295 lines
13 KiB
Python
295 lines
13 KiB
Python
import json
|
|
import torch
|
|
from tqdm import tqdm
|
|
from .model_2rvq import PromptCondAudioDiffusion
|
|
from diffusers import DDIMScheduler, DDPMScheduler
|
|
import torchaudio
|
|
import librosa
|
|
import os
|
|
import math
|
|
import numpy as np
|
|
# from tools.get_mulan import get_mulan
|
|
from .tools.get_1dvae_large import get_model
|
|
#import tools.torch_tools as torch_tools
|
|
from safetensors.torch import load_file
|
|
from .audio import AudioFile
|
|
# import kaldiio
|
|
|
|
class Tango:
|
|
def __init__(self, \
|
|
model_path, \
|
|
layer_num=6, \
|
|
rvq_num=1, \
|
|
device="cuda:0"):
|
|
|
|
self.sample_rate = 48000
|
|
scheduler_name = "configs/scheduler/stable_diffusion_2.1_largenoise_sample.json"
|
|
self.device = device
|
|
|
|
self.vae = get_model()
|
|
self.vae = self.vae.to(device)
|
|
self.vae=self.vae.eval()
|
|
self.layer_num = layer_num
|
|
|
|
self.MAX_DURATION = 360
|
|
main_config = {
|
|
"num_channels":32,
|
|
"unet_model_name":None,
|
|
"unet_model_config_path":"configs/models/transformer2D_wocross_inch112_1x4_multi_large.json",
|
|
"snr_gamma":None,
|
|
}
|
|
self.rvq_num = rvq_num
|
|
# print("rvq_num: ", self.rvq_num)
|
|
# exit()
|
|
self.model = PromptCondAudioDiffusion(**main_config).to(device)
|
|
if model_path.endswith(".safetensors"):
|
|
main_weights = load_file(model_path)
|
|
else:
|
|
main_weights = torch.load(model_path, map_location=device)
|
|
self.model.load_state_dict(main_weights, strict=False)
|
|
print ("Successfully loaded checkpoint from:", model_path)
|
|
|
|
self.model.eval()
|
|
self.model.init_device_dtype(torch.device(device), torch.float32)
|
|
print("scaling factor: ", self.model.normfeat.std)
|
|
|
|
# self.scheduler = DDIMScheduler.from_pretrained( \
|
|
# scheduler_name, subfolder="scheduler")
|
|
# self.scheduler = DDPMScheduler.from_pretrained( \
|
|
# scheduler_name, subfolder="scheduler")
|
|
print("Successfully loaded inference scheduler from {}".format(scheduler_name))
|
|
|
|
|
|
|
|
@torch.no_grad()
|
|
@torch.autocast(device_type="cuda", dtype=torch.float32)
|
|
def sound2code(self, orig_samples, batch_size=8):
|
|
if(orig_samples.ndim == 2):
|
|
audios = orig_samples.unsqueeze(0).to(self.device)
|
|
elif(orig_samples.ndim == 3):
|
|
audios = orig_samples.to(self.device)
|
|
else:
|
|
assert orig_samples.ndim in (2,3), orig_samples.shape
|
|
audios = self.preprocess_audio(audios)
|
|
audios = audios.squeeze(0)
|
|
orig_length = audios.shape[-1]
|
|
min_samples = int(40 * self.sample_rate)
|
|
# 40秒对应10个token
|
|
output_len = int(orig_length / float(self.sample_rate) * 25) + 1
|
|
# print("output_len: ", output_len)
|
|
|
|
while(audios.shape[-1] < min_samples):
|
|
audios = torch.cat([audios, audios], -1)
|
|
int_max_len=audios.shape[-1]//min_samples+1
|
|
audios = torch.cat([audios, audios], -1)
|
|
audios=audios[:,:int(int_max_len*(min_samples))]
|
|
codes_list=[]
|
|
|
|
audio_input = audios.reshape(2, -1, min_samples).permute(1, 0, 2).reshape(-1, 2, min_samples)
