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This commit is contained in:
Sam Khoze
2024-06-18 13:21:08 -07:00
committed by GitHub
parent 6af40bc6cf
commit dc8b8bca5a
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TTS/vc/models/__init__.py
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import importlib
import re
from typing import Dict, List, Union
def to_camel(text):
text = text.capitalize()
return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
def setup_model(config: "Coqpit", samples: Union[List[List], List[Dict]] = None) -> "BaseVC":
print(" > Using model: {}".format(config.model))
# fetch the right model implementation.
if "model" in config and config["model"].lower() == "freevc":
MyModel = importlib.import_module("TTS.vc.models.freevc").FreeVC
model = MyModel.init_from_config(config, samples)
return model
TTS/vc/models/__pycache__/__init__.cpython-311.pyc
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TTS/vc/models/base_vc.py
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import os
import random
from typing import Dict, List, Tuple, Union
import torch
import torch.distributed as dist
from coqpit import Coqpit
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
from trainer.torch import DistributedSampler, DistributedSamplerWrapper
from TTS.model import BaseTrainerModel
from TTS.tts.datasets.dataset import TTSDataset
from TTS.tts.utils.data import get_length_balancer_weights
from TTS.tts.utils.languages import LanguageManager, get_language_balancer_weights
from TTS.tts.utils.speakers import SpeakerManager, get_speaker_balancer_weights
from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
# pylint: skip-file
class BaseVC(BaseTrainerModel):
"""Base `vc` class. Every new `vc` model must inherit this.
It defines common `vc` specific functions on top of `Model` implementation.
"""
MODEL_TYPE = "vc"
def __init__(
self,
config: Coqpit,
ap: "AudioProcessor",
speaker_manager: SpeakerManager = None,
language_manager: LanguageManager = None,
):
super().__init__()
self.config = config
self.ap = ap
self.speaker_manager = speaker_manager
self.language_manager = language_manager
self._set_model_args(config)
def _set_model_args(self, config: Coqpit):
"""Setup model args based on the config type (`ModelConfig` or `ModelArgs`).
`ModelArgs` has all the fields reuqired to initialize the model architecture.
`ModelConfig` has all the fields required for training, inference and containes `ModelArgs`.
If the config is for training with a name like "*Config", then the model args are embeded in the
config.model_args
If the config is for the model with a name like "*Args", then we assign the directly.
"""
# don't use isintance not to import recursively
if "Config" in config.__class__.__name__:
self.config = config
self.args = config.model_args
elif "Args" in config.__class__.__name__:
self.args = config
else:
raise ValueError("config must be either a *Config or *Args")
def init_multispeaker(self, config: Coqpit, data: List = None):
"""Initialize a speaker embedding layer if needen and define expected embedding channel size for defining
`in_channels` size of the connected layers.
This implementation yields 3 possible outcomes:
1. If `config.use_speaker_embedding` and `config.use_d_vector_file are False, do nothing.
2. If `config.use_d_vector_file` is True, set expected embedding channel size to `config.d_vector_dim` or 512.
3. If `config.use_speaker_embedding`, initialize a speaker embedding layer with channel size of
`config.d_vector_dim` or 512.
You can override this function for new models.
Args:
config (Coqpit): Model configuration.
"""
# set number of speakers
if self.speaker_manager is not None:
self.num_speakers = self.speaker_manager.num_speakers
elif hasattr(config, "num_speakers"):
self.num_speakers = config.num_speakers
# set ultimate speaker embedding size
if config.use_speaker_embedding or config.use_d_vector_file:
self.embedded_speaker_dim = (
config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512
)
# init speaker embedding layer
if config.use_speaker_embedding and not config.use_d_vector_file:
print(" > Init speaker_embedding layer.")
