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import torch
import logging
import torch.nn as nn
from audioldm2.clap.open_clip import create_model
from audioldm2.clap.training.data import get_audio_features
import torchaudio
from transformers import RobertaTokenizer, AutoTokenizer, T5EncoderModel
import torch.nn.functional as F
from audioldm2.latent_diffusion.modules.audiomae.AudioMAE import Vanilla_AudioMAE
from audioldm2.latent_diffusion.modules.phoneme_encoder.encoder import TextEncoder
from transformers import AutoTokenizer, T5Config
from audioldm2.audiomae_gen.sequence_input import Sequence2AudioMAE
import numpy as np
"""
The model forward function can return three types of data:
1. tensor: used directly as conditioning signal
2. dict: where there is a main key as condition, there are also other key that you can use to pass loss function and itermediate result. etc.
3. list: the length is 2, in which the first element is tensor, the second element is attntion mask.
The output shape for the cross attention condition should be:
x,x_mask = [bs, seq_len, emb_dim], [bs, seq_len]
All the returned data, in which will be used as diffusion input, will need to be in float type
"""
class PhonemeEncoder(nn.Module):
def __init__(self, vocabs_size=41, pad_length=250, pad_token_id=None):
super().__init__()
"""
encoder = PhonemeEncoder(40)
data = torch.randint(0, 39, (2, 250))
output = encoder(data)
import ipdb;ipdb.set_trace()
"""
assert pad_token_id is not None
self.device = None
self.PAD_LENGTH = int(pad_length)
self.pad_token_id = pad_token_id
self.pad_token_sequence = torch.tensor([self.pad_token_id] * self.PAD_LENGTH)
self.text_encoder = TextEncoder(
n_vocab=vocabs_size,
out_channels=192,
hidden_channels=192,
filter_channels=768,
n_heads=2,
n_layers=6,
kernel_size=3,
p_dropout=0.1,
)
self.learnable_positional_embedding = torch.nn.Parameter(
torch.zeros((1, 192, self.PAD_LENGTH))
) # [batchsize, seqlen, padlen]
self.learnable_positional_embedding.requires_grad = True
# Required
def get_unconditional_condition(self, batchsize):
unconditional_tokens = self.pad_token_sequence.expand(
batchsize, self.PAD_LENGTH
)
return self(unconditional_tokens) # Need to return float type
# def get_unconditional_condition(self, batchsize):
# hidden_state = torch.zeros((batchsize, self.PAD_LENGTH, 192)).to(self.device)
# attention_mask = torch.ones((batchsize, self.PAD_LENGTH)).to(self.device)
# return [hidden_state, attention_mask] # Need to return float type
def _get_src_mask(self, phoneme):
src_mask = phoneme != self.pad_token_id
return src_mask
def _get_src_length(self, phoneme):
src_mask = self._get_src_mask(phoneme)
length = torch.sum(src_mask, dim=-1)
return length
# def make_empty_condition_unconditional(self, src_length, text_emb, attention_mask):
# # src_length: [bs]
# # text_emb: [bs, 192, pad_length]
# # attention_mask: [bs, pad_length]
# mask = src_length[..., None, None] > 1
# text_emb = text_emb * mask
# attention_mask[src_length < 1] = attention_mask[src_length < 1] * 0.0 + 1.0
# return text_emb, attention_mask
def forward(self, phoneme_idx):
if self.device is None:
self.device = self.learnable_positional_embedding.device
self.pad_token_sequence = self.pad_token_sequence.to(self.device)
phoneme_idx = phoneme_idx.to(self.device)
src_length = self._get_src_length(phoneme_idx)
text_emb, m, logs, text_emb_mask = self.text_encoder(phoneme_idx, src_length)
text_emb = text_emb + self.learnable_positional_embedding
# text_emb, text_emb_mask = self.make_empty_condition_unconditional(src_length, text_emb, text_emb_mask)
return [
text_emb.permute(0, 2, 1),
text_emb_mask.squeeze(1),
] # [2, 250, 192], [2, 250]
class FlanT5HiddenState(nn.Module):
"""
llama = FlanT5HiddenState()
