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from cached_path import cached_path
# from dp.phonemizer import Phonemizer
print("NLTK")
import nltk
nltk.download('punkt')
print("SCIPY")
from scipy.io.wavfile import write
print("TORCH STUFF")
import torch
print("START")
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)
# 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()
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(path):
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_s = model.style_encoder(mel_tensor.unsqueeze(1))
ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
return torch.cat([ref_s, ref_p], dim=1)
device = 'cpu'
if torch.cuda.is_available():
device = 'cuda'
elif torch.backends.mps.is_available():
print("MPS would be available but cannot be used rn")
# device = 'mps'
# config = yaml.safe_load(open("Models/LibriTTS/config.yml"))
config = yaml.safe_load(open(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/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_params = recursive_munch(config['model_params'])
model = build_model(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/LibriTTS/epochs_2nd_00020.pth", map_location='cpu')
params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.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
)
voicelist = ['f-us-1', 'f-us-2', 'f-us-3', 'f-us-4', 'm-us-1', 'm-us-2', 'm-us-3', 'm-us-4']
voices = {}
# todo: cache computed style, load using pickle
for v in voicelist:
print(f"Loading voice {v}")
voices[v] = compute_style(f'voices/{v}.wav')
import pickle
with open('voices.pkl', 'wb') as f:
pickle.dump(voices, f) |