# 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 collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass, field
from itertools import chain
import logging
import random
import re
import typing as tp
import warnings
from einops import rearrange
from num2words import num2words
import spacy
from transformers import T5EncoderModel, T5Tokenizer # type: ignore
import torchaudio
import torch
from torch import nn
from torch import Tensor
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from .streaming import StreamingModule
from .transformer import create_sin_embedding
from ..data.audio_dataset import SegmentInfo
from ..utils.autocast import TorchAutocast
from ..utils.utils import hash_trick, length_to_mask, collate
logger = logging.getLogger(__name__)
TextCondition = tp.Optional[str] # a text condition can be a string or None (if doesn't exist)
ConditionType = tp.Tuple[Tensor, Tensor] # condition, mask
class WavCondition(tp.NamedTuple):
wav: Tensor
length: Tensor
path: tp.List[tp.Optional[str]] = []
def nullify_condition(condition: ConditionType, dim: int = 1):
"""This function transforms an input condition to a null condition.
The way it is done by converting it to a single zero vector similarly
to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
Args:
condition (ConditionType): a tuple of condition and mask (tp.Tuple[Tensor, Tensor])
dim (int): the dimension that will be truncated (should be the time dimension)
WARNING!: dim should not be the batch dimension!
Returns:
ConditionType: a tuple of null condition and mask
"""
assert dim != 0, "dim cannot be the batch dimension!"
assert type(condition) == tuple and \
type(condition[0]) == Tensor and \
type(condition[1]) == Tensor, "'nullify_condition' got an unexpected input type!"
cond, mask = condition
B = cond.shape[0]
last_dim = cond.dim() - 1
out = cond.transpose(dim, last_dim)
out = 0. * out[..., :1]
out = out.transpose(dim, last_dim)
mask = torch.zeros((B, 1), device=out.device).int()
assert cond.dim() == out.dim()
return out, mask
def nullify_wav(wav: Tensor) -> WavCondition:
"""Create a nullified WavCondition from a wav tensor with appropriate shape.
Args:
wav (Tensor): tensor of shape [B, T]
Returns:
WavCondition: wav condition with nullified wav.
"""
null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
return WavCondition(
wav=null_wav,
length=torch.tensor([0] * wav.shape[0], device=wav.device),
path=['null_wav'] * wav.shape[0]
)
@dataclass
class ConditioningAttributes:
text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
def __getitem__(self, item):
return getattr(self, item)
@property
def text_attributes(self):
return self.text.keys()
@property
def wav_attributes(self):
return self.wav.keys()
@property
def attributes(self):
return {"text": self.text_attributes, "wav": self.wav_attributes}
def to_flat_dict(self):
return {
**{f"text.{k}": v for k, v in self.text.items()},
**{f"wav.{k}": v for k, v in self.wav.items()},
}
@classmethod
def from_flat_dict(cls, x):
out = cls()
for k, v in x.items():
kind, att = k.split(".")
out[kind][att] = v
return out
class SegmentWithAttributes(SegmentInfo):
"""Base class for all dataclasses that are used for conditioning.
All child classes should implement `to_condition_attributes` that converts
the existing attributes to a dataclass of type ConditioningAttributes.
"""
def to_condition_attributes(self) -> ConditioningAttributes:
raise NotImplementedError()
class Tokenizer:
"""Base class for all tokenizers
(in case we want to introduce more advances tokenizers in the future).
"""
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
raise NotImplementedError()
class WhiteSpaceTokenizer(Tokenizer):
"""This tokenizer should be used for natural language descriptions.
For example:
["he didn't, know he's going home.", 'shorter sentence'] =>
[[78, 62, 31, 4, 78, 25, 19, 34],
[59, 77, 0, 0, 0, 0, 0, 0]]
"""
PUNCTUATIONS = "?:!.,;"
def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
lemma: bool = True, stopwords: bool = True) -> None:
self.n_bins = n_bins
self.pad_idx = pad_idx
self.lemma = lemma
self.stopwords = stopwords
try:
self.nlp = spacy.load(language)
except IOError:
spacy.cli.download(language) # type: ignore
self.nlp = spacy.load(language)
@tp.no_type_check
def __call__(
self,
texts: tp.List[tp.Optional[str]],
return_text: bool = False
) -> tp.Tuple[Tensor, Tensor]:
"""Take a list of strings and convert them to a tensor of indices.
Args:
texts (tp.List[str]): List of strings.
return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
Returns:
tp.Tuple[Tensor, Tensor]:
- Indices of words in the LUT.