data = ["","this is not an empty sentence"]
encoder_hidden_states = llama(data)
import ipdb;ipdb.set_trace()
"""
def __init__(
self, text_encoder_name="google/flan-t5-large", freeze_text_encoder=True
):
super().__init__()
self.freeze_text_encoder = freeze_text_encoder
self.tokenizer = AutoTokenizer.from_pretrained(text_encoder_name)
self.model = T5EncoderModel(T5Config.from_pretrained(text_encoder_name))
if freeze_text_encoder:
self.model.eval()
for p in self.model.parameters():
p.requires_grad = False
else:
print("=> The text encoder is learnable")
self.empty_hidden_state_cfg = None
self.device = None
# Required
def get_unconditional_condition(self, batchsize):
param = next(self.model.parameters())
if self.freeze_text_encoder:
assert param.requires_grad == False
# device = param.device
if self.empty_hidden_state_cfg is None:
self.empty_hidden_state_cfg, _ = self([""])
hidden_state = torch.cat([self.empty_hidden_state_cfg] * batchsize).float()
attention_mask = (
torch.ones((batchsize, hidden_state.size(1)))
.to(hidden_state.device)
.float()
)
return [hidden_state, attention_mask] # Need to return float type
def forward(self, batch):
param = next(self.model.parameters())
if self.freeze_text_encoder:
assert param.requires_grad == False
if self.device is None:
self.device = param.device
# print("Manually change text")
# for i in range(len(batch)):
# batch[i] = "dog barking"
try:
return self.encode_text(batch)
except Exception as e:
print(e, batch)
logging.exception("An error occurred: %s", str(e))
def encode_text(self, prompt):
device = self.model.device
batch = self.tokenizer(
prompt,
max_length=128, # self.tokenizer.model_max_length
padding=True,
truncation=True,
return_tensors="pt",
)
input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(
device
)
# Get text encoding
if self.freeze_text_encoder:
with torch.no_grad():
encoder_hidden_states = self.model(
input_ids=input_ids, attention_mask=attention_mask
)[0]
else:
encoder_hidden_states = self.model(
input_ids=input_ids, attention_mask=attention_mask
)[0]
return [
encoder_hidden_states.detach(),
attention_mask.float(),
] # Attention mask == 1 means usable token
class SequenceGenAudioMAECond(Sequence2AudioMAE):
def __init__(
self,
cond_stage_config,
base_learning_rate,
sequence_gen_length,
sequence_input_key,
sequence_input_embed_dim,
batchsize,
always_output_audiomae_gt=False,
pretrained_path=None,
force_reload_pretrain_avoid_overwrite=False,
learnable=True,
use_warmup=True,
device=None,
use_gt_mae_output=None, # False: does not use AudioMAE GT, True: Use AudioMAE GT
use_gt_mae_prob=None,
): # The prob of using AudioMAE GT
if use_warmup:
use_warmup = False
super().__init__(
base_learning_rate=base_learning_rate,
cond_stage_config=cond_stage_config,
sequence_gen_length=sequence_gen_length,
sequence_input_key=sequence_input_key,
use_warmup=use_warmup,
sequence_input_embed_dim=sequence_input_embed_dim,
batchsize=batchsize,
)
assert use_gt_mae_output is not None and use_gt_mae_prob is not None
self.always_output_audiomae_gt = always_output_audiomae_gt
self.force_reload_pretrain_avoid_overwrite = (
force_reload_pretrain_avoid_overwrite
)
self.pretrained_path = pretrained_path
self.device = device
if self.force_reload_pretrain_avoid_overwrite:
self.is_reload = False
else:
self.is_reload = True
self.load_pretrain_model()
self.use_gt_mae_output = use_gt_mae_output
self.use_gt_mae_prob = use_gt_mae_prob
self.learnable = learnable
if not learnable:
# Only optimize the GPT2 model
for p in self.model.parameters():
p.requires_grad = False
self.eval()
def load_pretrain_model(self):
if self.pretrained_path is not None:
print("Reload SequenceGenAudioMAECond from %s" % self.pretrained_path)
