MockingBird/synthesizer/preprocess_speaker.py

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import librosa
import numpy as np
from encoder import inference as encoder
from utils import logmmse
from synthesizer import audio
from pathlib import Path
from pypinyin import Style
from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
from pypinyin.converter import DefaultConverter
from pypinyin.core import Pinyin
class PinyinConverter(NeutralToneWith5Mixin, DefaultConverter):
pass
pinyin = Pinyin(PinyinConverter()).pinyin
def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str,
skip_existing: bool, hparams):
## FOR REFERENCE:
# For you not to lose your head if you ever wish to change things here or implement your own
# synthesizer.
# - Both the audios and the mel spectrograms are saved as numpy arrays
# - There is no processing done to the audios that will be saved to disk beyond volume
# normalization (in split_on_silences)
# - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This
# is why we re-apply it on the audio on the side of the vocoder.
# - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved
# without extra padding. This means that you won't have an exact relation between the length
# of the wav and of the mel spectrogram. See the vocoder data loader.
# Skip existing utterances if needed
mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename)
wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename)
if skip_existing and mel_fpath.exists() and wav_fpath.exists():
return None
# Trim silence
if hparams.trim_silence:
wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True)
# Skip utterances that are too short
if len(wav) < hparams.utterance_min_duration * hparams.sample_rate:
return None
# Compute the mel spectrogram
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
# Skip utterances that are too long
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
# Write the spectrogram, embed and audio to disk
np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False)
np.save(wav_fpath, wav, allow_pickle=False)
# Return a tuple describing this training example
return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text
def _split_on_silences_aidatatang_200zh(wav_fpath, words, hparams):
# Load the audio waveform
wav, _ = librosa.load(wav_fpath, hparams.sample_rate)
wav = librosa.effects.trim(wav, top_db= 40, frame_length=2048, hop_length=512)[0]
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# denoise, we may not need it here.
if len(wav) > hparams.sample_rate*(0.3+0.1):
noise_wav = np.concatenate([wav[:int(hparams.sample_rate*0.15)],
wav[-int(hparams.sample_rate*0.15):]])
profile = logmmse.profile_noise(noise_wav, hparams.sample_rate)
wav = logmmse.denoise(wav, profile, eta=0)
resp = pinyin(words, style=Style.TONE3)
res = [v[0] for v in resp if v[0].strip()]
res = " ".join(res)
return wav, res
def preprocess_speaker_general(speaker_dir, out_dir: Path, skip_existing: bool, hparams, dict_info, no_alignments: bool):
metadata = []
wav_fpath_list = speaker_dir.glob("*.wav")
# Iterate over each wav
for wav_fpath in wav_fpath_list:
words = dict_info.get(wav_fpath.name.split(".")[0])
words = dict_info.get(wav_fpath.name) if not words else words # try with wav
if not words:
print("no wordS")
continue
sub_basename = "%s_%02d" % (wav_fpath.name, 0)
wav, text = _split_on_silences_aidatatang_200zh(wav_fpath, words, hparams)
metadata.append(_process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
def preprocess_speaker(speaker_dir, out_dir: Path, skip_existing: bool, hparams, no_alignments: bool):
metadata = []
for book_dir in speaker_dir.glob("*"):
if no_alignments:
# Gather the utterance audios and texts
# LibriTTS uses .wav but we will include extensions for compatibility with other datasets
extensions = ["*.wav", "*.flac", "*.mp3"]
for extension in extensions:
wav_fpaths = book_dir.glob(extension)
for wav_fpath in wav_fpaths:
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# Get the corresponding text
# Check for .txt (for compatibility with other datasets)
text_fpath = wav_fpath.with_suffix(".txt")
if not text_fpath.exists():
# Check for .normalized.txt (LibriTTS)
text_fpath = wav_fpath.with_suffix(".normalized.txt")
assert text_fpath.exists()