- And a mask indicating where the padding tokens are
"""
output, lengths = [], []
texts = deepcopy(texts)
for i, text in enumerate(texts):
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(Tensor([self.pad_idx]))
lengths.append(0)
continue
# convert numbers to words
text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text) # type: ignore
# normalize text
text = self.nlp(text) # type: ignore
# remove stopwords
if self.stopwords:
text = [w for w in text if not w.is_stop] # type: ignore
# remove punctuations
text = [w for w in text if w.text not in self.PUNCTUATIONS] # type: ignore
# lemmatize if needed
text = [getattr(t, "lemma_" if self.lemma else "text") for t in text] # type: ignore
texts[i] = " ".join(text)
lengths.append(len(text))
# convert to tensor
tokens = Tensor([hash_trick(w, self.n_bins) for w in text])
output.append(tokens)
mask = length_to_mask(torch.IntTensor(lengths)).int()
padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
if return_text:
return padded_output, mask, texts # type: ignore
return padded_output, mask
class NoopTokenizer(Tokenizer):
"""This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
split it to ["Jeff", "Buckley"] and return an index per word.
For example:
["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
["Metal", "Rock", "Classical"] => [0, 223, 51]
"""
def __init__(self, n_bins: int, pad_idx: int = 0):
self.n_bins = n_bins
self.pad_idx = pad_idx
def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
output, lengths = [], []
for text in texts:
# if current sample doesn't have a certain attribute, replace with pad token
if text is None:
output.append(self.pad_idx)
lengths.append(0)
else:
output.append(hash_trick(text, self.n_bins))
lengths.append(1)
tokens = torch.LongTensor(output).unsqueeze(1)
mask = length_to_mask(torch.IntTensor(lengths)).int()
return tokens, mask
class BaseConditioner(nn.Module):
"""Base model for all conditioner modules. We allow the output dim to be different
than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large;
2) make all condition dims consistent.
Args:
dim (int): Hidden dim of the model (text-encoder/LUT).
output_dim (int): Output dim of the conditioner.
"""
def __init__(self, dim, output_dim):
super().__init__()
self.dim = dim
self.output_dim = output_dim
self.output_proj = nn.Linear(dim, output_dim)
def tokenize(self, *args, **kwargs) -> tp.Any:
"""Should be any part of the processing that will lead to a synchronization
point, e.g. BPE tokenization with transfer to the GPU.
The returned value will be saved and return later when calling forward().
"""
raise NotImplementedError()
def forward(self, inputs: tp.Any) -> ConditionType:
"""Gets input that should be used as conditioning (e.g, genre, description or a waveform).
Outputs a ConditionType, after the input data was embedded as a dense vector.
Returns:
ConditionType:
- A tensor of size [B, T, D] where B is the batch size, T is the length of the
output embedding and D is the dimension of the embedding.
- And a mask indicating where the padding tokens.
"""
raise NotImplementedError()
class TextConditioner(BaseConditioner):
...
class LUTConditioner(TextConditioner):
"""Lookup table TextConditioner.
Args:
n_bins (int): Number of bins.
dim (int): Hidden dim of the model (text-encoder/LUT).
output_dim (int): Output dim of the conditioner.
tokenizer (str): Name of the tokenizer.
pad_idx (int, optional): Index for padding token. Defaults to 0.
"""
def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
super().__init__(dim, output_dim)
self.embed = nn.Embedding(n_bins, dim)
self.tokenizer: Tokenizer
if tokenizer == "whitespace":
self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
elif tokenizer == "noop":
self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
else:
raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
device = self.embed.weight.device
tokens, mask = self.tokenizer(x)
tokens, mask = tokens.to(device), mask.to(device)
return tokens, mask
def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
tokens, mask = inputs
embeds = self.embed(tokens)
embeds = self.output_proj(embeds)
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class T5Conditioner(TextConditioner):
"""T5-based TextConditioner.
Args:
name (str): Name of the T5 model.
output_dim (int): Output dim of the conditioner.
finetune (bool): Whether to fine-tune T5 at train time.
device (str): Device for T5 Conditioner.
autocast_dtype (tp.Optional[str], optional): Autocast dtype.
word_dropout (float, optional): Word dropout probability.
normalize_text (bool, optional): Whether to apply text normalization.