state_dict = torch.load(self.pretrained_path)["state_dict"]
self.load_state_dict(state_dict)
# Required
def get_unconditional_condition(self, batchsize):
return_dict = self.cfg_uncond(batchsize)
return_dict["crossattn_audiomae_generated"] = [
return_dict["crossattn_audiomae_pooled"][0],
torch.ones_like(return_dict["crossattn_audiomae_pooled"][1]).float(),
]
return return_dict
def forward(self, batch):
# The conditional module can return both tensor or dictionaries
# The returned tensor will be corresponding to the cond_stage_key
# The returned dict will have keys that correspond to the cond_stage_key
ret_dict = {}
if self.force_reload_pretrain_avoid_overwrite and not self.is_reload:
self.load_pretrain_model()
self.is_reload = True
# if(self.always_output_audiomae_gt or (self.use_gt_mae_output and torch.rand(1).item() < self.use_gt_mae_prob)):
# cond_dict = self.get_input(batch)
# ret_dict["crossattn_audiomae_generated"] = [cond_dict["crossattn_audiomae_pooled"][0], torch.ones_like(cond_dict["crossattn_audiomae_pooled"][1]).float()] # Input sequence and mask
# else:
input_embeds, cond_dict = self.generate(batch)
input_embeds_mask = (
torch.ones((input_embeds.size(0), input_embeds.size(1)))
.to(input_embeds.device)
.float()
)
ret_dict["crossattn_audiomae_generated"] = [
input_embeds,
input_embeds_mask,
] # Input sequence and mask
# If the following two keys are not in cond_stage_key, then they will not be used as condition
for key in cond_dict.keys():
ret_dict[key] = cond_dict[key]
return ret_dict
class AudioMAEConditionCTPoolRandTFSeparated(nn.Module):
"""
audiomae = AudioMAEConditionCTPool2x2()
data = torch.randn((4, 1024, 128))
output = audiomae(data)
import ipdb;ipdb.set_trace()
exit(0)
"""
def __init__(
self,
time_pooling_factors=[1, 2, 4, 8],
freq_pooling_factors=[1, 2, 4, 8],
eval_time_pooling=None,
eval_freq_pooling=None,
mask_ratio=0.0,
regularization=False,
no_audiomae_mask=True,
no_audiomae_average=False,
):
super().__init__()
self.device = None
self.time_pooling_factors = time_pooling_factors
self.freq_pooling_factors = freq_pooling_factors
self.no_audiomae_mask = no_audiomae_mask
self.no_audiomae_average = no_audiomae_average
self.eval_freq_pooling = eval_freq_pooling
self.eval_time_pooling = eval_time_pooling
self.mask_ratio = mask_ratio
self.use_reg = regularization
self.audiomae = Vanilla_AudioMAE()
self.audiomae.eval()
for p in self.audiomae.parameters():
p.requires_grad = False
# Required
def get_unconditional_condition(self, batchsize):
param = next(self.audiomae.parameters())
assert param.requires_grad == False
device = param.device
# time_pool, freq_pool = max(self.time_pooling_factors), max(self.freq_pooling_factors)
time_pool, freq_pool = min(self.eval_time_pooling, 64), min(
self.eval_freq_pooling, 8
)
# time_pool = self.time_pooling_factors[np.random.choice(list(range(len(self.time_pooling_factors))))]
# freq_pool = self.freq_pooling_factors[np.random.choice(list(range(len(self.freq_pooling_factors))))]
token_num = int(512 / (time_pool * freq_pool))
return [
torch.zeros((batchsize, token_num, 768)).to(device).float(),
torch.ones((batchsize, token_num)).to(device).float(),
]
def pool(self, representation, time_pool=None, freq_pool=None):
assert representation.size(-1) == 768
representation = representation[:, 1:, :].transpose(1, 2)
bs, embedding_dim, token_num = representation.size()
representation = representation.reshape(bs, embedding_dim, 64, 8)
if self.training:
if time_pool is None and freq_pool is None:
time_pool = min(
64,
self.time_pooling_factors[
np.random.choice(list(range(len(self.time_pooling_factors))))
],
)
freq_pool = min(
8,
self.freq_pooling_factors[
np.random.choice(list(range(len(self.freq_pooling_factors))))
],
)
