Spaces:
Running
Running
File size: 10,928 Bytes
9d61c9b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 |
from dataclasses import dataclass
import math
import random
from typing import Any, List, Tuple, Union
import numpy as np
from scipy.stats import betabinom
import torch
import torch.nn.functional as F
from models.config import PreprocessingConfig, VocoderBasicConfig, get_lang_map
from .audio import normalize_loudness, preprocess_audio
from .audio_processor import AudioProcessor
from .compute_yin import compute_yin, norm_interp_f0
from .normalize_text import NormalizeText
from .tacotron_stft import TacotronSTFT
from .tokenizer_ipa_espeak import TokenizerIpaEspeak as TokenizerIPA
@dataclass
class PreprocessForAcousticResult:
wav: torch.Tensor
mel: torch.Tensor
pitch: torch.Tensor
phones_ipa: Union[str, List[str]]
phones: torch.Tensor
attn_prior: torch.Tensor
energy: torch.Tensor
raw_text: str
normalized_text: str
speaker_id: int
chapter_id: str | int
utterance_id: str
pitch_is_normalized: bool
class PreprocessLibriTTS:
r"""Preprocessing PreprocessLibriTTS audio and text data for use with a TacotronSTFT model.
Args:
preprocess_config (PreprocessingConfig): The preprocessing configuration.
lang (str): The language of the input text.
Attributes:
min_seconds (float): The minimum duration of audio clips in seconds.
max_seconds (float): The maximum duration of audio clips in seconds.
hop_length (int): The hop length of the STFT.
sampling_rate (int): The sampling rate of the audio.
use_audio_normalization (bool): Whether to normalize the loudness of the audio.
tacotronSTFT (TacotronSTFT): The TacotronSTFT object used for computing mel spectrograms.
min_samples (int): The minimum number of audio samples in a clip.
max_samples (int): The maximum number of audio samples in a clip.
"""
def __init__(
self,
preprocess_config: PreprocessingConfig,
lang: str = "en",
):
super().__init__()
lang_map = get_lang_map(lang)
self.phonemizer_lang = lang_map.phonemizer
normilize_text_lang = lang_map.nemo
self.normilize_text = NormalizeText(normilize_text_lang)
self.tokenizer = TokenizerIPA(lang)
self.vocoder_train_config = VocoderBasicConfig()
self.preprocess_config = preprocess_config
self.sampling_rate = self.preprocess_config.sampling_rate
self.use_audio_normalization = self.preprocess_config.use_audio_normalization
self.hop_length = self.preprocess_config.stft.hop_length
self.filter_length = self.preprocess_config.stft.filter_length
self.mel_fmin = self.preprocess_config.stft.mel_fmin
self.win_length = self.preprocess_config.stft.win_length
self.tacotronSTFT = TacotronSTFT(
filter_length=self.filter_length,
hop_length=self.hop_length,
win_length=self.preprocess_config.stft.win_length,
n_mel_channels=self.preprocess_config.stft.n_mel_channels,
sampling_rate=self.sampling_rate,
mel_fmin=self.mel_fmin,
mel_fmax=self.preprocess_config.stft.mel_fmax,
center=False,
)
min_seconds, max_seconds = (
self.preprocess_config.min_seconds,
self.preprocess_config.max_seconds,
)
self.min_samples = int(self.sampling_rate * min_seconds)
self.max_samples = int(self.sampling_rate * max_seconds)
self.audio_processor = AudioProcessor()
def beta_binomial_prior_distribution(
self,
phoneme_count: int,
mel_count: int,
scaling_factor: float = 1.0,
) -> torch.Tensor:
r"""Computes the beta-binomial prior distribution for the attention mechanism.
Args:
phoneme_count (int): Number of phonemes in the input text.
mel_count (int): Number of mel frames in the input mel-spectrogram.
scaling_factor (float, optional): Scaling factor for the beta distribution. Defaults to 1.0.
Returns:
torch.Tensor: A 2D tensor containing the prior distribution.
"""
P, M = phoneme_count, mel_count
x = np.arange(0, P)
mel_text_probs = []
for i in range(1, M + 1):
a, b = scaling_factor * i, scaling_factor * (M + 1 - i)
rv: Any = betabinom(P, a, b)
mel_i_prob = rv.pmf(x)
mel_text_probs.append(mel_i_prob)
return torch.from_numpy(np.array(mel_text_probs))
def acoustic(
self,
row: Tuple[torch.Tensor, int, str, str, int, str | int, str],
) -> Union[None, PreprocessForAcousticResult]:
r"""Preprocesses audio and text data for use with a TacotronSTFT model.
Args:
row (Tuple[torch.FloatTensor, int, str, str, int, str | int, str]): The input row. The row is a tuple containing the following elements: (audio, sr_actual, raw_text, normalized_text, speaker_id, chapter_id, utterance_id).
Returns:
dict: A dictionary containing the preprocessed audio and text data.
Examples:
>>> preprocess_audio = PreprocessAudio("english_only")
>>> audio = torch.randn(1, 44100)
>>> sr_actual = 44100
>>> raw_text = "Hello, world!"