"""
MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
"google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
"google/flan-t5-xl", "google/flan-t5-xxl"]
MODELS_DIMS = {
"t5-small": 512,
"t5-base": 768,
"t5-large": 1024,
"t5-3b": 1024,
"t5-11b": 1024,
"google/flan-t5-small": 512,
"google/flan-t5-base": 768,
"google/flan-t5-large": 1024,
"google/flan-t5-3b": 1024,
"google/flan-t5-11b": 1024,
}
def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
normalize_text: bool = False):
assert name in self.MODELS, f"unrecognized t5 model name (should in {self.MODELS})"
super().__init__(self.MODELS_DIMS[name], output_dim)
self.device = device
self.name = name
self.finetune = finetune
self.word_dropout = word_dropout
if autocast_dtype is None or self.device == 'cpu':
self.autocast = TorchAutocast(enabled=False)
if self.device != 'cpu':
logger.warning("T5 has no autocast, this might lead to NaN")
else:
dtype = getattr(torch, autocast_dtype)
assert isinstance(dtype, torch.dtype)
logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
# Let's disable logging temporarily because T5 will vomit some errors otherwise.
# thanks https://gist.github.com/simon-weber/7853144
previous_level = logging.root.manager.disable
logging.disable(logging.ERROR)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
finally:
logging.disable(previous_level)
if finetune:
self.t5 = t5
else:
# this makes sure that the t5 models is not part
# of the saved checkpoint
self.__dict__["t5"] = t5.to(device)
self.normalize_text = normalize_text
if normalize_text:
self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
# if current sample doesn't have a certain attribute, replace with empty string
entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
if self.normalize_text:
_, _, entries = self.text_normalizer(entries, return_text=True)
if self.word_dropout > 0. and self.training:
new_entries = []
for entry in entries:
words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
new_entries.append(" ".join(words))
entries = new_entries
empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
inputs = self.t5_tokenizer(entries, return_tensors="pt", padding=True).to(self.device)
mask = inputs["attention_mask"]
mask[empty_idx, :] = 0 # zero-out index where the input is non-existant
return inputs
def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
mask = inputs["attention_mask"]
with torch.set_grad_enabled(self.finetune), self.autocast:
embeds = self.t5(**inputs).last_hidden_state
embeds = self.output_proj(embeds.to(self.output_proj.weight))
embeds = (embeds * mask.unsqueeze(-1))
return embeds, mask
class WaveformConditioner(BaseConditioner):
"""Base class for all conditioners that take a waveform as input.
Classes that inherit must implement `_get_wav_embedding` that outputs
a continuous tensor, and `_downsampling_factor` that returns the down-sampling
factor of the embedding model.
Args:
dim (int): The internal representation dimension.
output_dim (int): Output dimension.
device (tp.Union[torch.device, str]): Device.
"""
def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
super().__init__(dim, output_dim)
self.device = device
def tokenize(self, wav_length: WavCondition) -> WavCondition:
wav, length, path = wav_length
assert length is not None
return WavCondition(wav.to(self.device), length.to(self.device), path)
def _get_wav_embedding(self, wav: Tensor) -> Tensor:
"""Gets as input a wav and returns a dense vector of conditions."""
raise NotImplementedError()
def _downsampling_factor(self):
"""Returns the downsampling factor of the embedding model."""
raise NotImplementedError()
def forward(self, inputs: WavCondition) -> ConditionType:
"""
Args:
input (WavCondition): Tuple of (waveform, lengths).
Returns:
ConditionType: Dense vector representing the conditioning along with its' mask.
"""
wav, lengths, path = inputs
with torch.no_grad():
embeds = self._get_wav_embedding(wav)
embeds = embeds.to(self.output_proj.weight)
embeds = self.output_proj(embeds)
if lengths is not None:
lengths = lengths / self._downsampling_factor()
mask = length_to_mask(lengths, max_len=embeds.shape[1]).int() # type: ignore
else:
mask = torch.ones_like(embeds)
embeds = (embeds * mask.unsqueeze(2).to(self.device))
return embeds, mask
class ChromaStemConditioner(WaveformConditioner):
"""Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
information about melody.
Args:
output_dim (int): Output dimension for the conditioner.
sample_rate (int): Sample rate for the chroma extractor.
n_chroma (int): Number of chroma for the chroma extractor.
radix2_exp (int): Radix2 exponent for the chroma extractor.
duration (float): Duration used during training. This is later used for correct padding
in case we are using chroma as prefix.
match_len_on_eval (bool, optional): If True then all chromas are padded to the training
duration. Defaults to False.
eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
Defaults to None.
n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
device (tp.Union[torch.device, str], optional): Device for the conditioner.
