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from typing import Sequence, Optional, Union
import sys
# sys.path.append('/aifs4su/data/zheny/fairseq/vae_v2/codec_final')
import math
import random
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from modules.seanet import SEANetEncoder, SEANetDecoder
from quantization import ResidualVectorQuantizer#,VectorQuantize
from transformers import AutoModel
# from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import AutoFeatureExtractor, WhisperModel
# sys.path.append('/scratch/buildlam/codeclm/jiahaopan/codec_final/RepCodec')
from RepCodec.repcodec.modules.encoder import Encoder
from RepCodec.repcodec.modules.decoder import Decoder
# sys.path.append('/data/zheny/UniAudio/codec/descriptaudiocodecs')
#descript-audio-codec/dac/model
import descriptaudiocodec.dac.model.dac as dac2
# sys.path.append('/aifs4su/data/zheny/peiwensun/project/s3prl/s3prl/upstream/hubert/')
# from simple_expert import UpstreamExpert
def get_model_size(model):
# 计算总参数数
total_params = sum(p.numel() for p in model.parameters())
# 假设每个参数都是32位浮点数，计算模型大小（以字节为单位）
model_size_bytes = total_params # 每个参数4字节
# 转换为更易读的单位（例如，MB）
model_size_mb = model_size_bytes / (1024 ** 2)
return total_params, model_size_mb
class SoundStream(nn.Module):
""" SoundStream model or EnCodec model.
Args:
n_filters (int): n_filters (int): Base width for the model.
D (int): Intermediate representation dimension.
target_bandwidths (Sequence[int]): Target bandwidths in K-bits/second.
ratios (Sequence[int]): downsampling factors, whose multiplication is the hop size.
sample_rate (int): wave sampling rate.
bins (int): number of code words in a codebook.
normalize (bool): audio normalization.
"""
def __init__(
self,
n_filters: int = 32,
D: int = 128,
# target_bandwidths: Sequence[Union[int, float]] = [0.5, 1, 1.5, 2, 4, 6],
target_bandwidths: Sequence[Union[int, float]] = [1, 1.5, 2, 4, 6],
ratios: Sequence[int] = [8, 5, 4, 2], # downsampling by 320
sample_rate: int = 16000,
bins: int = 1024,
normalize: bool = False,
causal: bool = False,
):
super().__init__()
self.hop_length = np.prod(ratios)
# total nb of codebooks, e.g., 6Kb/s, sr=16000 and hop_length=320 => nq = 12
n_q = int(1000 * target_bandwidths[-1] // (math.ceil(sample_rate / self.hop_length) * 10))
self.frame_rate = math.ceil(sample_rate / np.prod(ratios)) # 50 Hz
self.bits_per_codebook = int(math.log2(bins)) # 1024 => 10
self.target_bandwidths = target_bandwidths
self.n_q = n_q
self.sample_rate = sample_rate
# Encoder model
# self.encoder = SEANetEncoder(n_filters=n_filters, dimension=D, ratios=ratios, causal=causal)
self.encoder = dac2.Encoder( 64,ratios,D)
# RVQ model
self.encoder_semantic = Encoder(input_channels=768,encode_channels=768)
self.decoder_semantic = Decoder(code_dim=768,output_channels=768,decode_channels=768)
# out_D=D+768
self.quantizer = ResidualVectorQuantizer(dimension=D+768, n_q=n_q, bins=bins)
# Decoder model
# self.decoder = SEANetDecoder(n_filters= n_filters, dimension=D, ratios=ratios, causal=causal)
self.decoder_2 = dac2.Decoder( D,1024,ratios,)
# )
# self.upstream = UpstreamExpert(
# ckpt = '/aifs4su/data/zheny/fairseq/outputs/2024-05-08/12-50-35/checkpoints2/checkpoint_8_225000_converted.pt',
# )#.to(self.args.device)
# self.upstream.model = self.upstream.model.to(self.device)
c=1
# self.upstream(wavs)
# self.processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
self.is_semantic= True
if self.is_semantic:
