpectrogram(
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sample_rate=audio_config["sample_rate"],
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n_fft=audio_config["fft_size"],
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win_length=audio_config["win_length"],
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hop_length=audio_config["hop_length"],
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window_fn=torch.hamming_window,
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n_mels=audio_config["num_mels"],
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),
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)
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self.torch_spec = self.get_torch_mel_spectrogram_class(audio_config)
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else:
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self.torch_spec = None
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@ -125,75 +97,3 @@ class LSTMSpeakerEncoder(nn.Module):
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if l2_norm:
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d = torch.nn.functional.normalize(d, p=2, dim=1)
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return d
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@torch.no_grad()
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def inference(self, x, l2_norm=True):
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d = self.forward(x, l2_norm=l2_norm)
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return d
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def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True):
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"""
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Generate embeddings for a batch of utterances
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x: 1xTxD
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"""
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max_len = x.shape[1]
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if max_len < num_frames:
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num_frames = max_len
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offsets = np.linspace(0, max_len - num_frames, num=num_eval)
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frames_batch = []
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for offset in offsets:
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offset = int(offset)
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end_offset = int(offset + num_frames)
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frames = x[:, offset:end_offset]
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frames_batch.append(frames)
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frames_batch = torch.cat(frames_batch, dim=0)
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embeddings = self.inference(frames_batch)
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if return_mean:
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embeddings = torch.mean(embeddings, dim=0, keepdim=True)
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return embeddings
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def batch_compute_embedding(self, x, seq_lens, num_frames=160, overlap=0.5):
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"""
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Generate embeddings for a batch of utterances
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x: BxTxD
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"""
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num_overlap = num_frames * overlap
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max_len = x.shape[1]
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embed = None
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num_iters = seq_lens / (num_frames - num_overlap)
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cur_iter = 0
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for offset in range(0, max_len, num_frames - num_overlap):
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cur_iter += 1
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end_offset = min(x.shape[1], offset + num_frames)
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frames = x[:, offset:end_offset]
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if embed is None:
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embed = self.inference(frames)
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else:
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embed[cur_iter <= num_iters, :] += self.inference(frames[cur_iter <= num_iters, :, :])
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return embed / num_iters
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# pylint: disable=unused-argument, redefined-builtin
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def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
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state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
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self.load_state_dict(state["model"])
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# load the criterion for emotion classification
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if "criterion" in state and config.loss == "softmaxproto" and config.model == "emotion_encoder" and config.map_classid_to_classname is not None:
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criterion = SoftmaxAngleProtoLoss(config.model_params["proj_dim"], len(config.map_classid_to_classname.keys()))
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criterion.load_state_dict(state["criterion"])
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else:
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criterion = None
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if use_cuda:
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self.cuda()
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if criterion is not None:
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criterion = criterion.cuda()
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if eval:
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self.eval()
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assert not self.training
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return criterion
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@ -1,24 +1,8 @@
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import numpy as np
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import torch
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import torchaudio
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from torch import nn
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# from TTS.utils.audio import TorchSTFT
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from TTS.utils.io import load_fsspec
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from TTS.encoder.losses import SoftmaxAngleProtoLoss
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class PreEmphasis(nn.Module):
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def __init__(self, coefficient=0.97):
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super().__init__()
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self.coefficient = coefficient
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self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
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def forward(self, x):
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assert len(x.size()) == 2
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x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
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return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
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from TTS.encoder.models.base_encoder import BaseEncoder
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=8):
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@ -71,7 +55,7 @@ class SEBasicBlock(nn.Module):
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return out
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class ResNetSpeakerEncoder(nn.Module):
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class ResNetSpeakerEncoder(BaseEncoder):
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"""Implementation of the model H/ASP without batch normalization in speaker embedding. This model was proposed in: https://arxiv.org/abs/2009.14153
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Adapted from: https://github.com/clovaai/voxceleb_trainer
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"""
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@ -110,32 +94,7 @@ class ResNetSpeakerEncoder(nn.Module):
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self.instancenorm = nn.InstanceNorm1d(input_dim)
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if self.use_torch_spec:
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self.torch_spec = torch.nn.Sequential(
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PreEmphasis(audio_config["preemphasis"]),
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# TorchSTFT(
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# n_fft=audio_config["fft_size"],
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# hop_length=audio_config["hop_length"],
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# win_length=audio_config["win_length"],
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# sample_rate=audio_config["sample_rate"],
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# window="hamming_window",
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# mel_fmin=0.0,
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# mel_fmax=None,
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# use_htk=True,
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# do_amp_to_db=False,
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# n_mels=audio_config["num_mels"],
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# power=2.0,
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# use_mel=True,
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# mel_norm=None,
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# )
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torchaudio.transforms.MelSpectrogram(
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sample_rate=audio_config["sample_rate"],
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n_fft=audio_config["fft_size"],
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win_length=audio_config["win_length"],
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hop_length=audio_config["hop_length"],
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window_fn=torch.hamming_window,
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n_mels=audio_config["num_mels"],
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),
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)
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self.torch_spec = self.get_torch_mel_spectrogram_class(audio_config)
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else:
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self.torch_spec = None
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@ -238,57 +197,3 @@ class ResNetSpeakerEncoder(nn.Module):
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if l2_norm:
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x = torch.nn.functional.normalize(x, p=2, dim=1)
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return x
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@torch.no_grad()
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def inference(self, x, l2_norm=False):
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return self.forward(x, l2_norm)
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@torch.no_grad()
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def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True, l2_norm=True):
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"""
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Generate embeddings for a batch of utterances
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x: 1xTxD
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"""
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# map to the waveform size
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if self.use_torch_spec:
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num_frames = num_frames * self.audio_config["hop_length"]
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max_len = x.shape[1]
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if max_len < num_frames:
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num_frames = max_len
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offsets = np.linspace(0, max_len - num_frames, num=num_eval)
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frames_batch = []
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for offset in offsets:
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offset = int(offset)
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end_offset = int(offset + num_frames)
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frames = x[:, offset:end_offset]
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frames_batch.append(frames)
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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 load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
|
||||
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
|
||||
self.load_state_dict(state["model"])
|
||||
# load the criterion for emotion classification
|
||||
if "criterion" in state and config.loss == "softmaxproto" and config.model == "emotion_encoder" and config.map_classid_to_classname is not None:
|
||||
criterion = SoftmaxAngleProtoLoss(config.model_params["proj_dim"], len(config.map_classid_to_classname.keys()))
|
||||
criterion.load_state_dict(state["criterion"])
|
||||
else:
|
||||
criterion = None
|
||||
|
||||
if use_cuda:
|
||||
self.cuda()
|
||||
if criterion is not None:
|
||||
criterion = criterion.cuda()
|
||||
if eval:
|
||||
self.eval()
|
||||
assert not self.training
|
||||
return criterion
|
||||
|
|
|
|||
Loading…
Reference in New Issue