**kwargs: Additional parameters for the chroma extractor.
"""
def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
duration: float, match_len_on_eval: bool = False, eval_wavs: tp.Optional[str] = None,
n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
from demucs import pretrained
super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
self.sample_rate = sample_rate
self.match_len_on_eval = match_len_on_eval
self.duration = duration
self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
device=device, **kwargs)
self.chroma_len = self._get_chroma_len()
def _downsampling_factor(self):
return self.chroma.winhop
def _get_chroma_len(self):
"""Get length of chroma during training"""
dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
dummy_chr = self.chroma(dummy_wav)
return dummy_chr.shape[1]
@torch.no_grad()
def _get_filtered_wav(self, wav):
from demucs.apply import apply_model
from demucs.audio import convert_audio
with self.autocast:
wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
stems = apply_model(self.demucs, wav, device=self.device)
stems = stems[:, self.stem_idx] # extract stem
stems = stems.sum(1) # merge extracted stems
stems = stems.mean(1, keepdim=True) # mono
stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
return stems
@torch.no_grad()
def _get_wav_embedding(self, wav):
# avoid 0-size tensors when we are working with null conds
if wav.shape[-1] == 1:
return self.chroma(wav)
stems = self._get_filtered_wav(wav)
chroma = self.chroma(stems)
if self.match_len_on_eval:
b, t, c = chroma.shape
if t > self.chroma_len:
chroma = chroma[:, :self.chroma_len]
logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
elif t < self.chroma_len:
chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
return chroma
class ChromaExtractor(nn.Module):
"""Chroma extraction class, handles chroma extraction and quantization.
Args:
sample_rate (int): Sample rate.
n_chroma (int): Number of chroma to consider.
radix2_exp (int): Radix2 exponent.
nfft (tp.Optional[int], optional): Number of FFT.
winlen (tp.Optional[int], optional): Window length.
winhop (tp.Optional[int], optional): Window hop size.
argmax (bool, optional): Whether to use argmax. Defaults to False.
norm (float, optional): Norm for chroma normalization. Defaults to inf.
device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
"""
def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
super().__init__()
from librosa import filters
self.device = device
self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
self.winlen = winlen or 2 ** radix2_exp
self.nfft = nfft or self.winlen
self.winhop = winhop or (self.winlen // 4)
self.sr = sample_rate
self.n_chroma = n_chroma
self.norm = norm
self.argmax = argmax
self.window = torch.hann_window(self.winlen).to(device)
self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
n_chroma=self.n_chroma)).to(device)
self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
hop_length=self.winhop, power=2, center=True,
pad=0, normalized=True).to(device)
def forward(self, wav):
with self.autocast:
T = wav.shape[-1]
# in case we are getting a wav that was dropped out (nullified)
# make sure wav length is no less that nfft
if T < self.nfft:
pad = self.nfft - T
r = 0 if pad % 2 == 0 else 1
wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
spec = self.spec(wav).squeeze(1)
raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
norm_chroma = rearrange(norm_chroma, "b d t -> b t d")
if self.argmax:
idx = norm_chroma.argmax(-1, keepdims=True)
norm_chroma[:] = 0
norm_chroma.scatter_(dim=-1, index=idx, value=1)
return norm_chroma
def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str):
"""Utility function for nullifying an attribute inside an ConditioningAttributes object.
If the condition is of type "wav", then nullify it using "nullify_condition".
If the condition is of any other type, set its' value to None.
Works in-place.
"""
if condition_type not in ["text", "wav"]:
raise ValueError(
"dropout_condition got an unexpected condition type!"
f" expected 'wav' or 'text' but got '{condition_type}'"
)
if condition not in getattr(sample, condition_type):
raise ValueError(
"dropout_condition received an unexpected condition!"
f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
f"but got '{condition}' of type '{condition_type}'!"
)
if condition_type == "wav":
wav, length, path = sample.wav[condition]
sample.wav[condition] = nullify_wav(wav)
else:
sample.text[condition] = None
return sample
class DropoutModule(nn.Module):
"""Base class for all dropout modules."""
def __init__(self, seed: int = 1234):
super().__init__()
self.rng = torch.Generator()
self.rng.manual_seed(seed)
class AttributeDropout(DropoutModule):
"""Applies dropout with a given probability per attribute. This is different from the behavior of
ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example,
"artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout
where if "artist" is dropped "genre" must also be dropped.
Args:
p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
...