|
|
|
|
for audio_inx in range(0, audio_input.shape[0], batch_size):
|
|
# import pdb; pdb.set_trace()
|
|
codes, _, spk_embeds = self.model.fetch_codes_batch((audio_input[audio_inx:audio_inx+batch_size]), additional_feats=[],layer=self.layer_num, rvq_num=self.rvq_num)
|
|
# print("codes",codes[0].shape)
|
|
|
|
codes_list.append(torch.cat(codes, 1))
|
|
# print("codes_list",codes_list[0].shape)
|
|
|
|
codes = torch.cat(codes_list, 0).permute(1,0,2).reshape(self.rvq_num, -1)[None] # B 3 T -> 3 B T
|
|
codes=codes[:,:,:output_len]
|
|
|
|
return codes
|
|
|
|
@torch.no_grad()
|
|
@torch.autocast(device_type="cuda", dtype=torch.float32)
|
|
def sound2code_ds(self, orig_samples, ds, batch_size=8):
|
|
if(orig_samples.ndim == 2):
|
|
audios = orig_samples.unsqueeze(0).to(self.device)
|
|
elif(orig_samples.ndim == 3):
|
|
audios = orig_samples.to(self.device)
|
|
else:
|
|
assert orig_samples.ndim in (2,3), orig_samples.shape
|
|
audios = self.preprocess_audio(audios)
|
|
audios = audios.squeeze(0)
|
|
orig_length = audios.shape[-1]
|
|
min_samples = int(40 * self.sample_rate)
|
|
# 40秒对应10个token
|
|
output_len = int(orig_length / float(self.sample_rate) * 25) + 1
|
|
# print("output_len: ", output_len)
|
|
|
|
while(audios.shape[-1] < min_samples):
|
|
audios = torch.cat([audios, audios], -1)
|
|
int_max_len=audios.shape[-1]//min_samples+1
|
|
audios = torch.cat([audios, audios], -1)
|
|
audios=audios[:,:int(int_max_len*(min_samples))]
|
|
codes_list=[]
|
|
|
|
audio_input = audios.reshape(2, -1, min_samples).permute(1, 0, 2).reshape(-1, 2, min_samples)
|
|
|
|
for audio_inx in range(0, audio_input.shape[0], batch_size):
|
|
# import pdb; pdb.set_trace()
|
|
codes, _, spk_embeds = self.model.fetch_codes_batch_ds((audio_input[audio_inx:audio_inx+batch_size]), additional_feats=[],layer=self.layer_num, rvq_num=self.rvq_num, ds=ds)
|
|
# print("codes",codes[0].shape)
|
|
|
|
codes_list.append(torch.cat(codes, 1))
|
|
# print("codes_list",codes_list[0].shape)
|
|
|
|
codes = torch.cat(codes_list, 0).permute(1,0,2).reshape(self.rvq_num, -1)[None] # B 3 T -> 3 B T
|
|
codes=codes[:,:,:output_len]
|
|
|
|
return codes
|
|
|
|
@torch.no_grad()
|
|
def code2sound(self, codes, prompt=None, duration=40, guidance_scale=1.5, num_steps=20, disable_progress=False):
|
|
codes = codes.to(self.device)
|
|
|
|
min_samples = duration * 25 # 40ms per frame
|
|
hop_samples = min_samples // 4 * 3
|
|
ovlp_samples = min_samples - hop_samples
|
|
hop_frames = hop_samples
|
|
ovlp_frames = ovlp_samples
|
|
first_latent = torch.randn(codes.shape[0], min_samples, 64).to(self.device)
|
|
first_latent_length = 0
|
|
first_latent_codes_length = 0
|
|
|
|
if(isinstance(prompt, torch.Tensor)):
|
|
# prepare prompt
|
|
prompt = prompt.to(self.device)
|
|
if(prompt.ndim == 3):
|
|
assert prompt.shape[0] == 1, prompt.shape
|
|
prompt = prompt[0]
|
|
elif(prompt.ndim == 1):
|
|
prompt = prompt.unsqueeze(0).repeat(2,1)
|
|
elif(prompt.ndim == 2):
|
|
if(prompt.shape[0] == 1):
|
|
prompt = prompt.repeat(2,1)
