Spaces:
Running on Zero
Running on Zero
File size: 13,903 Bytes
cc348e2 fd86276 cc348e2 d091a25 cc348e2 d091a25 cc348e2 7a3c0c9 cc348e2 fd86276 cc348e2 fd86276 cc348e2 fd86276 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | import itertools
import os
import librosa.display as lbd
import matplotlib.pyplot as plt
# import sounddevice
import soundfile
import torch
from InferenceInterfaces.InferenceArchitectures.InferenceFastSpeech2 import FastSpeech2
from InferenceInterfaces.InferenceArchitectures.InferenceHiFiGAN import HiFiGANGenerator
from InferenceInterfaces.InferenceArchitectures.Avocodo.InferenceHiFiGAN import HiFiGANGeneratorAvocodo
from Preprocessing.ProsodicConditionExtractor import ProsodicConditionExtractor
from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend
from Preprocessing.TextFrontend import get_language_id
from Preprocessing.Language_embedding import LanguageEmbedding
class InferenceFastSpeech2(torch.nn.Module):
def __init__(self, device="cpu", model_name="Austrian", language=None, noise_reduce=False, Avocodo=False, model_dir="Models"):
super().__init__()
self.device = device
self.text2phone = ArticulatoryCombinedTextFrontend(language=language, add_silence_to_end=True)
# checkpoint = torch.load(os.path.join(model_dir, f"FastSpeech2_{model_name}", "best.pt"), map_location='cpu')
checkpoint = torch.load(
os.path.join(model_dir, f"FastSpeech2_{model_name}", "best.pt"),
map_location="cpu",
weights_only=False
)
print("using model: ", os.path.join(model_dir, model_name))
self.use_lang_id = True
try:
self.phone2mel = FastSpeech2(weights=checkpoint["model"]).to(torch.device(device)) # multi speaker multi language
except RuntimeError:
try:
self.use_lang_id = False
self.phone2mel = FastSpeech2(weights=checkpoint["model"], lang_emb=None).to(torch.device(device)) # multi speaker single language
except RuntimeError:
self.phone2mel = FastSpeech2(weights=checkpoint["model"], lang_emb=None, utt_embed_dim=None).to(torch.device(device)) # single speaker
self.mel2wav = HiFiGANGenerator(path_to_weights=os.path.join(model_dir, "HiFiGAN_aridialect", "best.pt")).to(torch.device(device))
if Avocodo:
self.mel2wav = HiFiGANGeneratorAvocodo(path_to_weights=os.path.join(model_dir, "Avocodo", "best.pt")).to(torch.device(device))
self.default_utterance_embedding = checkpoint["default_emb"].to(self.device)
self.lang_emb = None
self.phone2mel.eval()
self.mel2wav.eval()
if self.use_lang_id:
self.lang_id = get_language_id(language)
else:
self.lang_id = None
self.to(torch.device(device))
self.noise_reduce = noise_reduce
if self.noise_reduce:
self.prototypical_noise = None
self.update_noise_profile()
def set_utterance_embedding(self, path_to_reference_audio):
wave, sr = soundfile.read(path_to_reference_audio)
self.default_utterance_embedding = ProsodicConditionExtractor(sr=sr).extract_condition_from_reference_wave(wave).to(self.device)
if self.noise_reduce:
self.update_noise_profile()
def set_language_embedding(self, path_to_reference_audio, use_avg=True):
# select between {at_emb, vd_emb, ivg_emb, goi_emb, interp_at_vd_emb, spanish_emb, fr_emb }
if use_avg == True:
# self.default_lang_emb = torch.from_numpy(torch.load(path_to_reference_audio)).to(self.device) # reference audio is actually a .pt file, that is averaged
self.default_lang_emb = torch.from_numpy(torch.load(path_to_reference_audio, map_location="cpu", weights_only=False)).to(self.device)
print("default_lang_emb: " + str(path_to_reference_audio))
else:
emb = LanguageEmbedding()
self.default_lang_emb=emb.get_emb_from_path(path_to_wavfile=path_to_reference_audio).to(self.device)
print("default_lang_emb: " + str(path_to_reference_audio))
def update_noise_profile(self):
self.noise_reduce = False
self.prototypical_noise = self("~." * 100, input_is_phones=True).cpu().numpy()
self.noise_reduce = True
def set_language(self, lang_id):
"""
The id parameter actually refers to the shorthand. This has become ambiguous with the introduction of the actual language IDs
"""
self.text2phone = ArticulatoryCombinedTextFrontend(language=lang_id, add_silence_to_end=True)
if self.use_lang_id:
self.lang_id = get_language_id(lang_id).to(self.device)
else:
self.lang_id = None
def set_phoneme_input(self, input_is_phones=None):
"""
Set input method of text. input_is_phones=None
"""
self.input_is_phones = input_is_phones
def forward(self,
text,
view=False,
duration_scaling_factor=1.0,
pitch_variance_scale=1.0,
energy_variance_scale=1.0,
durations=None,
pitch=None,
energy=None,
lang_emb=None,
input_is_phones=False,
path_to_wavfile=""):
"""
duration_scaling_factor: reasonable values are 0.8 < scale < 1.2.
1.0 means no scaling happens, higher values increase durations for the whole
utterance, lower values decrease durations for the whole utterance.
pitch_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the pitch curve,
lower values decrease variance of the pitch curve.
energy_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the energy curve,
lower values decrease variance of the energy curve.