self.speaker_embedding = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
self.speaker_embedding.weight.data.normal_(0, 0.3)
def get_aux_input(self, **kwargs) -> Dict:
"""Prepare and return `aux_input` used by `forward()`"""
return {"speaker_id": None, "style_wav": None, "d_vector": None, "language_id": None}
def get_aux_input_from_test_sentences(self, sentence_info):
if hasattr(self.config, "model_args"):
config = self.config.model_args
else:
config = self.config
# extract speaker and language info
text, speaker_name, style_wav, language_name = None, None, None, None
if isinstance(sentence_info, list):
if len(sentence_info) == 1:
text = sentence_info[0]
elif len(sentence_info) == 2:
text, speaker_name = sentence_info
elif len(sentence_info) == 3:
text, speaker_name, style_wav = sentence_info
elif len(sentence_info) == 4:
text, speaker_name, style_wav, language_name = sentence_info
else:
text = sentence_info
# get speaker id/d_vector
speaker_id, d_vector, language_id = None, None, None
if self.speaker_manager is not None:
if config.use_d_vector_file:
if speaker_name is None:
d_vector = self.speaker_manager.get_random_embedding()
else:
d_vector = self.speaker_manager.get_d_vector_by_name(speaker_name)
elif config.use_speaker_embedding:
if speaker_name is None:
speaker_id = self.speaker_manager.get_random_id()
else:
speaker_id = self.speaker_manager.name_to_id[speaker_name]
# get language id
if self.language_manager is not None and config.use_language_embedding and language_name is not None:
language_id = self.language_manager.name_to_id[language_name]
return {
"text": text,
"speaker_id": speaker_id,
"style_wav": style_wav,
"d_vector": d_vector,
"language_id": language_id,
}
def format_batch(self, batch: Dict) -> Dict:
"""Generic batch formatting for `VCDataset`.
You must override this if you use a custom dataset.
Args:
batch (Dict): [description]
Returns:
Dict: [description]
"""
# setup input batch
text_input = batch["token_id"]
text_lengths = batch["token_id_lengths"]
speaker_names = batch["speaker_names"]
linear_input = batch["linear"]
mel_input = batch["mel"]
mel_lengths = batch["mel_lengths"]
stop_targets = batch["stop_targets"]
item_idx = batch["item_idxs"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
attn_mask = batch["attns"]
waveform = batch["waveform"]
pitch = batch["pitch"]
energy = batch["energy"]
language_ids = batch["language_ids"]
max_text_length = torch.max(text_lengths.float())
max_spec_length = torch.max(mel_lengths.float())
# compute durations from attention masks
durations = None
if attn_mask is not None:
durations = torch.zeros(attn_mask.shape[0], attn_mask.shape[2])
for idx, am in enumerate(attn_mask):
# compute raw durations
c_idxs = am[:, : text_lengths[idx], : mel_lengths[idx]].max(1)[1]
# c_idxs, counts = torch.unique_consecutive(c_idxs, return_counts=True)
c_idxs, counts = torch.unique(c_idxs, return_counts=True)
dur = torch.ones([text_lengths[idx]]).to(counts.dtype)
