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msinference.py
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msinference.py
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import torch
from cached_path import cached_path
import nltk
# nltk.download('punkt')
import random
random.seed(0)
import numpy as np
np.random.seed(0)
import time
import random
import yaml
import torch.nn.functional as F
import copy
import torchaudio
import librosa
from models import *
from scipy.io.wavfile import write
from munch import Munch
from torch import nn
from nltk.tokenize import word_tokenize
from monotonic_align import mask_from_lens
from monotonic_align.core import maximum_path_c
torch.manual_seed(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# IPA Phonemizer: https://github.com/bootphon/phonemizer
_pad = "$"
_punctuation = ';:,.!?¡¿—…"«»“” '
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
# Export all symbols:
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
dicts = {}
for i in range(len((symbols))):
dicts[symbols[i]] = i
class TextCleaner:
def __init__(self, dummy=None):
self.word_index_dictionary = dicts
print(len(dicts))
def __call__(self, text):
indexes = []
for char in text:
try:
indexes.append(self.word_index_dictionary[char])
except KeyError:
print('CLEAN', text)
return indexes
textclenaer = TextCleaner()
to_mel = torchaudio.transforms.MelSpectrogram(
n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
mean, std = -4, 4
# START UTIL
def recursive_munch(d):
if isinstance(d, dict):
return Munch((k, recursive_munch(v)) for k, v in d.items())
elif isinstance(d, list):
return [recursive_munch(v) for v in d]
else:
return d
# ======== UTILS ABOVE
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")
pass
# device = 'mps'
import phonemizer
global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True)
# 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('Utils/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
)
def inference(text, ref_s, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1, use_gruut=False):
text = text.strip()
ps = global_phonemizer.phonemize([text])
# print(f'PHONEMIZER: {ps=}\n\n') #PHONEMIZER: ps=['ɐbˈɛbæbləm ']
ps = word_tokenize(ps[0])
# print(f'TOKENIZER: {ps=}\n\n') #OKENIZER: ps=['ɐbˈɛbæbləm']
ps = ' '.join(ps)
tokens = textclenaer(ps)
# print(f'TEXTCLEAN: {ps=}\n\n') #TEXTCLEAN: ps='ɐbˈɛbæbləm'
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
print(f'TOKENSFINAL: {ps=}\n\n')
with torch.no_grad():
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
text_mask = length_to_mask(input_lengths).to(device)
# -----------------------
# WHO TRANSLATES these tokens to sylla
# print(text_mask.shape, '\n__\n', tokens, '\n__\n', text_mask.min(), text_mask.max())
# text_mask=is binary
# tokes = tensor([[ 0, 55, 157, 86, 125, 83, 55, 156, 57, 158, 123, 48, 83, 61,
# 157, 102, 61, 16, 138, 64, 16, 53, 156, 138, 54, 62, 131, 85,
# 123, 83, 54, 16, 50, 156, 86, 123, 102, 125, 102, 46, 147, 16,
# 62, 135, 16, 76, 158, 92, 55, 156, 86, 56, 62, 177, 46, 16,
# 50, 157, 43, 102, 58, 85, 55, 156, 51, 158, 46, 51, 158, 83,
# 16, 48, 76, 158, 123, 16, 72, 53, 61, 157, 86, 61, 83, 44,
# 156, 102, 54, 177, 125, 51, 16, 72, 56, 46, 16, 102, 112, 53,
# 54, 156, 63, 158, 147, 83, 56, 16, 4]], device='cuda:0')
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)
# print('BERTdu', bert_dur.shape, tokens.shape, '\n') # bert what is the 768 per token -> IS USED in sampler
# BERTdu torch.Size([1, 11, 768]) torch.Size([1, 11])
s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
embedding=bert_dur,
embedding_scale=embedding_scale,
features=ref_s, # reference from the same speaker as the embedding
num_steps=diffusion_steps).squeeze(1)
s = s_pred[:, 128:]
ref = s_pred[:, :128]
ref = alpha * ref + (1 - alpha) * ref_s[:, :128]
s = beta * s + (1 - beta) * ref_s[:, 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))
if model_params.decoder.type == "hifigan":
asr_new = torch.zeros_like(en)
asr_new[:, :, 0] = en[:, :, 0]
asr_new[:, :, 1:] = en[:, :, 0:-1]
en = asr_new
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
if model_params.decoder.type == "hifigan":
asr_new = torch.zeros_like(asr)
asr_new[:, :, 0] = asr[:, :, 0]
asr_new[:, :, 1:] = asr[:, :, 0:-1]
asr = asr_new
out = model.decoder(asr,
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later