|
|
|
|
if(prompt.shape[-1] < int(30 * self.sample_rate)):
|
|
# if less than 30s, just choose the first 10s
|
|
prompt = prompt[:,:int(10*self.sample_rate)] # limit max length to 10.24
|
|
else:
|
|
# else choose from 20.48s which might includes verse or chorus
|
|
prompt = prompt[:,int(20*self.sample_rate):int(30*self.sample_rate)] # limit max length to 10.24
|
|
|
|
true_latent = self.vae.encode_audio(prompt).permute(0,2,1)
|
|
# print("true_latent.shape", true_latent.shape)
|
|
# print("first_latent.shape", first_latent.shape)
|
|
#true_latent.shape torch.Size([1, 250, 64])
|
|
# first_latent.shape torch.Size([1, 1000, 64])
|
|
|
|
first_latent[:,0:true_latent.shape[1],:] = true_latent
|
|
first_latent_length = true_latent.shape[1]
|
|
first_latent_codes = self.sound2code(prompt)
|
|
first_latent_codes_length = first_latent_codes.shape[-1]
|
|
codes = torch.cat([first_latent_codes, codes], -1)
|
|
|
|
codes_len= codes.shape[-1]
|
|
target_len = int((codes_len - first_latent_codes_length) / 100 * 4 * self.sample_rate)
|
|
# target_len = int(codes_len / 100 * 4 * self.sample_rate)
|
|
# code repeat
|
|
if(codes_len < min_samples):
|
|
while(codes.shape[-1] < min_samples):
|
|
codes = torch.cat([codes, codes], -1)
|
|
codes = codes[:,:,0:min_samples]
|
|
codes_len = codes.shape[-1]
|
|
if((codes_len - ovlp_samples) % hop_samples > 0):
|
|
len_codes=math.ceil((codes_len - ovlp_samples) / float(hop_samples)) * hop_samples + ovlp_samples
|
|
while(codes.shape[-1] < len_codes):
|
|
codes = torch.cat([codes, codes], -1)
|
|
codes = codes[:,:,0:len_codes]
|
|
latent_length = min_samples
|
|
latent_list = []
|
|
spk_embeds = torch.zeros([1, 32, 1, 32], device=codes.device)
|
|
with torch.autocast(device_type="cuda", dtype=torch.float16):
|
|
for sinx in range(0, codes.shape[-1]-hop_samples, hop_samples):
|
|
codes_input=[]
|
|
codes_input.append(codes[:,:,sinx:sinx+min_samples])
|
|
if(sinx == 0):
|
|
# print("Processing {} to {}".format(sinx/self.sample_rate, (sinx + min_samples)/self.sample_rate))
|
|
incontext_length = first_latent_length
|
|
latents = self.model.inference_codes(codes_input, spk_embeds, first_latent, latent_length, incontext_length=incontext_length, additional_feats=[], guidance_scale=1.5, num_steps = num_steps, disable_progress=disable_progress, scenario='other_seg')
|
|
latent_list.append(latents)
|
|
else:
|
|
# print("Processing {} to {}".format(sinx/self.sample_rate, (sinx + min_samples)/self.sample_rate))
|
|
true_latent = latent_list[-1][:,:,-ovlp_frames:].permute(0,2,1)
|
|
print("true_latent.shape", true_latent.shape)
|
|
len_add_to_1000 = 1000 - true_latent.shape[-2]
|
|
# print("len_add_to_1000", len_add_to_1000)
|
|
# exit()
|
|
incontext_length = true_latent.shape[-2]
|
|
true_latent = torch.cat([true_latent, torch.randn(true_latent.shape[0], len_add_to_1000, true_latent.shape[-1]).to(self.device)], -2)
|
|
latents = self.model.inference_codes(codes_input, spk_embeds, true_latent, latent_length, incontext_length=incontext_length, additional_feats=[], guidance_scale=1.5, num_steps = num_steps, disable_progress=disable_progress, scenario='other_seg')
