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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from pathlib import Path
from typing import Optional, List, Dict
import zipfile
import tempfile
from dataclasses import dataclass
from itertools import groupby
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from examples.speech_to_text.data_utils import load_tsv_to_dicts
from fairseq.data.audio.audio_utils import TTSSpectrogram, TTSMelScale
def trim_or_pad_to_target_length(
data_1d_or_2d: np.ndarray, target_length: int
) -> np.ndarray:
assert len(data_1d_or_2d.shape) in {1, 2}
delta = data_1d_or_2d.shape[0] - target_length
if delta >= 0: # trim if being longer
data_1d_or_2d = data_1d_or_2d[: target_length]
else: # pad if being shorter
if len(data_1d_or_2d.shape) == 1:
data_1d_or_2d = np.concatenate(
[data_1d_or_2d, np.zeros(-delta)], axis=0
)
else:
data_1d_or_2d = np.concatenate(
[data_1d_or_2d, np.zeros((-delta, data_1d_or_2d.shape[1]))],
axis=0
)
return data_1d_or_2d
def extract_logmel_spectrogram(
waveform: torch.Tensor, sample_rate: int,
output_path: Optional[Path] = None, win_length: int = 1024,
hop_length: int = 256, n_fft: int = 1024,
win_fn: callable = torch.hann_window, n_mels: int = 80,
f_min: float = 0., f_max: float = 8000, eps: float = 1e-5,
overwrite: bool = False, target_length: Optional[int] = None
):
if output_path is not None and output_path.is_file() and not overwrite:
return
spectrogram_transform = TTSSpectrogram(
n_fft=n_fft, win_length=win_length, hop_length=hop_length,
window_fn=win_fn
)
mel_scale_transform = TTSMelScale(
n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
n_stft=n_fft // 2 + 1
)
spectrogram = spectrogram_transform(waveform)
mel_spec = mel_scale_transform(spectrogram)
logmel_spec = torch.clamp(mel_spec, min=eps).log()
assert len(logmel_spec.shape) == 3 and logmel_spec.shape[0] == 1
logmel_spec = logmel_spec.squeeze().t() # D x T -> T x D
if target_length is not None:
trim_or_pad_to_target_length(logmel_spec, target_length)
if output_path is not None:
np.save(output_path.as_posix(), logmel_spec)
else:
return logmel_spec
def extract_pitch(
waveform: torch.Tensor, sample_rate: int,
output_path: Optional[Path] = None, hop_length: int = 256,
log_scale: bool = True, phoneme_durations: Optional[List[int]] = None
):
if output_path is not None and output_path.is_file():
return
try:
import pyworld
except ImportError:
raise ImportError("Please install PyWORLD: pip install pyworld")
_waveform = waveform.squeeze(0).double().numpy()
pitch, t = pyworld.dio(
_waveform, sample_rate, frame_period=hop_length / sample_rate * 1000
)
pitch = pyworld.stonemask(_waveform, pitch, t, sample_rate)
if phoneme_durations is not None:
pitch = trim_or_pad_to_target_length(pitch, sum(phoneme_durations))
try:
from scipy.interpolate import interp1d
except ImportError:
raise ImportError("Please install SciPy: pip install scipy")
nonzero_ids = np.where(pitch != 0)[0]
interp_fn = interp1d(
nonzero_ids,
pitch[nonzero_ids],
fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]),
bounds_error=False,
)
pitch = interp_fn(np.arange(0, len(pitch)))
d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations]))
pitch = np.array(
[
np.mean(pitch[d_cumsum[i-1]: d_cumsum[i]])
for i in range(1, len(d_cumsum))
]
)
assert len(pitch) == len(phoneme_durations)
if log_scale:
pitch = np.log(pitch + 1)
if output_path is not None:
np.save(output_path.as_posix(), pitch)
else:
return pitch
def extract_energy(
waveform: torch.Tensor, output_path: Optional[Path] = None,
hop_length: int = 256, n_fft: int = 1024, log_scale: bool = True,
phoneme_durations: Optional[List[int]] = None
):
if output_path is not None and output_path.is_file():
return
assert len(waveform.shape) == 2 and waveform.shape[0] == 1
waveform = waveform.view(1, 1, waveform.shape[1])
waveform = F.pad(
waveform.unsqueeze(1), [n_fft // 2, n_fft // 2, 0, 0],
mode="reflect"
)
waveform = waveform.squeeze(1)
fourier_basis = np.fft.fft(np.eye(n_fft))
cutoff = int((n_fft / 2 + 1))
fourier_basis = np.vstack(
[np.real(fourier_basis[:cutoff, :]),
np.imag(fourier_basis[:cutoff, :])]
)
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
forward_transform = F.conv1d(
