infer.py

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 spaces
import yaml
import re
import numpy as np
import torch
import torch.nn.functional as F
import torchaudio
from ipa_uk import ipa
from unicodedata import normalize
from ukrainian_word_stress import Stressifier, StressSymbol
stressify = Stressifier(stress_symbol=StressSymbol.CombiningAcuteAccent)



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



config = yaml.safe_load(open('styletts_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('epoch_2nd_00027_filatov_whisp_cont.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 split_to_parts(text):
    split_symbols = '.?!:'
    parts = ['']
    index = 0
    for s in text:
        parts[index] += s
        if s in split_symbols and len(parts[index]) > 150:
            index += 1
            parts.append('')
    return parts
    


def _inf(text, s_prev, noise, alpha, diffusion_steps, embedding_scale):
    text = text.strip()
    text = text.replace('"', '')
    text = text.replace('+', 'ˈ')
    text = normalize('NFKC', text)

    text = re.sub(r'[᠆‐‑‒–—―⁻₋−⸺⸻]', '-', text)
    text = re.sub(r' - ', ': ', text)
    ps = ipa(stressify(text))
    #ps = text
    print(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, ps


@spaces.GPU
def inference(text, progress, alpha=0.7, diffusion_steps=10, embedding_scale=1.2):

    wavs = []
    s_prev = None

    #sentences = text.split('|')
    sentences = split_to_parts(text)
    print(sentences)
    phonemes = ''
    noise = torch.randn(1,1,256).to(device)
    for text in progress.tqdm(sentences):
        if text.strip() == "": continue
        wav, s_prev, ps = _inf(text, s_prev, noise, alpha=alpha, diffusion_steps=diffusion_steps, embedding_scale=embedding_scale)
        wavs.append(wav)
        phonemes += ' ' + ps
    return  np.concatenate(wavs), phonemes