MockingBird/synthesizer/preprocess_speaker.py

import platform
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
    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_aidatatang_200zh(speaker_dir, out_dir: Path, skip_existing: bool, hparams, directory, no_alignments: bool):
    dict_info = {}
    transcript_dirs = directory.joinpath("transcript/aidatatang_200_zh_transcript.txt")
    with open(transcript_dirs,"rb") as fp:
        dict_transcript = [v.decode() for v in fp]
    for v in dict_transcript:
        if not v:
            continue
        v = v.strip().replace("\n","").split(" ")
        dict_info[v[0]] = " ".join(v[1:])

    metadata = []
    if platform.system() == "Windows":
        split = "\\"
    else:
        split = "/" 
    for wav_fpath in speaker_dir.glob("*.wav"):
        name = str(wav_fpath).split(split)[-1]
        key = name.split(".")[0]
        words = dict_info.get(key)
        if not words:
            continue
        sub_basename = "%s_%02d" % (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