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from cached_path import cached_path
from dp.phonemizer import Phonemizer
import torch
torch.manual_seed(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
import random
random.seed(0)
import numpy as np
np.random.seed(0)
import nltk
nltk.download('punkt')
# load packages
import time
import random
import yaml
from munch import Munch
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torchaudio
import librosa
from nltk.tokenize import word_tokenize
from models import *
from utils import *
from text_utils import TextCleaner
textclenaer = TextCleaner()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
to_mel = torchaudio.transforms.MelSpectrogram(
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
mean, std = -4, 4
def length_to_mask(lengths):
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
mask = torch.gt(mask+1, lengths.unsqueeze(1))
return mask
def preprocess(wave):
wave_tensor = torch.from_numpy(wave).float()
mel_tensor = to_mel(wave_tensor)
mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
return mel_tensor
def compute_style(ref_dicts):
reference_embeddings = {}
for key, path in ref_dicts.items():
wave, sr = librosa.load(path, sr=24000)
audio, index = librosa.effects.trim(wave, top_db=30)
if sr != 24000:
audio = librosa.resample(audio, sr, 24000)
mel_tensor = preprocess(audio).to(device)
with torch.no_grad():
ref = model.style_encoder(mel_tensor.unsqueeze(1))
reference_embeddings[key] = (ref.squeeze(1), audio)
return reference_embeddings
# load phonemizer
# import phonemizer
# global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True, words_mismatch='ignore')
phonemizer = Phonemizer.from_checkpoint(str(cached_path('https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_ipa_forward.pt')))
config = yaml.safe_load(open(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/config.yml'))))
# load pretrained ASR model
ASR_config = config.get('ASR_config', False)
ASR_path = config.get('ASR_path', False)
text_aligner = load_ASR_models(ASR_path, ASR_config)
# load pretrained F0 model
F0_path = config.get('F0_path', False)
pitch_extractor = load_F0_models(F0_path)
# load BERT model
from Utils.PLBERT.util import load_plbert
BERT_path = config.get('PLBERT_dir', False)
plbert = load_plbert(BERT_path)
model = build_model(recursive_munch(config['model_params']), text_aligner, pitch_extractor, plbert)
_ = [model[key].eval() for key in model]
_ = [model[key].to(device) for key in model]
# params_whole = torch.load("Models/LJSpeech/epoch_2nd_00100.pth", map_location='cpu')
params_whole = torch.load(str(cached_path('hf://yl4579/StyleTTS2-LJSpeech/Models/LJSpeech/epoch_2nd_00100.pth')), map_location='cpu')
params = params_whole['net']
for key in model:
if key in params:
print('%s loaded' % key)
try:
model[key].load_state_dict(params[key])
except:
from collections import OrderedDict
state_dict = params[key]
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
model[key].load_state_dict(new_state_dict, strict=False)
# except:
# _load(params[key], model[key])
_ = [model[key].eval() for key in model]
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
sampler = DiffusionSampler(
model.diffusion.diffusion,
sampler=ADPM2Sampler(),
sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
clamp=False
)
def inference(text, noise, diffusion_steps=5, embedding_scale=1):
text = text.strip()
text = text.replace('"', '')
ps = phonemizer([text], lang='en_us')
ps = word_tokenize(ps[0])
ps = ' '.join(ps)
tokens = textclenaer(ps)
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
with torch.no_grad():
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
text_mask = length_to_mask(input_lengths).to(tokens.device)
t_en = model.text_encoder(tokens, input_lengths, text_mask)
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
s_pred = sampler(noise,
embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
embedding_scale=embedding_scale).squeeze(0)
s = s_pred[:, 128:]
ref = s_pred[:, :128]
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
x, _ = model.predictor.lstm(d)
duration = model.predictor.duration_proj(x)
duration = torch.sigmoid(duration).sum(axis=-1)
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
pred_dur[-1] += 5
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
c_frame = 0
for i in range(pred_aln_trg.size(0)):
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
c_frame += int(pred_dur[i].data)
# encode prosody
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
return out.squeeze().cpu().numpy()
def LFinference(text, s_prev, noise, alpha=0.7, diffusion_steps=5, embedding_scale=1):
text = text.strip()
text = text.replace('"', '')
ps = phonemizer([text], lang='en_us')
ps = word_tokenize(ps[0])
ps = ' '.join(ps)
tokens = textclenaer(ps)
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
with torch.no_grad():
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(tokens.device)
text_mask = length_to_mask(input_lengths).to(tokens.device)
t_en = model.text_encoder(tokens, input_lengths, text_mask)
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
s_pred = sampler(noise,
embedding=bert_dur[0].unsqueeze(0), num_steps=diffusion_steps,
embedding_scale=embedding_scale).squeeze(0)
if s_prev is not None:
# convex combination of previous and current style
s_pred = alpha * s_prev + (1 - alpha) * s_pred
s = s_pred[:, 128:]
ref = s_pred[:, :128]
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
x, _ = model.predictor.lstm(d)
duration = model.predictor.duration_proj(x)
duration = torch.sigmoid(duration).sum(axis=-1)
pred_dur = torch.round(duration.squeeze()).clamp(min=1)
pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
c_frame = 0
for i in range(pred_aln_trg.size(0)):
pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
c_frame += int(pred_dur[i].data)
# encode prosody
en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
out = model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(device)),
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
return out.squeeze().cpu().numpy(), s_pred |