"genre": 0.1,
"artist": 0.5,
"wav": 0.25,
...
active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
seed (int, optional): Random seed.
"""
def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
super().__init__(seed=seed)
self.active_on_eval = active_on_eval
# construct dict that return the values from p otherwise 0
self.p = {}
for condition_type, probs in p.items():
self.p[condition_type] = defaultdict(lambda: 0, probs)
def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
"""
Args:
samples (tp.List[ConditioningAttributes]): List of conditions.
Returns:
tp.List[ConditioningAttributes]: List of conditions after certain attributes were set to None.
"""
if not self.training and not self.active_on_eval:
return samples
samples = deepcopy(samples)
for condition_type, ps in self.p.items(): # for condition types [text, wav]
for condition, p in ps.items(): # for attributes of each type (e.g., [artist, genre])
if torch.rand(1, generator=self.rng).item() < p:
for sample in samples:
dropout_condition(sample, condition_type, condition)
return samples
def __repr__(self):
return f"AttributeDropout({dict(self.p)})"
class ClassifierFreeGuidanceDropout(DropoutModule):
"""Applies Classifier Free Guidance dropout, meaning all attributes
are dropped with the same probability.
Args:
p (float): Probability to apply condition dropout during training.
seed (int): Random seed.
"""
def __init__(self, p: float, seed: int = 1234):
super().__init__(seed=seed)
self.p = p
def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
"""
Args:
samples (tp.List[ConditioningAttributes]): List of conditions.
Returns:
tp.List[ConditioningAttributes]: List of conditions after all attributes were set to None.
"""
if not self.training:
return samples
# decide on which attributes to drop in a batched fashion
drop = torch.rand(1, generator=self.rng).item() < self.p
if not drop:
return samples
# nullify conditions of all attributes
samples = deepcopy(samples)
for condition_type in ["wav", "text"]:
for sample in samples:
for condition in sample.attributes[condition_type]:
dropout_condition(sample, condition_type, condition)
return samples
def __repr__(self):
return f"ClassifierFreeGuidanceDropout(p={self.p})"
class ConditioningProvider(nn.Module):
"""Main class to provide conditions given all the supported conditioners.
Args:
conditioners (dict): Dictionary of conditioners.
merge_text_conditions_p (float, optional): Probability to merge all text sources
into a single text condition. Defaults to 0.
drop_desc_p (float, optional): Probability to drop the original description
when merging all text sources into a single text condition. Defaults to 0.
device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
"""
def __init__(
self,
conditioners: tp.Dict[str, BaseConditioner],
merge_text_conditions_p: float = 0,
drop_desc_p: float = 0,
device: tp.Union[torch.device, str] = "cpu",
):
super().__init__()
self.device = device
self.merge_text_conditions_p = merge_text_conditions_p
self.drop_desc_p = drop_desc_p
self.conditioners = nn.ModuleDict(conditioners)
@property
def text_conditions(self):
return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
@property
def wav_conditions(self):
return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
@property
def has_wav_condition(self):
return len(self.wav_conditions) > 0
def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
"""Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
This should be called before starting any real GPU work to avoid synchronization points.
This will return a dict matching conditioner names to their arbitrary tokenized representations.
Args:
inputs (list[ConditioningAttribres]): List of ConditioningAttributes objects containing
text and wav conditions.
"""
assert all([type(x) == ConditioningAttributes for x in inputs]), \
"got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]" \
f" but types were {set([type(x) for x in inputs])}"
output = {}
text = self._collate_text(inputs)
wavs = self._collate_wavs(inputs)
assert set(text.keys() | wavs.keys()).issubset(set(self.conditioners.keys())), \
f"got an unexpected attribute! Expected {self.conditioners.keys()}, got {text.keys(), wavs.keys()}"
for attribute, batch in chain(text.items(), wavs.items()):
output[attribute] = self.conditioners[attribute].tokenize(batch)
return output
def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
"""Compute pairs of `(embedding, mask)` using the configured conditioners
and the tokenized representations. The output is for example:
{
"genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
"description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
...
}
Args:
tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
"""
output = {}
for attribute, inputs in tokenized.items():
condition, mask = self.conditioners[attribute](inputs)
output[attribute] = (condition, mask)
return output
def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
"""Given a list of ConditioningAttributes objects, compile a dictionary where the keys
are the attributes and the values are the aggregated input per attribute.