waveform, forward_basis, stride=hop_length, padding=0
)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
energy = torch.norm(magnitude, dim=1).squeeze(0).numpy()
if phoneme_durations is not None:
energy = trim_or_pad_to_target_length(energy, sum(phoneme_durations))
d_cumsum = np.cumsum(np.concatenate([np.array([0]), phoneme_durations]))
energy = np.array(
[
np.mean(energy[d_cumsum[i - 1]: d_cumsum[i]])
for i in range(1, len(d_cumsum))
]
)
assert len(energy) == len(phoneme_durations)
if log_scale:
energy = np.log(energy + 1)
if output_path is not None:
np.save(output_path.as_posix(), energy)
else:
return energy
def get_global_cmvn(feature_root: Path, output_path: Optional[Path] = None):
mean_x, mean_x2, n_frames = None, None, 0
feature_paths = feature_root.glob("*.npy")
for p in tqdm(feature_paths):
with open(p, 'rb') as f:
frames = np.load(f).squeeze()
n_frames += frames.shape[0]
cur_mean_x = frames.sum(axis=0)
if mean_x is None:
mean_x = cur_mean_x
else:
mean_x += cur_mean_x
cur_mean_x2 = (frames ** 2).sum(axis=0)
if mean_x2 is None:
mean_x2 = cur_mean_x2
else:
mean_x2 += cur_mean_x2
mean_x /= n_frames
mean_x2 /= n_frames
var_x = mean_x2 - mean_x ** 2
std_x = np.sqrt(np.maximum(var_x, 1e-10))
if output_path is not None:
with open(output_path, 'wb') as f:
np.savez(f, mean=mean_x, std=std_x)
else:
return {"mean": mean_x, "std": std_x}
def ipa_phonemize(text, lang="en-us", use_g2p=False):
if use_g2p:
assert lang == "en-us", "g2pE phonemizer only works for en-us"
try:
from g2p_en import G2p
g2p = G2p()
return " ".join("|" if p == " " else p for p in g2p(text))
except ImportError:
raise ImportError(
"Please install phonemizer: pip install g2p_en"
)
else:
try:
from phonemizer import phonemize
from phonemizer.separator import Separator
return phonemize(
text, backend='espeak', language=lang,
separator=Separator(word="| ", phone=" ")
)
except ImportError:
raise ImportError(
"Please install phonemizer: pip install phonemizer"
)
@dataclass
class ForceAlignmentInfo(object):
tokens: List[str]
frame_durations: List[int]
start_sec: Optional[float]
end_sec: Optional[float]
def get_mfa_alignment_by_sample_id(
textgrid_zip_path: str, sample_id: str, sample_rate: int,
hop_length: int, silence_phones: List[str] = ("sil", "sp", "spn")
) -> ForceAlignmentInfo:
try:
import tgt
except ImportError:
raise ImportError("Please install TextGridTools: pip install tgt")
filename = f"{sample_id}.TextGrid"
out_root = Path(tempfile.gettempdir())
tgt_path = out_root / filename
with zipfile.ZipFile(textgrid_zip_path) as f_zip:
f_zip.extract(filename, path=out_root)
textgrid = tgt.io.read_textgrid(tgt_path.as_posix())
os.remove(tgt_path)
phones, frame_durations = [], []
start_sec, end_sec, end_idx = 0, 0, 0
for t in textgrid.get_tier_by_name("phones")._objects:
s, e, p = t.start_time, t.end_time, t.text
# Trim leading silences
if len(phones) == 0:
if p in silence_phones:
continue
else:
start_sec = s
phones.append(p)
if p not in silence_phones:
end_sec = e
end_idx = len(phones)
r = sample_rate / hop_length
frame_durations.append(int(np.round(e * r) - np.round(s * r)))
# Trim tailing silences
phones = phones[:end_idx]
frame_durations = frame_durations[:end_idx]
return ForceAlignmentInfo(
tokens=phones, frame_durations=frame_durations, start_sec=start_sec,
end_sec=end_sec
)
def get_mfa_alignment(
textgrid_zip_path: str, sample_ids: List[str], sample_rate: int,
hop_length: int
) -> Dict[str, ForceAlignmentInfo]:
return {
i: get_mfa_alignment_by_sample_id(
textgrid_zip_path, i, sample_rate, hop_length
) for i in tqdm(sample_ids)
}
def get_unit_alignment(
id_to_unit_tsv_path: str, sample_ids: List[str]
) -> Dict[str, ForceAlignmentInfo]:
id_to_units = {
e["id"]: e["units"] for e in load_tsv_to_dicts(id_to_unit_tsv_path)
}
id_to_units = {i: id_to_units[i].split() for i in sample_ids}
id_to_units_collapsed = {
i: [uu for uu, _ in groupby(u)] for i, u in id_to_units.items()
}
id_to_durations = {
i: [len(list(g)) for _, g in groupby(u)] for i, u in id_to_units.items()
}
return {
i: ForceAlignmentInfo(
tokens=id_to_units_collapsed[i], frame_durations=id_to_durations[i],
start_sec=None, end_sec=None
)
for i in sample_ids
}
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