dur[c_idxs] = counts
# smooth the durations and set any 0 duration to 1
# by cutting off from the largest duration indeces.
extra_frames = dur.sum() - mel_lengths[idx]
largest_idxs = torch.argsort(-dur)[:extra_frames]
dur[largest_idxs] -= 1
assert (
dur.sum() == mel_lengths[idx]
), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
durations[idx, : text_lengths[idx]] = dur
# set stop targets wrt reduction factor
stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()
return {
"text_input": text_input,
"text_lengths": text_lengths,
"speaker_names": speaker_names,
"mel_input": mel_input,
"mel_lengths": mel_lengths,
"linear_input": linear_input,
"stop_targets": stop_targets,
"stop_target_lengths": stop_target_lengths,
"attn_mask": attn_mask,
"durations": durations,
"speaker_ids": speaker_ids,
"d_vectors": d_vectors,
"max_text_length": float(max_text_length),
"max_spec_length": float(max_spec_length),
"item_idx": item_idx,
"waveform": waveform,
"pitch": pitch,
"energy": energy,
"language_ids": language_ids,
"audio_unique_names": batch["audio_unique_names"],
}
def get_sampler(self, config: Coqpit, dataset: TTSDataset, num_gpus=1):
weights = None
data_items = dataset.samples
if getattr(config, "use_language_weighted_sampler", False):
alpha = getattr(config, "language_weighted_sampler_alpha", 1.0)
print(" > Using Language weighted sampler with alpha:", alpha)
weights = get_language_balancer_weights(data_items) * alpha
if getattr(config, "use_speaker_weighted_sampler", False):
alpha = getattr(config, "speaker_weighted_sampler_alpha", 1.0)
print(" > Using Speaker weighted sampler with alpha:", alpha)
if weights is not None:
weights += get_speaker_balancer_weights(data_items) * alpha
else:
weights = get_speaker_balancer_weights(data_items) * alpha
if getattr(config, "use_length_weighted_sampler", False):
alpha = getattr(config, "length_weighted_sampler_alpha", 1.0)
print(" > Using Length weighted sampler with alpha:", alpha)
if weights is not None:
weights += get_length_balancer_weights(data_items) * alpha
else:
weights = get_length_balancer_weights(data_items) * alpha
if weights is not None:
sampler = WeightedRandomSampler(weights, len(weights))
else:
sampler = None
# sampler for DDP
if sampler is None:
sampler = DistributedSampler(dataset) if num_gpus > 1 else None
else: # If a sampler is already defined use this sampler and DDP sampler together
sampler = DistributedSamplerWrapper(sampler) if num_gpus > 1 else sampler
return sampler
def get_data_loader(
self,
config: Coqpit,
assets: Dict,
is_eval: bool,
samples: Union[List[Dict], List[List]],
verbose: bool,
num_gpus: int,
rank: int = None,
) -> "DataLoader":
if is_eval and not config.run_eval:
loader = None
else:
# setup multi-speaker attributes
if self.speaker_manager is not None:
if hasattr(config, "model_args"):
speaker_id_mapping = (
self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None
)
d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None
config.use_d_vector_file = config.model_args.use_d_vector_file
else:
speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None
d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None
else:
speaker_id_mapping = None
d_vector_mapping = None
# setup multi-lingual attributes
if self.language_manager is not None:
language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None
else:
language_id_mapping = None
# init dataloader
dataset = TTSDataset(
outputs_per_step=config.r if "r" in config else 1,
compute_linear_spec=config.model.lower() == "tacotron" or config.compute_linear_spec,
compute_f0=config.get("compute_f0", False),
f0_cache_path=config.get("f0_cache_path", None),
compute_energy=config.get("compute_energy", False),
energy_cache_path=config.get("energy_cache_path", None),
samples=samples,
ap=self.ap,
return_wav=config.return_wav if "return_wav" in config else False,
batch_group_size=0 if is_eval else config.batch_group_size * config.batch_size,
min_text_len=config.min_text_len,
max_text_len=config.max_text_len,
min_audio_len=config.min_audio_len,
max_audio_len=config.max_audio_len,
phoneme_cache_path=config.phoneme_cache_path,
precompute_num_workers=config.precompute_num_workers,
use_noise_augment=False if is_eval else config.use_noise_augment,
verbose=verbose,
speaker_id_mapping=speaker_id_mapping,
d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,
tokenizer=None,
start_by_longest=config.start_by_longest,
language_id_mapping=language_id_mapping,
)
# wait all the DDP process to be ready
if num_gpus > 1:
dist.barrier()
# sort input sequences from short to long
dataset.preprocess_samples()
# get samplers
sampler = self.get_sampler(config, dataset, num_gpus)
loader = DataLoader(
dataset,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
shuffle=config.shuffle if sampler is None else False, # if there is no other sampler
collate_fn=dataset.collate_fn,
drop_last=config.drop_last, # setting this False might cause issues in AMP training.
sampler=sampler,
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
return loader
def _get_test_aux_input(
self,
) -> Dict:
d_vector = None
if self.config.use_d_vector_file:
d_vector = [self.speaker_manager.embeddings[name]["embedding"] for name in self.speaker_manager.embeddings]
d_vector = (random.sample(sorted(d_vector), 1),)
aux_inputs = {
"speaker_id": None
if not self.config.use_speaker_embedding
else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
"d_vector": d_vector,
"style_wav": None, # TODO: handle GST style input
}
return aux_inputs
def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
"""Generic test run for `vc` models used by `Trainer`.
