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# Copyright (c) 2023 Amphion.
#
# This code is modified from https://github.com/ZhangXInFD/SpeechTokenizer/blob/main/speechtokenizer/model.py
# Licensed under Apache License 2.0
from .modules.seanet import SEANetEncoder, SEANetDecoder
from .modules.quantization import ResidualVectorQuantizer
import torch.nn as nn
from einops import rearrange
import torch
import numpy as np
class SpeechTokenizer(nn.Module):
def __init__(self, config):
"""
Parameters
----------
config : json
Model Config.
"""
super().__init__()
self.encoder = SEANetEncoder(
n_filters=config.get("n_filters"),
dimension=config.get("dimension"),
ratios=config.get("strides"),
lstm=config.get("lstm_layers"),
bidirectional=config.get("bidirectional"),
dilation_base=config.get("dilation_base"),
residual_kernel_size=config.get("residual_kernel_size"),
n_residual_layers=config.get("n_residual_layers"),
activation=config.get("activation"),
)
self.sample_rate = config.get("sample_rate")
self.n_q = config.get("n_q")
self.downsample_rate = np.prod(config.get("strides"))
if config.get("dimension") != config.get("semantic_dimension"):
self.transform = nn.Linear(
config.get("dimension"), config.get("semantic_dimension")
)
else:
self.transform = nn.Identity()
self.quantizer = ResidualVectorQuantizer(
dimension=config.get("dimension"),
n_q=config.get("n_q"),
bins=config.get("codebook_size"),
)
self.decoder = SEANetDecoder(
n_filters=config.get("n_filters"),
dimension=config.get("dimension"),
ratios=config.get("strides"),
lstm=config.get("lstm_layers"),
bidirectional=False,
dilation_base=config.get("dilation_base"),
residual_kernel_size=config.get("residual_kernel_size"),
n_residual_layers=config.get("n_residual_layers"),
activation=config.get("activation"),
)
@classmethod
def load_from_checkpoint(cls, config_path: str, ckpt_path: str):
"""
Parameters
----------
config_path : str
Path of model configuration file.
ckpt_path : str
Path of model checkpoint.
Returns
-------
model : SpeechTokenizer
SpeechTokenizer model.
"""
import json
with open(config_path) as f:
cfg = json.load(f)
model = cls(cfg)
params = torch.load(ckpt_path, map_location="cpu")
model.load_state_dict(params)
return model
def forward(self, x: torch.tensor, n_q: int = None, layers: list = [0]):
"""
Parameters
----------
x : torch.tensor
Input wavs. Shape: (batch, channels, timesteps).
n_q : int, optional
Number of quantizers in RVQ used to encode. The default is all layers.
layers : list[int], optional
Layers of RVQ should return quantized result. The default is the first layer.
Returns
-------
o : torch.tensor
Output wavs. Shape: (batch, channels, timesteps).
commit_loss : torch.tensor
Commitment loss from residual vector quantizers.
feature : torch.tensor
Output of RVQ's first layer. Shape: (batch, timesteps, dimension)
"""
n_q = n_q if n_q else self.n_q
e = self.encoder(x)
quantized, codes, commit_loss, quantized_list = self.quantizer(
e, n_q=n_q, layers=layers
)
feature = rearrange(quantized_list[0], "b d t -> b t d")
feature = self.transform(feature)
o = self.decoder(quantized)
return o, commit_loss, feature
def forward_feature(self, x: torch.tensor, layers: list = None):
"""
Parameters
----------
x : torch.tensor
Input wavs. Shape should be (batch, channels, timesteps).
layers : list[int], optional
Layers of RVQ should return quantized result. The default is all layers.
Returns
-------
quantized_list : list[torch.tensor]
Quantized of required layers.
"""
e = self.encoder(x)
layers = layers if layers else list(range(self.n_q))
quantized, codes, commit_loss, quantized_list = self.quantizer(e, layers=layers)
return quantized_list
def encode(self, x: torch.tensor, n_q: int = None, st: int = None):
"""
Parameters
----------
x : torch.tensor
Input wavs. Shape: (batch, channels, timesteps).
n_q : int, optional
Number of quantizers in RVQ used to encode. The default is all layers.
st : int, optional
Start quantizer index in RVQ. The default is 0.
Returns
-------
codes : torch.tensor
Output indices for each quantizer. Shape: (n_q, batch, timesteps)
"""
e = self.encoder(x)
if st is None:
st = 0
n_q = n_q if n_q else self.n_q
codes = self.quantizer.encode(e, n_q=n_q, st=st)
return codes
def decode(self, codes: torch.tensor, st: int = 0):
"""
Parameters
----------
codes : torch.tensor
Indices for each quantizer. Shape: (n_q, batch, timesteps).
st : int, optional
Start quantizer index in RVQ. The default is 0.
Returns
-------
o : torch.tensor
Reconstruct wavs from codes. Shape: (batch, channels, timesteps)
"""
quantized = self.quantizer.decode(codes, st=st)
o = self.decoder(quantized)
return o
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