# freq_pool = min(8, time_pool) # TODO here I make some modification.
else:
time_pool, freq_pool = min(self.eval_time_pooling, 64), min(
self.eval_freq_pooling, 8
)
self.avgpooling = nn.AvgPool2d(
kernel_size=(time_pool, freq_pool), stride=(time_pool, freq_pool)
)
self.maxpooling = nn.MaxPool2d(
kernel_size=(time_pool, freq_pool), stride=(time_pool, freq_pool)
)
pooled = (
self.avgpooling(representation) + self.maxpooling(representation)
) / 2 # [bs, embedding_dim, time_token_num, freq_token_num]
pooled = pooled.flatten(2).transpose(1, 2)
return pooled # [bs, token_num, embedding_dim]
def regularization(self, x):
assert x.size(-1) == 768
x = F.normalize(x, p=2, dim=-1)
return x
# Required
def forward(self, batch, time_pool=None, freq_pool=None):
assert batch.size(-2) == 1024 and batch.size(-1) == 128
if self.device is None:
self.device = batch.device
batch = batch.unsqueeze(1)
with torch.no_grad():
representation = self.audiomae(
batch,
mask_ratio=self.mask_ratio,
no_mask=self.no_audiomae_mask,
no_average=self.no_audiomae_average,
)
representation = self.pool(representation, time_pool, freq_pool)
if self.use_reg:
representation = self.regularization(representation)
return [
representation,
torch.ones((representation.size(0), representation.size(1)))
.to(representation.device)
.float(),
]
class AudioMAEConditionCTPoolRand(nn.Module):
"""
audiomae = AudioMAEConditionCTPool2x2()
data = torch.randn((4, 1024, 128))
output = audiomae(data)
import ipdb;ipdb.set_trace()
exit(0)
"""
def __init__(
self,
time_pooling_factors=[1, 2, 4, 8],
freq_pooling_factors=[1, 2, 4, 8],
eval_time_pooling=None,
eval_freq_pooling=None,
mask_ratio=0.0,
regularization=False,
no_audiomae_mask=True,
no_audiomae_average=False,
):
super().__init__()
self.device = None
self.time_pooling_factors = time_pooling_factors
self.freq_pooling_factors = freq_pooling_factors
self.no_audiomae_mask = no_audiomae_mask
self.no_audiomae_average = no_audiomae_average
self.eval_freq_pooling = eval_freq_pooling
self.eval_time_pooling = eval_time_pooling
self.mask_ratio = mask_ratio
self.use_reg = regularization
self.audiomae = Vanilla_AudioMAE()
self.audiomae.eval()
for p in self.audiomae.parameters():
p.requires_grad = False
# Required
def get_unconditional_condition(self, batchsize):
param = next(self.audiomae.parameters())
assert param.requires_grad == False
device = param.device
# time_pool, freq_pool = max(self.time_pooling_factors), max(self.freq_pooling_factors)
time_pool, freq_pool = min(self.eval_time_pooling, 64), min(
self.eval_freq_pooling, 8
)
# time_pool = self.time_pooling_factors[np.random.choice(list(range(len(self.time_pooling_factors))))]
# freq_pool = self.freq_pooling_factors[np.random.choice(list(range(len(self.freq_pooling_factors))))]
token_num = int(512 / (time_pool * freq_pool))
return [
torch.zeros((batchsize, token_num, 768)).to(device).float(),
torch.ones((batchsize, token_num)).to(device).float(),
]
def pool(self, representation, time_pool=None, freq_pool=None):
assert representation.size(-1) == 768
representation = representation[:, 1:, :].transpose(1, 2)
bs, embedding_dim, token_num = representation.size()
representation = representation.reshape(bs, embedding_dim, 64, 8)
if self.training:
if time_pool is None and freq_pool is None:
time_pool = min(
64,
self.time_pooling_factors[
np.random.choice(list(range(len(self.time_pooling_factors))))
],
)
# freq_pool = self.freq_pooling_factors[np.random.choice(list(range(len(self.freq_pooling_factors))))]
freq_pool = min(8, time_pool) # TODO here I make some modification.
