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MusicGen / audiocraft /models /encodec.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from abc import ABC, abstractmethod
import typing as tp
from einops import rearrange
import torch
from torch import nn
from .. import quantization as qt
class CompressionModel(ABC, nn.Module):
@abstractmethod
def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
...
@abstractmethod
def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
"""See `EncodecModel.encode`"""
...
@abstractmethod
def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
"""See `EncodecModel.decode`"""
...
@property
@abstractmethod
def channels(self) -> int:
...
@property
@abstractmethod
def frame_rate(self) -> int:
...
@property
@abstractmethod
def sample_rate(self) -> int:
...
@property
@abstractmethod
def cardinality(self) -> int:
...
@property
@abstractmethod
def num_codebooks(self) -> int:
...
@property
@abstractmethod
def total_codebooks(self) -> int:
...
@abstractmethod
def set_num_codebooks(self, n: int):
"""Set the active number of codebooks used by the quantizer.
"""
...
class EncodecModel(CompressionModel):
"""Encodec model operating on the raw waveform.
Args:
encoder (nn.Module): Encoder network.
decoder (nn.Module): Decoder network.
quantizer (qt.BaseQuantizer): Quantizer network.
frame_rate (int): Frame rate for the latent representation.
sample_rate (int): Audio sample rate.
channels (int): Number of audio channels.
causal (bool): Whether to use a causal version of the model.
renormalize (bool): Whether to renormalize the audio before running the model.
"""
# we need assignement to override the property in the abstract class,
# I couldn't find a better way...
frame_rate: int = 0
sample_rate: int = 0
channels: int = 0
def __init__(self,
encoder: nn.Module,
decoder: nn.Module,
quantizer: qt.BaseQuantizer,
frame_rate: int,
sample_rate: int,
channels: int,
causal: bool = False,
renormalize: bool = False):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.quantizer = quantizer
self.frame_rate = frame_rate
self.sample_rate = sample_rate
self.channels = channels
self.renormalize = renormalize
self.causal = causal
if self.causal:
# we force disabling here to avoid handling linear overlap of segments
# as supported in original EnCodec codebase.
assert not self.renormalize, 'Causal model does not support renormalize'
@property
def total_codebooks(self):
"""Total number of quantizer codebooks available.
"""
return self.quantizer.total_codebooks
@property
def num_codebooks(self):
"""Active number of codebooks used by the quantizer.
"""
return self.quantizer.num_codebooks
def set_num_codebooks(self, n: int):
"""Set the active number of codebooks used by the quantizer.
"""
self.quantizer.set_num_codebooks(n)
@property
def cardinality(self):
"""Cardinality of each codebook.
"""
return self.quantizer.bins
def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
scale: tp.Optional[torch.Tensor]
if self.renormalize:
mono = x.mean(dim=1, keepdim=True)
volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
scale = 1e-8 + volume
x = x / scale
scale = scale.view(-1, 1)
else:
scale = None
return x, scale
def postprocess(self,
x: torch.Tensor,
scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
if scale is not None:
assert self.renormalize
x = x * scale.view(-1, 1, 1)
return x
def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
assert x.dim() == 3
length = x.shape[-1]
x, scale = self.preprocess(x)
emb = self.encoder(x)
q_res = self.quantizer(emb, self.frame_rate)
out = self.decoder(q_res.x)
# remove extra padding added by the encoder and decoder
assert out.shape[-1] >= length, (out.shape[-1], length)
out = out[..., :length]
q_res.x = self.postprocess(out, scale)
return q_res
def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
"""Encode the given input tensor to quantized representation along with scale parameter.
Args:
x (torch.Tensor): Float tensor of shape [B, C, T]
Returns:
codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
scale a float tensor containing the scale for audio renormalizealization.
"""
assert x.dim() == 3
x, scale = self.preprocess(x)
emb = self.encoder(x)
codes = self.quantizer.encode(emb)
return codes, scale
def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
"""Decode the given codes to a reconstructed representation, using the scale to perform
audio denormalization if needed.
Args:
codes (torch.Tensor): Int tensor of shape [B, K, T]
scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
Returns:
out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
"""
emb = self.quantizer.decode(codes)
out = self.decoder(emb)
out = self.postprocess(out, scale)
# out contains extra padding added by the encoder and decoder
return out
class FlattenedCompressionModel(CompressionModel):
"""Wraps a CompressionModel and flatten its codebooks, e.g.
instead of returning [B, K, T], return [B, S, T * (K // S)] with
S the number of codebooks per step, and `K // S` the number of 'virtual steps'
for each real time step.
Args:
model (CompressionModel): compression model to wrap.
codebooks_per_step (int): number of codebooks to keep per step,
this must divide the number of codebooks provided by the wrapped model.
extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
if each codebook has a cardinality N, then the first codebook will
use the range [0, N - 1], and the second [N, 2 N - 1] etc.
On decoding, this can lead to potentially invalid sequences.
Any invalid entry will be silently remapped to the proper range
with a modulo.
"""
def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
extend_cardinality: bool = True):
super().__init__()
self.model = model
self.codebooks_per_step = codebooks_per_step
self.extend_cardinality = extend_cardinality
@property
def total_codebooks(self):
return self.model.total_codebooks
@property
def num_codebooks(self):
"""Active number of codebooks used by the quantizer.
..Warning:: this reports the number of codebooks after the flattening
of the codebooks!
"""
assert self.model.num_codebooks % self.codebooks_per_step == 0
return self.codebooks_per_step
def set_num_codebooks(self, n: int):
"""Set the active number of codebooks used by the quantizer.
..Warning:: this sets the number of codebooks **before** the flattening
of the codebooks.
"""
assert n % self.codebooks_per_step == 0
self.model.set_num_codebooks(n)
@property
def num_virtual_steps(self) -> int:
"""Return the number of virtual steps, e.g. one real step
will be split into that many steps.
"""
return self.model.num_codebooks // self.codebooks_per_step
@property
def frame_rate(self) -> int:
return self.model.frame_rate * self.num_virtual_steps
@property
def sample_rate(self) -> int:
return self.model.sample_rate
@property
def channels(self) -> int:
return self.model.channels
@property
def cardinality(self):
"""Cardinality of each codebook.
"""
if self.extend_cardinality:
return self.model.cardinality * self.num_virtual_steps
else:
return self.model.cardinality
def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
raise NotImplementedError("Not supported, use encode and decode.")
def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
indices, scales = self.model.encode(x)
B, K, T = indices.shape
indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
if self.extend_cardinality:
for virtual_step in range(1, self.num_virtual_steps):
indices[..., virtual_step] += self.model.cardinality * virtual_step
indices = rearrange(indices, 'b k t v -> b k (t v)')
return (indices, scales)
def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
B, K, T = codes.shape
assert T % self.num_virtual_steps == 0
codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
# We silently ignore potential errors from the LM when
# using extend_cardinality.
codes = codes % self.model.cardinality
return self.model.decode(codes, scale)