File size: 7,749 Bytes
0374441
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
from cached_path import cached_path


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 = global_phonemizer.phonemize([text])
    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 = global_phonemizer.phonemize([text])
  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