else:
time_pool, freq_pool = min(self.eval_time_pooling, 64), min(
self.eval_freq_pooling, 8
)
self.avgpooling = nn.AvgPool2d(
kernel_size=(time_pool, freq_pool), stride=(time_pool, freq_pool)
)
self.maxpooling = nn.MaxPool2d(
kernel_size=(time_pool, freq_pool), stride=(time_pool, freq_pool)
)
pooled = (
self.avgpooling(representation) + self.maxpooling(representation)
) / 2 # [bs, embedding_dim, time_token_num, freq_token_num]
pooled = pooled.flatten(2).transpose(1, 2)
return pooled # [bs, token_num, embedding_dim]
def regularization(self, x):
assert x.size(-1) == 768
x = F.normalize(x, p=2, dim=-1)
return x
# Required
def forward(self, batch, time_pool=None, freq_pool=None):
assert batch.size(-2) == 1024 and batch.size(-1) == 128
if self.device is None:
self.device = next(self.audiomae.parameters()).device
batch = batch.unsqueeze(1).to(self.device)
with torch.no_grad():
representation = self.audiomae(
batch,
mask_ratio=self.mask_ratio,
no_mask=self.no_audiomae_mask,
no_average=self.no_audiomae_average,
)
representation = self.pool(representation, time_pool, freq_pool)
if self.use_reg:
representation = self.regularization(representation)
return [
representation,
torch.ones((representation.size(0), representation.size(1)))
.to(representation.device)
.float(),
]
class CLAPAudioEmbeddingClassifierFreev2(nn.Module):
def __init__(
self,
pretrained_path="",
enable_cuda=False,
sampling_rate=16000,
embed_mode="audio",
amodel="HTSAT-base",
unconditional_prob=0.1,
random_mute=False,
max_random_mute_portion=0.5,
training_mode=True,
):
super().__init__()
self.device = "cpu" # The model itself is on cpu
self.cuda = enable_cuda
self.precision = "fp32"
self.amodel = amodel # or 'PANN-14'
self.tmodel = "roberta" # the best text encoder in our training
self.enable_fusion = False # False if you do not want to use the fusion model
self.fusion_type = "aff_2d"
self.pretrained = pretrained_path
self.embed_mode = embed_mode
self.embed_mode_orig = embed_mode
self.sampling_rate = sampling_rate
self.unconditional_prob = unconditional_prob
self.random_mute = random_mute
self.tokenize = RobertaTokenizer.from_pretrained("roberta-base")
self.max_random_mute_portion = max_random_mute_portion
self.training_mode = training_mode
self.model, self.model_cfg = create_model(
self.amodel,
self.tmodel,
self.pretrained,
precision=self.precision,
device=self.device,
enable_fusion=self.enable_fusion,
fusion_type=self.fusion_type,
)
self.model = self.model.to(self.device)
audio_cfg = self.model_cfg["audio_cfg"]
self.mel_transform = torchaudio.transforms.MelSpectrogram(
sample_rate=audio_cfg["sample_rate"],
n_fft=audio_cfg["window_size"],
win_length=audio_cfg["window_size"],
hop_length=audio_cfg["hop_size"],
center=True,
pad_mode="reflect",
power=2.0,
norm=None,
onesided=True,
n_mels=64,
f_min=audio_cfg["fmin"],
f_max=audio_cfg["fmax"],
)
for p in self.model.parameters():
p.requires_grad = False
self.unconditional_token = None
self.model.eval()
def get_unconditional_condition(self, batchsize):
self.unconditional_token = self.model.get_text_embedding(
self.tokenizer(["", ""])
)[0:1]
return torch.cat([self.unconditional_token.unsqueeze(0)] * batchsize, dim=0)
def batch_to_list(self, batch):
ret = []
for i in range(batch.size(0)):
ret.append(batch[i])
return ret
def make_decision(self, probability):
if float(torch.rand(1)) < probability:
return True
else:
return False
def random_uniform(self, start, end):
val = torch.rand(1).item()
return start + (end - start) * val