You can override this for a different behaviour.
Args:
assets (dict): A dict of training assets. For `vc` models, it must include `{'audio_processor': ap}`.
Returns:
Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
"""
print(" | > Synthesizing test sentences.")
test_audios = {}
test_figures = {}
test_sentences = self.config.test_sentences
aux_inputs = self._get_test_aux_input()
for idx, sen in enumerate(test_sentences):
if isinstance(sen, list):
aux_inputs = self.get_aux_input_from_test_sentences(sen)
sen = aux_inputs["text"]
outputs_dict = synthesis(
self,
sen,
self.config,
"cuda" in str(next(self.parameters()).device),
speaker_id=aux_inputs["speaker_id"],
d_vector=aux_inputs["d_vector"],
style_wav=aux_inputs["style_wav"],
use_griffin_lim=True,
do_trim_silence=False,
)
test_audios["{}-audio".format(idx)] = outputs_dict["wav"]
test_figures["{}-prediction".format(idx)] = plot_spectrogram(
outputs_dict["outputs"]["model_outputs"], self.ap, output_fig=False
)
test_figures["{}-alignment".format(idx)] = plot_alignment(
outputs_dict["outputs"]["alignments"], output_fig=False
)
return test_figures, test_audios
def on_init_start(self, trainer):
"""Save the speaker.pth and language_ids.json at the beginning of the training. Also update both paths."""
if self.speaker_manager is not None:
output_path = os.path.join(trainer.output_path, "speakers.pth")
self.speaker_manager.save_ids_to_file(output_path)
trainer.config.speakers_file = output_path
# some models don't have `model_args` set
if hasattr(trainer.config, "model_args"):
trainer.config.model_args.speakers_file = output_path
trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
print(f" > `speakers.pth` is saved to {output_path}.")
print(" > `speakers_file` is updated in the config.json.")
if self.language_manager is not None:
output_path = os.path.join(trainer.output_path, "language_ids.json")
self.language_manager.save_ids_to_file(output_path)
trainer.config.language_ids_file = output_path
if hasattr(trainer.config, "model_args"):
trainer.config.model_args.language_ids_file = output_path
trainer.config.save_json(os.path.join(trainer.output_path, "config.json"))
print(f" > `language_ids.json` is saved to {output_path}.")
print(" > `language_ids_file` is updated in the config.json.")
TTS/vc/models/freevc.py
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from typing import Dict, List, Optional, Tuple, Union
import librosa
import numpy as np
import torch
from coqpit import Coqpit
from torch import nn
from torch.nn import Conv1d, Conv2d, ConvTranspose1d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
import TTS.vc.modules.freevc.commons as commons
import TTS.vc.modules.freevc.modules as modules
from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.io import load_fsspec
from TTS.vc.configs.freevc_config import FreeVCConfig
from TTS.vc.models.base_vc import BaseVC
from TTS.vc.modules.freevc.commons import get_padding, init_weights
from TTS.vc.modules.freevc.mel_processing import mel_spectrogram_torch
from TTS.vc.modules.freevc.speaker_encoder.speaker_encoder import SpeakerEncoder as SpeakerEncoderEx
from TTS.vc.modules.freevc.wavlm import get_wavlm
class ResidualCouplingBlock(nn.Module):
def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, n_flows=4, gin_channels=0):
super().__init__()
self.channels = channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.n_flows = n_flows
self.gin_channels = gin_channels
self.flows = nn.ModuleList()
for i in range(n_flows):
self.flows.append(
modules.ResidualCouplingLayer(
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=gin_channels,
mean_only=True,
)
)
self.flows.append(modules.Flip())
def forward(self, x, x_mask, g=None, reverse=False):
if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
class Encoder(nn.Module):
def __init__(
self, in_channels, out_channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.hidden_channels = hidden_channels
self.kernel_size = kernel_size
self.dilation_rate = dilation_rate
self.n_layers = n_layers
self.gin_channels = gin_channels
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
def forward(self, x, x_lengths, g=None):
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
class Generator(torch.nn.Module):
def __init__(
self,
initial_channel,
resblock,
resblock_kernel_sizes,
resblock_dilation_sizes,
upsample_rates,
upsample_initial_channel,
upsample_kernel_sizes,
gin_channels=0,
):
super(Generator, self).__init__()
self.num_kernels = len(resblock_kernel_sizes)
self.num_upsamples = len(upsample_rates)
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
self.ups.append(
weight_norm(
ConvTranspose1d(
upsample_initial_channel // (2**i),
upsample_initial_channel // (2 ** (i + 1)),
k,
u,
padding=(k - u) // 2,
)
)
)
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(init_weights)
if gin_channels != 0:
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
def forward(self, x, g=None):
x = self.conv_pre(x)
if g is not None:
x = x + self.cond(g)
for i in range(self.num_upsamples):
x = F.leaky_relu(x, modules.LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
x = F.leaky_relu(x)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print("Removing weight norm...")