>>> output = preprocess_audio(audio, sr_actual, raw_text)
>>> output.keys()
dict_keys(['wav', 'mel', 'pitch', 'phones', 'raw_text', 'normalized_text', 'speaker_id', 'chapter_id', 'utterance_id', 'pitch_is_normalized'])
"""
(
audio,
sr_actual,
raw_text,
normalized_text,
speaker_id,
chapter_id,
utterance_id,
) = row
wav, sampling_rate = preprocess_audio(audio, sr_actual, self.sampling_rate)
# TODO: check this, maybe you need to move it to some other place
# TODO: maybe we can increate the max_samples ?
# if wav.shape[0] < self.min_samples or wav.shape[0] > self.max_samples:
# return None
if self.use_audio_normalization:
wav = normalize_loudness(wav)
normalized_text = self.normilize_text(normalized_text)
# NOTE: fixed version of tokenizer with punctuation
phones_ipa, phones = self.tokenizer(normalized_text)
# Convert to tensor
phones = torch.Tensor(phones)
mel_spectrogram = self.tacotronSTFT.get_mel_from_wav(wav)
# Skipping small sample due to the mel-spectrogram containing less than self.mel_fmin frames
# if mel_spectrogram.shape[1] < self.mel_fmin:
# return None
# Text is longer than mel, will be skipped due to monotonic alignment search
if phones.shape[0] >= mel_spectrogram.shape[1]:
return None
pitch, _, _, _ = compute_yin(
wav,
sr=sampling_rate,
w_len=self.filter_length,
w_step=self.hop_length,
f0_min=50,
f0_max=1000,
harmo_thresh=0.25,
)
pitch, _ = norm_interp_f0(pitch)
if np.sum(pitch != 0) <= 1:
return None
pitch = torch.from_numpy(pitch)
# TODO this shouldnt be necessary, currently pitch sometimes has 1 less frame than spectrogram,
# We should find out why
mel_spectrogram = mel_spectrogram[:, : pitch.shape[0]]
attn_prior = self.beta_binomial_prior_distribution(
phones.shape[0],
mel_spectrogram.shape[1],
).T
assert pitch.shape[0] == mel_spectrogram.shape[1], (
pitch.shape,
mel_spectrogram.shape[1],
)
energy = self.audio_processor.wav_to_energy(
wav.unsqueeze(0),
self.filter_length,
self.hop_length,
self.win_length,
)
return PreprocessForAcousticResult(
wav=wav,
mel=mel_spectrogram,
pitch=pitch,
attn_prior=attn_prior,
energy=energy,
phones_ipa=phones_ipa,
phones=phones,
raw_text=raw_text,
normalized_text=normalized_text,
speaker_id=speaker_id,
chapter_id=chapter_id,
utterance_id=utterance_id,
# TODO: check the pitch normalization process
pitch_is_normalized=False,
)
def univnet(self, row: Tuple[torch.Tensor, int, str, str, int, str | int, str]):
r"""Preprocesses audio data for use with a UnivNet model.
This method takes a row of data, extracts the audio and preprocesses it.
It then selects a random segment from the preprocessed audio and its corresponding mel spectrogram.
Args:
row (Tuple[torch.FloatTensor, int, str, str, int, str | int, str]): The input row. The row is a tuple containing the following elements: (audio, sr_actual, raw_text, normalized_text, speaker_id, chapter_id, utterance_id).
Returns:
Tuple[torch.Tensor, torch.Tensor, int]: A tuple containing the selected segment of the mel spectrogram, the corresponding audio segment, and the speaker ID.
Examples:
>>> preprocess = PreprocessLibriTTS()
>>> audio = torch.randn(1, 44100)
>>> sr_actual = 44100
>>> speaker_id = 0
>>> mel, audio_segment, speaker_id = preprocess.preprocess_univnet((audio, sr_actual, "", "", speaker_id, 0, ""))
"""
(
audio,
sr_actual,
_,
_,
speaker_id,
_,
_,
) = row
segment_size = self.vocoder_train_config.segment_size
frames_per_seg = math.ceil(segment_size / self.hop_length)
wav, _ = preprocess_audio(audio, sr_actual, self.sampling_rate)
if self.use_audio_normalization:
wav = normalize_loudness(wav)
mel_spectrogram = self.tacotronSTFT.get_mel_from_wav(wav)
if wav.shape[0] < segment_size:
wav = F.pad(
wav,
(0, segment_size - wav.shape[0]),
"constant",
)
if mel_spectrogram.shape[1] < frames_per_seg:
mel_spectrogram = F.pad(
mel_spectrogram,
(0, frames_per_seg - mel_spectrogram.shape[1]),
"constant",
)
from_frame = random.randint(0, mel_spectrogram.shape[1] - frames_per_seg)
# Skip last frame, otherwise errors are thrown, find out why
if from_frame > 0:
from_frame -= 1
till_frame = from_frame + frames_per_seg
mel_spectrogram = mel_spectrogram[:, from_frame:till_frame]
wav = wav[from_frame * self.hop_length : till_frame * self.hop_length]
return mel_spectrogram, wav, speaker_id
|