"""
print("phoneme input flag in forward: " + str(self.input_is_phones))
#emb = LanguageEmbedding()
with torch.inference_mode():
phones = self.text2phone.string_to_tensor(text, input_phonemes=self.input_is_phones, path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/hpo_vd_wean_0002.wav").to(torch.device(self.device))
#print(self.default_lang_emb)
mel, durations, pitch, energy = self.phone2mel(phones,
return_duration_pitch_energy=True,
utterance_embedding=self.default_utterance_embedding,
durations=durations,
pitch=pitch,
energy=energy,
#lang_emb=emb.get_emb_from_path(path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/hpo_vd_wean_0002.wav"),
#lang_emb=emb.get_emb_from_path(path_to_wavfile="/data/vokquant/data/aridialect/aridialect_wav16000/spo_at_berlin_001.wav"),
#lang_emb=self.default_lang_emb.squeeze(0),
lang_emb=self.default_lang_emb,
duration_scaling_factor=duration_scaling_factor,
pitch_variance_scale=pitch_variance_scale,
energy_variance_scale=energy_variance_scale)
mel = mel.transpose(0, 1)
wave = self.mel2wav(mel)
if view:
from Utility.utils import cumsum_durations
fig, ax = plt.subplots(nrows=2, ncols=1)
ax[0].plot(wave.cpu().numpy())
lbd.specshow(mel.cpu().numpy(),
ax=ax[1],
sr=16000,
cmap='GnBu',
y_axis='mel',
x_axis=None,
hop_length=256)
ax[0].yaxis.set_visible(False)
ax[1].yaxis.set_visible(False)
duration_splits, label_positions = cumsum_durations(durations.cpu().numpy())
ax[1].set_xticks(duration_splits, minor=True)
ax[1].xaxis.grid(True, which='minor')
ax[1].set_xticks(label_positions, minor=False)
ax[1].set_xticklabels(self.text2phone.get_phone_string(text, for_plot_labels=True))
ax[0].set_title(text)
plt.subplots_adjust(left=0.05, bottom=0.1, right=0.95, top=.9, wspace=0.0, hspace=0.0)
plt.show()
if self.noise_reduce:
import noisereduce
wave = torch.tensor(noisereduce.reduce_noise(y=wave.cpu().numpy(), y_noise=self.prototypical_noise, sr=48000, stationary=True), device=self.device)
return wave
def read_to_file(self,
text_list,
file_location,
duration_scaling_factor=1.0,
pitch_variance_scale=1.0,
energy_variance_scale=1.0,
silent=False,
dur_list=None,
pitch_list=None,
energy_list=None):
"""
Args:
silent: Whether to be verbose about the process
text_list: A list of strings to be read
file_location: The path and name of the file it should be saved to
energy_list: list of energy tensors to be used for the texts
pitch_list: list of pitch tensors to be used for the texts
dur_list: list of duration tensors to be used for the texts
duration_scaling_factor: reasonable values are 0.8 < scale < 1.2.
1.0 means no scaling happens, higher values increase durations for the whole
utterance, lower values decrease durations for the whole utterance.
pitch_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the pitch curve,
lower values decrease variance of the pitch curve.
energy_variance_scale: reasonable values are 0.6 < scale < 1.4.
1.0 means no scaling happens, higher values increase variance of the energy curve,
lower values decrease variance of the energy curve.
"""
if not dur_list:
dur_list = []
if not pitch_list:
pitch_list = []
if not energy_list:
energy_list = []
wav = None
silence = torch.zeros([24000])
for (text, durations, pitch, energy) in itertools.zip_longest(text_list, dur_list, pitch_list, energy_list):
if text.strip() != "":
if not silent:
print("Now synthesizing: {}".format(text))
if wav is None:
if durations is not None:
durations = durations.to(self.device)
if pitch is not None:
pitch = pitch.to(self.device)
if energy is not None:
energy = energy.to(self.device)
wav = self(text,
durations=durations,
pitch=pitch,
energy=energy,
duration_scaling_factor=duration_scaling_factor,
pitch_variance_scale=pitch_variance_scale,
energy_variance_scale=energy_variance_scale).cpu()
wav = torch.cat((wav, silence), 0)
else:
wav = torch.cat((wav, self(text,
durations=durations.to(self.device),
pitch=pitch.to(self.device),
energy=energy.to(self.device),
duration_scaling_factor=duration_scaling_factor,
pitch_variance_scale=pitch_variance_scale,
energy_variance_scale=energy_variance_scale).cpu()), 0)
wav = torch.cat((wav, silence), 0)
soundfile.write(file=file_location, data=wav.cpu().numpy(), samplerate=48000)
def read_aloud(self,
text,
view=False,
duration_scaling_factor=1.0,
pitch_variance_scale=1.0,
energy_variance_scale=1.0,
blocking=False):
if text.strip() == "":
return
wav = self(text,
view,
duration_scaling_factor=duration_scaling_factor,
pitch_variance_scale=pitch_variance_scale,
energy_variance_scale=energy_variance_scale).cpu()
wav = torch.cat((wav, torch.zeros([24000])), 0)
# if not blocking:
# sounddevice.play(wav.numpy(), samplerate=48000)
# else:
# sounddevice.play(torch.cat((wav, torch.zeros([12000])), 0).numpy(), samplerate=48000)
# sounddevice.wait()
if not blocking:
return (48000, wav.numpy())
else:
wav = torch.cat((wav, torch.zeros([12000])), 0)
return (48000, wav.numpy())
|