for l in self.ups:
remove_parametrizations(l, "weight")
for l in self.resblocks:
remove_parametrizations(l, "weight")
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
]
)
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList(
[
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
]
)
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
def forward(self, x):
fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(MultiPeriodDiscriminator, self).__init__()
periods = [2, 3, 5, 7, 11]
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
self.discriminators = nn.ModuleList(discs)
def forward(self, y, y_hat):
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for i, d in enumerate(self.discriminators):
y_d_r, fmap_r = d(y)
y_d_g, fmap_g = d(y_hat)
y_d_rs.append(y_d_r)
y_d_gs.append(y_d_g)
fmap_rs.append(fmap_r)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class SpeakerEncoder(torch.nn.Module):
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
super(SpeakerEncoder, self).__init__()
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
self.relu = nn.ReLU()
def forward(self, mels):
self.lstm.flatten_parameters()
_, (hidden, _) = self.lstm(mels)
embeds_raw = self.relu(self.linear(hidden[-1]))
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
mel_slices = []
for i in range(0, total_frames - partial_frames, partial_hop):
mel_range = torch.arange(i, i + partial_frames)
mel_slices.append(mel_range)
return mel_slices
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
mel_len = mel.size(1)
last_mel = mel[:, -partial_frames:]
if mel_len > partial_frames:
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
mels = list(mel[:, s] for s in mel_slices)
mels.append(last_mel)
mels = torch.stack(tuple(mels), 0).squeeze(1)
with torch.no_grad():
partial_embeds = self(mels)
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
# embed = embed / torch.linalg.norm(embed, 2)
else:
with torch.no_grad():
embed = self(last_mel)
return embed
class FreeVC(BaseVC):
"""
Papaer::
https://arxiv.org/abs/2210.15418#
Paper Abstract::
Voice conversion (VC) can be achieved by first extracting source content information and target speaker
information, and then reconstructing waveform with these information. However, current approaches normally
either extract dirty content information with speaker information leaked in, or demand a large amount of
annotated data for training. Besides, the quality of reconstructed waveform can be degraded by the
mismatch between conversion model and vocoder. In this paper, we adopt the end-to-end framework of VITS for
high-quality waveform reconstruction, and propose strategies for clean content information extraction without
text annotation. We disentangle content information by imposing an information bottleneck to WavLM features,
and propose the spectrogram-resize based data augmentation to improve the purity of extracted content
information. Experimental results show that the proposed method outperforms the latest VC models trained with
annotated data and has greater robustness.