For example:
Input:
[
ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
]
Output:
{
"genre": ["Rock", "Hip-hop"],
"description": ["A rock song with a guitar solo", "A hip-hop verse"]
}
"""
batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
def is_valid(k, v):
k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
v_valid = v is not None and isinstance(v, (int, float, str, list))
return k_valid and v_valid
def process_value(v):
if isinstance(v, (int, float, str)):
return v
if isinstance(v, list):
return ", ".join(v)
else:
RuntimeError(f"unknown type for text value! ({type(v), v})")
desc = cond.text['description']
meta_data = ""
if random.uniform(0, 1) < merge_text_conditions_p:
meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
random.shuffle(meta_pairs)
meta_data = ". ".join(meta_pairs)
desc = desc if not random.uniform(0, 1) < drop_desc_p else None
if desc is None:
desc = meta_data if len(meta_data) > 1 else None
else:
desc = desc.rstrip('.') + ". " + meta_data
cond.text['description'] = desc.strip() if desc else None
if self.training and self.merge_text_conditions_p:
for sample in samples:
_merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)
texts = [x.text for x in samples]
for text in texts:
for condition in self.text_conditions:
batch_per_attribute[condition].append(text[condition])
return batch_per_attribute
def _collate_wavs(self, samples: tp.List[ConditioningAttributes]):
"""Generate a dict where the keys are attributes by which we fetch similar wavs,
and the values are Tensors of wavs according to said attribtues.
*Note*: by the time the samples reach this function, each sample should have some waveform
inside the "wav" attribute. It should be either:
1. A real waveform
2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
Args:
samples (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
Returns:
dict: A dicionary mapping an attribute name to wavs.
"""
wavs = defaultdict(list)
lens = defaultdict(list)
paths = defaultdict(list)
out = {}
for sample in samples:
for attribute in self.wav_conditions:
wav, length, path = sample.wav[attribute]
wavs[attribute].append(wav.flatten())
lens[attribute].append(length)
paths[attribute].append(path)
# stack all wavs to a single tensor
for attribute in self.wav_conditions:
stacked_wav, _ = collate(wavs[attribute], dim=0)
out[attribute] = WavCondition(stacked_wav.unsqueeze(1),
torch.cat(lens['self_wav']), paths[attribute]) # type: ignore
return out
class ConditionFuser(StreamingModule):
"""Condition fuser handles the logic to combine the different conditions
to the actual model input.
Args:
fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
each condition. For example:
{
"prepend": ["description"],
"sum": ["genre", "bpm"],
"cross": ["description"],
}
cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
"""
FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
cross_attention_pos_emb_scale: float = 1.0):
super().__init__()
assert all(
[k in self.FUSING_METHODS for k in fuse2cond.keys()]
), f"got invalid fuse method, allowed methods: {self.FUSING_MEHTODS}"
self.cross_attention_pos_emb = cross_attention_pos_emb
self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
self.cond2fuse: tp.Dict[str, str] = {}
for fuse_method, conditions in fuse2cond.items():
for condition in conditions:
self.cond2fuse[condition] = fuse_method
def forward(
self,
input: Tensor,
conditions: tp.Dict[str, ConditionType]
) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
"""Fuse the conditions to the provided model input.
Args:
input (Tensor): Transformer input.
conditions (tp.Dict[str, ConditionType]): Dict of conditions.
Returns:
tp.Tuple[Tensor, Tensor]: The first tensor is the transformer input
after the conditions have been fused. The second output tensor is the tensor
used for cross-attention or None if no cross attention inputs exist.
"""
B, T, _ = input.shape
if 'offsets' in self._streaming_state:
first_step = False
offsets = self._streaming_state['offsets']
else:
first_step = True
offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
f"given conditions contain unknown attributes for fuser, " \
f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
cross_attention_output = None
for cond_type, (cond, cond_mask) in conditions.items():
op = self.cond2fuse[cond_type]
if op == "sum":
input += cond
elif op == "input_interpolate":
cond = rearrange(cond, "b t d -> b d t")
cond = F.interpolate(cond, size=input.shape[1])
input += rearrange(cond, "b d t -> b t d")
elif op == "prepend":
if first_step:
input = torch.cat([cond, input], dim=1)
elif op == "cross":
if cross_attention_output is not None:
cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
else:
cross_attention_output = cond
else:
raise ValueError(f"unknown op ({op})")
if self.cross_attention_pos_emb and cross_attention_output is not None:
positions = torch.arange(
cross_attention_output.shape[1],
device=cross_attention_output.device
).view(1, -1, 1)
pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
if self._is_streaming:
self._streaming_state['offsets'] = offsets + T
return input, cross_attention_output