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speech.py
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import math
import os
import random
import time
from inspect import isfunction
from typing import Optional, Callable, List, Type, Any, TypeVar, Union, Tuple, Dict
import librosa
import nltk
import numpy as np
import phonemizer
import torch
import torchaudio
import yaml
from nltk import word_tokenize
from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
from Speech.text_utils import TextCleaner
from speech_models import *
from Utils.PLBERT.util import load_plbert
T = TypeVar("T")
# Almost all of this comes from https://github.com/yl4579/StyleTTS2
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 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
class SpeakerRunner:
def __init__(self):
device = "cuda"
self.device = "cuda"
torch.manual_seed(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
random.seed(0)
np.random.seed(0)
nltk.download('punkt')
self.text_cleaner = TextCleaner()
to_mel = torchaudio.transforms.MelSpectrogram(n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
mean, std = -4, 4
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
self.global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True,
with_stress=True,
words_mismatch='ignore')
config = yaml.safe_load(open("Models/LJSpeech/config.yml"))
ASR_config = config.get('ASR_config', False)
ASR_path = config.get('ASR_path', False)
text_aligner = load_ASR_models(ASR_path, ASR_config)
F0_path = config.get('F0_path', False)
pitch_extractor = load_F0_models(F0_path)
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 = params_whole['net']
for key in model:
if key in params:
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]
self.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
)
self.model = model
def inference(self, text, noise, diffusion_steps=5, embedding_scale=1):
text = text.strip()
text = text.replace('"', '')
ps = self.global_phonemizer.phonemize([text])
ps = word_tokenize(ps[0])
ps = ' '.join(ps)
tokens = self.text_cleaner(ps)
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(self.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 = self.model.text_encoder(tokens, input_lengths, text_mask)
bert_dur = self.model.bert(tokens, attention_mask=(~text_mask).int())
d_en = self.model.bert_encoder(bert_dur).transpose(-1, -2)
s_pred = self.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 = self.model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
x, _ = self.model.predictor.lstm(d)
duration = self.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(self.device))
F0_pred, N_pred = self.model.predictor.F0Ntrain(en, s)
out = self.model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(self.device)),
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
return out.squeeze().cpu().numpy()
def LFinference(self, text, s_prev, noise, alpha=0.7, diffusion_steps=5, embedding_scale=1):
text = text.strip()
text = text.replace('"', '')
ps = self.global_phonemizer.phonemize([text])
ps = word_tokenize(ps[0])
ps = ' '.join(ps)
tokens = self.text_cleaner(ps)
tokens.insert(0, 0)
tokens = torch.LongTensor(tokens).to(self.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 = self.model.text_encoder(tokens, input_lengths, text_mask)
bert_dur = self.model.bert(tokens, attention_mask=(~text_mask).int())
d_en = self.model.bert_encoder(bert_dur).transpose(-1, -2)
s_pred = self.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 = self.model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
x, _ = self.model.predictor.lstm(d)
duration = self.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(self.device))
F0_pred, N_pred = self.model.predictor.F0Ntrain(en, s)
out = self.model.decoder((t_en @ pred_aln_trg.unsqueeze(0).to(self.device)),
F0_pred, N_pred, ref.squeeze().unsqueeze(0))
return out.squeeze().cpu().numpy(), s_pred