Original Code::
https://github.com/OlaWod/FreeVC
Examples:
>>> from TTS.vc.configs.freevc_config import FreeVCConfig
>>> from TTS.vc.models.freevc import FreeVC
>>> config = FreeVCConfig()
>>> model = FreeVC(config)
"""
def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
super().__init__(config, None, speaker_manager, None)
self.init_multispeaker(config)
self.spec_channels = self.args.spec_channels
self.inter_channels = self.args.inter_channels
self.hidden_channels = self.args.hidden_channels
self.filter_channels = self.args.filter_channels
self.n_heads = self.args.n_heads
self.n_layers = self.args.n_layers
self.kernel_size = self.args.kernel_size
self.p_dropout = self.args.p_dropout
self.resblock = self.args.resblock
self.resblock_kernel_sizes = self.args.resblock_kernel_sizes
self.resblock_dilation_sizes = self.args.resblock_dilation_sizes
self.upsample_rates = self.args.upsample_rates
self.upsample_initial_channel = self.args.upsample_initial_channel
self.upsample_kernel_sizes = self.args.upsample_kernel_sizes
self.segment_size = self.args.segment_size
self.gin_channels = self.args.gin_channels
self.ssl_dim = self.args.ssl_dim
self.use_spk = self.args.use_spk
self.enc_p = Encoder(self.args.ssl_dim, self.inter_channels, self.hidden_channels, 5, 1, 16)
self.dec = Generator(
self.inter_channels,
self.resblock,
self.resblock_kernel_sizes,
self.resblock_dilation_sizes,
self.upsample_rates,
self.upsample_initial_channel,
self.upsample_kernel_sizes,
gin_channels=self.gin_channels,
)
self.enc_q = Encoder(
self.spec_channels, self.inter_channels, self.hidden_channels, 5, 1, 16, gin_channels=self.gin_channels
)
self.flow = ResidualCouplingBlock(
self.inter_channels, self.hidden_channels, 5, 1, 4, gin_channels=self.gin_channels
)
if not self.use_spk:
self.enc_spk = SpeakerEncoder(model_hidden_size=self.gin_channels, model_embedding_size=self.gin_channels)
else:
self.load_pretrained_speaker_encoder()
self.wavlm = get_wavlm()
@property
def device(self):
return next(self.parameters()).device
def load_pretrained_speaker_encoder(self):
"""Load pretrained speaker encoder model as mentioned in the paper."""
print(" > Loading pretrained speaker encoder model ...")
self.enc_spk_ex = SpeakerEncoderEx(
"https://github.com/coqui-ai/TTS/releases/download/v0.13.0_models/speaker_encoder.pt"
)
def init_multispeaker(self, config: Coqpit):
"""Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
or with external `d_vectors` computed from a speaker encoder model.
You must provide a `speaker_manager` at initialization to set up the multi-speaker modules.
Args:
config (Coqpit): Model configuration.
data (List, optional): Dataset items to infer number of speakers. Defaults to None.
"""
self.num_spks = self.args.num_spks
if self.speaker_manager:
self.num_spks = self.speaker_manager.num_spks
def forward(
self,
c: torch.Tensor,
spec: torch.Tensor,
g: Optional[torch.Tensor] = None,
mel: Optional[torch.Tensor] = None,
c_lengths: Optional[torch.Tensor] = None,
spec_lengths: Optional[torch.Tensor] = None,
) -> Tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
]:
"""
Forward pass of the model.
Args:
c: WavLM features. Shape: (batch_size, c_seq_len).
spec: The input spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
g: The speaker embedding. Shape: (batch_size, spk_emb_dim).
mel: The input mel-spectrogram for the speaker encoder. Shape: (batch_size, mel_seq_len, mel_dim).
c_lengths: The lengths of the WavLM features. Shape: (batch_size,).
spec_lengths: The lengths of the spectrogram. Shape: (batch_size,).
Returns:
o: The output spectrogram. Shape: (batch_size, spec_seq_len, spec_dim).
ids_slice: The slice indices. Shape: (batch_size, num_slices).
spec_mask: The spectrogram mask. Shape: (batch_size, spec_seq_len).
(z, z_p, m_p, logs_p, m_q, logs_q): A tuple of latent variables.