def _random_mute(self, waveform):
# waveform: [bs, t-steps]
t_steps = waveform.size(-1)
for i in range(waveform.size(0)):
mute_size = int(
self.random_uniform(0, end=int(t_steps * self.max_random_mute_portion))
)
mute_start = int(self.random_uniform(0, t_steps - mute_size))
waveform[i, mute_start : mute_start + mute_size] = 0
return waveform
def cos_similarity(self, waveform, text):
# waveform: [bs, t_steps]
original_embed_mode = self.embed_mode
with torch.no_grad():
self.embed_mode = "audio"
# MPS currently does not support ComplexFloat dtype and operator 'aten::_fft_r2c'
if self.cuda:
audio_emb = self(waveform.cuda())
else:
audio_emb = self(waveform.to("cpu"))
self.embed_mode = "text"
text_emb = self(text)
similarity = F.cosine_similarity(audio_emb, text_emb, dim=2)
self.embed_mode = original_embed_mode
return similarity.squeeze()
def build_unconditional_emb(self):
self.unconditional_token = self.model.get_text_embedding(
self.tokenizer(["", ""])
)[0:1]
def forward(self, batch):
# If you want this conditioner to be unconditional, set self.unconditional_prob = 1.0
# If you want this conditioner to be fully conditional, set self.unconditional_prob = 0.0
if self.model.training == True and not self.training_mode:
print(
"The pretrained CLAP model should always be in eval mode. Reloading model just in case you change the parameters."
)
self.model, self.model_cfg = create_model(
self.amodel,
self.tmodel,
self.pretrained,
precision=self.precision,
device="cuda" if self.cuda else "cpu",
enable_fusion=self.enable_fusion,
fusion_type=self.fusion_type,
)
for p in self.model.parameters():
p.requires_grad = False
self.model.eval()
if self.unconditional_token is None:
self.build_unconditional_emb()
# if(self.training_mode):
# assert self.model.training == True
# else:
# assert self.model.training == False
# the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
if self.embed_mode == "audio":
if not self.training:
print("INFO: clap model calculate the audio embedding as condition")
with torch.no_grad():
# assert (
# self.sampling_rate == 16000
# ), "We only support 16000 sampling rate"
# if self.random_mute:
# batch = self._random_mute(batch)
# batch: [bs, 1, t-samples]
if self.sampling_rate != 48000:
batch = torchaudio.functional.resample(
batch, orig_freq=self.sampling_rate, new_freq=48000
)
audio_data = batch.squeeze(1).to("cpu")
self.mel_transform = self.mel_transform.to(audio_data.device)
mel = self.mel_transform(audio_data)
audio_dict = get_audio_features(
audio_data,
mel,
480000,
data_truncating="fusion",
data_filling="repeatpad",
audio_cfg=self.model_cfg["audio_cfg"],
)
# [bs, 512]
embed = self.model.get_audio_embedding(audio_dict)
elif self.embed_mode == "text":
with torch.no_grad():
# the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
text_data = self.tokenizer(batch)
if isinstance(batch, str) or (
isinstance(batch, list) and len(batch) == 1
):
for key in text_data.keys():
text_data[key] = text_data[key].unsqueeze(0)
embed = self.model.get_text_embedding(text_data)
embed = embed.unsqueeze(1)
for i in range(embed.size(0)):
if self.make_decision(self.unconditional_prob):
embed[i] = self.unconditional_token
# embed = torch.randn((batch.size(0), 1, 512)).type_as(batch)
return embed.detach()
def tokenizer(self, text):
result = self.tokenize(
text,
padding="max_length",
truncation=True,
max_length=512,
return_tensors="pt",
)
return {k: v.squeeze(0) for k, v in result.items()}
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