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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.
"""Compression models or wrapper around existing models.
Also defines the main interface that a model must follow to be usable as an audio tokenizer.
"""
from abc import ABC, abstractmethod
import logging
import math
from pathlib import Path
import typing as tp
import numpy as np
import torch
from torch import nn
from transformers import EncodecModel as HFEncodecModel
from .. import quantization as qt
logger = logging.getLogger()
class CompressionModel(ABC, nn.Module):
"""Base API for all compression model that aim at being used as audio tokenizers
with a language model.
"""
@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`."""
...
@abstractmethod
def decode_latent(self, codes: torch.Tensor):
"""Decode from the discrete codes to continuous latent space."""
...
@property
@abstractmethod
def channels(self) -> int:
...
@property
@abstractmethod
def frame_rate(self) -> float:
...
@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."""
...
@staticmethod
def get_pretrained(
name: str, device: tp.Union[torch.device, str] = 'cpu'
) -> 'CompressionModel':
"""Instantiate a CompressionModel from a given pretrained model.
Args:
name (Path or str): name of the pretrained model. See after.
device (torch.device or str): Device on which the model is loaded.
Pretrained models:
- dac_44khz (https://github.com/descriptinc/descript-audio-codec)
- dac_24khz (same)
- facebook/encodec_24khz (https://huggingface.co/facebook/encodec_24khz)
- facebook/encodec_32khz (https://huggingface.co/facebook/encodec_32khz)
- your own model on HugginFace. Export instructions to come...
"""
from . import builders, loaders
model: CompressionModel
if name in ['dac_44khz', 'dac_24khz']:
model_type = name.split('_')[1]
logger.info("Getting pretrained compression model from DAC %s", model_type)
model = DAC(model_type)
elif name in ['debug_compression_model']:
logger.info("Getting pretrained compression model for debug")
model = builders.get_debug_compression_model()
elif Path(name).exists():
# We assume here if the paths exist that it is in fact an AC checkpoint
# that was exported using `audiocraft.utils.export` functions.
model = loaders.load_compression_model(name, device=device)
else:
logger.info("Getting pretrained compression model from HF %s", name)
hf_model = HFEncodecModel.from_pretrained(name)
model = HFEncodecCompressionModel(hf_model).to(device)
return model.to(device).eval()
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 assignment to override the property in the abstract class,
# I couldn't find a better way...
frame_rate: float = 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 (tuple of 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 (torch.Tensor, optional): Float tensor containing the scale value.
Returns:
out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
"""
emb = self.decode_latent(codes)
out = self.decoder(emb)
out = self.postprocess(out, scale)
# out contains extra padding added by the encoder and decoder
return out
def decode_latent(self, codes: torch.Tensor):
"""Decode from the discrete codes to continuous latent space."""
return self.quantizer.decode(codes)
class DAC(CompressionModel):
def __init__(self, model_type: str = "44khz"):
super().__init__()
try:
import dac.utils
except ImportError:
raise RuntimeError("Could not import dac, make sure it is installed, "
"please run `pip install descript-audio-codec`")
self.model = dac.utils.load_model(model_type=model_type)
self.n_quantizers = self.total_codebooks
def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
# We don't support training with this.
raise NotImplementedError("Forward and training with DAC not supported.")
def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
codes = self.model.encode(x, self.n_quantizers)[1]
return codes, None
def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
assert scale is None
z_q = self.decode_latent(codes)
return self.model.decode(z_q)
def decode_latent(self, codes: torch.Tensor):
"""Decode from the discrete codes to continuous latent space."""
return self.model.quantizer.from_codes(codes)[0]
@property
def channels(self) -> int:
return 1
@property
def frame_rate(self) -> float:
return self.model.sample_rate / self.model.hop_length
@property
def sample_rate(self) -> int:
return self.model.sample_rate
@property
def cardinality(self) -> int:
return self.model.codebook_size
@property
def num_codebooks(self) -> int:
return self.n_quantizers
@property
def total_codebooks(self) -> int:
return self.model.n_codebooks
def set_num_codebooks(self, n: int):
"""Set the active number of codebooks used by the quantizer.
"""
assert n >= 1
assert n <= self.total_codebooks
self.n_quantizers = n
class HFEncodecCompressionModel(CompressionModel):
"""Wrapper around HuggingFace Encodec.
"""
def __init__(self, model: HFEncodecModel):
super().__init__()
self.model = model
bws = self.model.config.target_bandwidths
num_codebooks = [
bw * 1000 / (self.frame_rate * math.log2(self.cardinality))
for bw in bws
]
deltas = [nc - int(nc) for nc in num_codebooks]
# Checking we didn't do some bad maths and we indeed have integers!
assert all(deltas) <= 1e-3, deltas
self.possible_num_codebooks = [int(nc) for nc in num_codebooks]
self.set_num_codebooks(max(self.possible_num_codebooks))
def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
# We don't support training with this.
raise NotImplementedError("Forward and training with HF EncodecModel not supported.")
def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
bandwidth_index = self.possible_num_codebooks.index(self.num_codebooks)
bandwidth = self.model.config.target_bandwidths[bandwidth_index]
res = self.model.encode(x, None, bandwidth)
assert len(res[0]) == 1
assert len(res[1]) == 1
return res[0][0], res[1][0]
def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
if scale is None:
scales = [None] # type: ignore
else:
scales = scale # type: ignore
res = self.model.decode(codes[None], scales)
return res[0]
def decode_latent(self, codes: torch.Tensor):
"""Decode from the discrete codes to continuous latent space."""
return self.model.quantizer.decode(codes.transpose(0, 1))
@property
def channels(self) -> int:
return self.model.config.audio_channels
@property
def frame_rate(self) -> float:
hop_length = int(np.prod(self.model.config.upsampling_ratios))
return self.sample_rate / hop_length
@property
def sample_rate(self) -> int:
return self.model.config.sampling_rate
@property
def cardinality(self) -> int:
return self.model.config.codebook_size
@property
def num_codebooks(self) -> int:
return self._num_codebooks
@property
def total_codebooks(self) -> int:
return max(self.possible_num_codebooks)
def set_num_codebooks(self, n: int):
"""Set the active number of codebooks used by the quantizer.
"""
if n not in self.possible_num_codebooks:
raise ValueError(f"Allowed values for num codebooks: {self.possible_num_codebooks}")
self._num_codebooks = n
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