"""
# If c_lengths is None, set it to the length of the last dimension of c
if c_lengths is None:
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
# If spec_lengths is None, set it to the length of the last dimension of spec
if spec_lengths is None:
spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
# If use_spk is False, compute g from mel using enc_spk
g = None
if not self.use_spk:
g = self.enc_spk(mel).unsqueeze(-1)
# Compute m_p, logs_p, z, m_q, logs_q, and spec_mask using enc_p and enc_q
_, m_p, logs_p, _ = self.enc_p(c, c_lengths)
z, m_q, logs_q, spec_mask = self.enc_q(spec.transpose(1, 2), spec_lengths, g=g)
# Compute z_p using flow
z_p = self.flow(z, spec_mask, g=g)
# Randomly slice z and compute o using dec
z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
o = self.dec(z_slice, g=g)
return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
@torch.no_grad()
def inference(self, c, g=None, mel=None, c_lengths=None):
"""
Inference pass of the model
Args:
c (torch.Tensor): Input tensor. Shape: (batch_size, c_seq_len).
g (torch.Tensor): Speaker embedding tensor. Shape: (batch_size, spk_emb_dim).
mel (torch.Tensor): Mel-spectrogram tensor. Shape: (batch_size, mel_seq_len, mel_dim).
c_lengths (torch.Tensor): Lengths of the input tensor. Shape: (batch_size,).
Returns:
torch.Tensor: Output tensor.
"""
if c_lengths == None:
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
if not self.use_spk:
g = self.enc_spk.embed_utterance(mel)
g = g.unsqueeze(-1)
z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
z = self.flow(z_p, c_mask, g=g, reverse=True)
o = self.dec(z * c_mask, g=g)
return o
def extract_wavlm_features(self, y):
"""Extract WavLM features from an audio tensor.
Args:
y (torch.Tensor): Audio tensor. Shape: (batch_size, audio_seq_len).
"""
with torch.no_grad():
c = self.wavlm.extract_features(y)[0]
c = c.transpose(1, 2)
return c
def load_audio(self, wav):
"""Read and format the input audio."""
if isinstance(wav, str):
wav, _ = librosa.load(wav, sr=self.config.audio.input_sample_rate)
if isinstance(wav, np.ndarray):
wav = torch.from_numpy(wav).to(self.device)
if isinstance(wav, torch.Tensor):
wav = wav.to(self.device)
if isinstance(wav, list):
wav = torch.from_numpy(np.array(wav)).to(self.device)
return wav.float()
@torch.inference_mode()
def voice_conversion(self, src, tgt):
"""
Voice conversion pass of the model.
Args:
src (str or torch.Tensor): Source utterance.
tgt (str or torch.Tensor): Target utterance.
Returns:
torch.Tensor: Output tensor.
"""
wav_tgt = self.load_audio(tgt).cpu().numpy()
wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
if self.config.model_args.use_spk:
g_tgt = self.enc_spk_ex.embed_utterance(wav_tgt)
g_tgt = torch.from_numpy(g_tgt)[None, :, None].to(self.device)
else:
wav_tgt = torch.from_numpy(wav_tgt).unsqueeze(0).to(self.device)
mel_tgt = mel_spectrogram_torch(
wav_tgt,
self.config.audio.filter_length,
self.config.audio.n_mel_channels,
self.config.audio.input_sample_rate,
self.config.audio.hop_length,
self.config.audio.win_length,
self.config.audio.mel_fmin,
self.config.audio.mel_fmax,
)
# src
wav_src = self.load_audio(src)
c = self.extract_wavlm_features(wav_src[None, :])
if self.config.model_args.use_spk:
audio = self.inference(c, g=g_tgt)
else:
audio = self.inference(c, mel=mel_tgt.transpose(1, 2))
audio = audio[0][0].data.cpu().float().numpy()
return audio
def eval_step():
...
@staticmethod
def init_from_config(config: FreeVCConfig, samples: Union[List[List], List[Dict]] = None, verbose=True):
model = FreeVC(config)
return model
def load_checkpoint(self, config, checkpoint_path, eval=False, strict=True, cache=False):
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
self.load_state_dict(state["model"], strict=strict)
if eval:
self.eval()
def train_step():
...