# self.semantic_model = AutoModel.from_pretrained("/aifs4su/data/zheny/DiT_TTS/ckpts/yz_2")
# self.semantic_model = AutoModel.from_pretrained("/aifs4su/data/zheny/fairseq/outputs/2024-05-11/13-27-56/hf15")
self.semantic_model = AutoModel.from_pretrained("./xcodec_mini_infer/semantic_ckpts/hf_1_325000")
self.semantic_model.eval()
# self.transform_linear = nn.Linear(1024, 768)
# processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
# self.semantic_model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
self.fc_prior = nn.Linear(D+768, D+768 )
# self.fc_prior= nn.Linear( D, D )
self.fc_post1= nn.Linear( D+768, 768 )
self.fc_post2= nn.Linear( D+768, D)
def get_last_layer(self):
return self.decoder.layers[-1].weight
def calculate_rec_loss(self, rec, target):
target = target / target.norm(dim=-1, keepdim=True)
rec = rec / rec.norm(dim=-1, keepdim=True)
rec_loss = (1 - (target * rec).sum(-1)).mean()
# rec_loss = F.mse_loss(target, rec)
return rec_loss
@torch.no_grad()
def get_regress_target(self, x ):
x= x[:,0,:]
x = F.pad(x, (160, 160))
target = self.semantic_model(x, output_hidden_states=True) .hidden_states
target = torch.stack(target, dim=1)#.transpose(-1, -2)#.flatten(start_dim=1, end_dim=2)
target = target.mean(1)
# target = target[9]
return target
def forward(self, x: torch.Tensor, bw: int):
e_semantic_input = self.get_regress_target_whisper(x).detach()
e_semantic = self.encoder_semantic(e_semantic_input.transpose(1, 2))
e_acoustic = self.encoder(x)
e= torch.cat([e_acoustic, e_semantic], dim=1)
e = self.fc_prior(e.transpose(1, 2)).transpose(1, 2)
quantized, codes, bandwidth, commit_loss = self.quantizer(e, self.frame_rate, bw)
quantized_semantic = self.fc_post1(quantized.transpose(1, 2)).transpose(1, 2)
quantized_acoustic = self.fc_post2(quantized.transpose(1, 2)).transpose(1, 2)
o = self.decoder_2(quantized_acoustic)
o_semantic = self.decoder_semantic(quantized_semantic )
semantic_recon_loss = F.mse_loss(e_semantic_input.transpose(1, 2).detach(),o_semantic)
return o, commit_loss, semantic_recon_loss,None
# return o, commit_loss, distill_loss.mean(),None
def encode(self, x: torch.Tensor, target_bw: Optional[int] = None) -> torch.Tensor:
# e = self.encoder(x)
# if target_bw is None:
# bw = self.target_bandwidths[-1]
# else:
bw = target_bw
# codes = self.quantizer.encode(e, self.frame_rate, bw)
# if e_acoustic.shape[2] != e_semantic.shape[2]:
# print(f"e_acoustic {e_acoustic.shape} e_semantic{e_semantic.shape}")
e_semantic_input = self.get_regress_target(x).detach()
e_semantic = self.encoder_semantic(e_semantic_input.transpose(1, 2))
e_acoustic = self.encoder(x)
if e_acoustic.shape[2] != e_semantic.shape[2]:
# e_acoustic = self.encoder(F.pad(x[:,0,:], (160, 160)).unsqueeze(0))
e_acoustic = self.encoder(torch.transpose(F.pad(x[:,0,:], (160, 160)).unsqueeze(0), 0, 1))
e= torch.cat([e_acoustic, e_semantic], dim=1)
e = self.fc_prior(e.transpose(1, 2)).transpose(1, 2)
quantized, codes, bandwidth, commit_loss = self.quantizer(e, self.frame_rate, bw)
return codes
def get_embed(self, codes: torch.Tensor) -> torch.Tensor:
return self.quantizer.decode(codes)
def decode(self, codes: torch.Tensor) -> torch.Tensor:
quantized = self.quantizer.decode(codes)
quantized_acoustic = self.fc_post2(quantized.transpose(1, 2)).transpose(1, 2)
o = self.decoder_2(quantized_acoustic)
return o
# test
if __name__ == '__main__':
soundstream = SoundStream(n_filters=32, D=256)#.cuda(0)
# get_model_size(soundstream)
for i in range(10):
print(f"Iter {i}: ")
x = torch.rand(1, 1, 16000)#.cuda(0)
o, commit_loss, distill_loss,_= soundstream(x,soundstream.target_bandwidths[-1])
print('output', o.shape)
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