with text_fpath.open("r") as text_file:
text = "".join([line for line in text_file])
text = text.replace("\"", "")
text = text.strip()
# Process the utterance
metadata.append(_process_utterance(wav, text, out_dir, str(wav_fpath.with_suffix("").name),
skip_existing, hparams))
else:
# Process alignment file (LibriSpeech support)
# Gather the utterance audios and texts
try:
alignments_fpath = next(book_dir.glob("*.alignment.txt"))
with alignments_fpath.open("r") as alignments_file:
alignments = [line.rstrip().split(" ") for line in alignments_file]
except StopIteration:
# A few alignment files will be missing
continue
# Iterate over each entry in the alignments file
for wav_fname, words, end_times in alignments:
wav_fpath = book_dir.joinpath(wav_fname + ".flac")
assert wav_fpath.exists()
words = words.replace("\"", "").split(",")
end_times = list(map(float, end_times.replace("\"", "").split(",")))
# Process each sub-utterance
wavs, texts = _split_on_silences(wav_fpath, words, end_times, hparams)
for i, (wav, text) in enumerate(zip(wavs, texts)):
sub_basename = "%s_%02d" % (wav_fname, i)
metadata.append(_process_utterance(wav, text, out_dir, sub_basename,
skip_existing, hparams))
return [m for m in metadata if m is not None]
# TODO: use original split func
def _split_on_silences(wav_fpath, words, end_times, hparams):
# Load the audio waveform
wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate)
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
words = np.array(words)
start_times = np.array([0.0] + end_times[:-1])
end_times = np.array(end_times)
assert len(words) == len(end_times) == len(start_times)
assert words[0] == "" and words[-1] == ""
# Find pauses that are too long
mask = (words == "") & (end_times - start_times >= hparams.silence_min_duration_split)
mask[0] = mask[-1] = True
breaks = np.where(mask)[0]
# Profile the noise from the silences and perform noise reduction on the waveform
silence_times = [[start_times[i], end_times[i]] for i in breaks]
silence_times = (np.array(silence_times) * hparams.sample_rate).astype(np.int)
noisy_wav = np.concatenate([wav[stime[0]:stime[1]] for stime in silence_times])
if len(noisy_wav) > hparams.sample_rate * 0.02:
profile = logmmse.profile_noise(noisy_wav, hparams.sample_rate)
wav = logmmse.denoise(wav, profile, eta=0)
# Re-attach segments that are too short
segments = list(zip(breaks[:-1], breaks[1:]))
segment_durations = [start_times[end] - end_times[start] for start, end in segments]
i = 0
while i < len(segments) and len(segments) > 1:
if segment_durations[i] < hparams.utterance_min_duration:
# See if the segment can be re-attached with the right or the left segment
left_duration = float("inf") if i == 0 else segment_durations[i - 1]
right_duration = float("inf") if i == len(segments) - 1 else segment_durations[i + 1]
joined_duration = segment_durations[i] + min(left_duration, right_duration)
# Do not re-attach if it causes the joined utterance to be too long
if joined_duration > hparams.hop_size * hparams.max_mel_frames / hparams.sample_rate:
i += 1
continue
# Re-attach the segment with the neighbour of shortest duration
j = i - 1 if left_duration <= right_duration else i
segments[j] = (segments[j][0], segments[j + 1][1])
segment_durations[j] = joined_duration
del segments[j + 1], segment_durations[j + 1]
else:
i += 1
# Split the utterance
segment_times = [[end_times[start], start_times[end]] for start, end in segments]
segment_times = (np.array(segment_times) * hparams.sample_rate).astype(np.int)
wavs = [wav[segment_time[0]:segment_time[1]] for segment_time in segment_times]
texts = [" ".join(words[start + 1:end]).replace(" ", " ") for start, end in segments]
# # DEBUG: play the audio segments (run with -n=1)
# import sounddevice as sd
# if len(wavs) > 1:
# print("This sentence was split in %d segments:" % len(wavs))
# else:
# print("There are no silences long enough for this sentence to be split:")
# for wav, text in zip(wavs, texts):
# # Pad the waveform with 1 second of silence because sounddevice tends to cut them early
# # when playing them. You shouldn't need to do that in your parsers.
# wav = np.concatenate((wav, [0] * 16000))
# print("\t%s" % text)
# sd.play(wav, 16000, blocking=True)
# print("")
return wavs, texts