|
|
latent_list.append(latents)
|
|
|
|
latent_list = [l.float() for l in latent_list]
|
|
latent_list[0] = latent_list[0][:,:,first_latent_length:]
|
|
min_samples = int(min_samples * self.sample_rate // 1000 * 40)
|
|
hop_samples = int(hop_samples * self.sample_rate // 1000 * 40)
|
|
ovlp_samples = min_samples - hop_samples
|
|
with torch.no_grad():
|
|
output = None
|
|
for i in range(len(latent_list)):
|
|
latent = latent_list[i]
|
|
cur_output = self.vae.decode_audio(latent)[0].detach().cpu()
|
|
|
|
if output is None:
|
|
output = cur_output
|
|
else:
|
|
ov_win = torch.from_numpy(np.linspace(0, 1, ovlp_samples)[None, :])
|
|
ov_win = torch.cat([ov_win, 1 - ov_win], -1)
|
|
print("output.shape", output.shape)
|
|
print("ov_win.shape", ov_win.shape)
|
|
output[:, -ovlp_samples:] = output[:, -ovlp_samples:] * ov_win[:, -ovlp_samples:] + cur_output[:, 0:ovlp_samples] * ov_win[:, 0:ovlp_samples]
|
|
output = torch.cat([output, cur_output[:, ovlp_samples:]], -1)
|
|
output = output[:, 0:target_len]
|
|
return output
|
|
|
|
@torch.no_grad()
|
|
def preprocess_audio(self, input_audios, threshold=0.8):
|
|
assert len(input_audios.shape) == 3, input_audios.shape
|
|
nchan = input_audios.shape[1]
|
|
input_audios = input_audios.reshape(input_audios.shape[0], -1)
|
|
norm_value = torch.ones_like(input_audios[:,0])
|
|
max_volume = input_audios.abs().max(dim=-1)[0]
|
|
norm_value[max_volume>threshold] = max_volume[max_volume>threshold] / threshold
|
|
return input_audios.reshape(input_audios.shape[0], nchan, -1)/norm_value.unsqueeze(-1).unsqueeze(-1)
|
|
|
|
@torch.no_grad()
|
|
def sound2sound(self, sound, prompt=None, steps=50, disable_progress=False):
|
|
codes = self.sound2code(sound)
|
|
# print(codes.shape)
|
|
# exit()
|
|
wave = self.code2sound(codes, prompt, guidance_scale=1.5, num_steps=steps, disable_progress=disable_progress)
|
|
# print(fname, wave.shape)
|
|
return wave
|
|
|
|
def file2code(self, fname):
|
|
try:
|
|
orig_samples, fs = torchaudio.load(fname)
|
|
except:
|
|
af = AudioFile(fname)
|
|
orig_samples = af.read()
|
|
fs = af.samplerate()
|
|
orig_samples = orig_samples[0]
|
|
if(fs!=self.sample_rate):
|
|
orig_samples = torchaudio.functional.resample(orig_samples, fs, self.sample_rate)
|
|
fs = self.sample_rate
|
|
if orig_samples.shape[0] == 1:
|
|
orig_samples = torch.cat([orig_samples, orig_samples], 0)
|
|
return self.sound2code(orig_samples)
|
|
|
|
def file2code_ds(self, fname, ds):
|
|
try:
|
|
orig_samples, fs = torchaudio.load(fname)
|
|
except:
|
|
af = AudioFile(fname)
|
|
orig_samples = af.read()
|
|
fs = af.samplerate()
|
|
orig_samples = orig_samples[0]
|
|
if(fs!=self.sample_rate):
|
|
orig_samples = torchaudio.functional.resample(orig_samples, fs, self.sample_rate)
|
|
fs = self.sample_rate
|
|
if orig_samples.shape[0] == 1:
|
|
orig_samples = torch.cat([orig_samples, orig_samples], 0)
|
|
return self.sound2code_ds(orig_samples, ds)
|