From 4178416385d88cc17516a12496130ffe6bed0d98 Mon Sep 17 00:00:00 2001 From: babysor00 Date: Sun, 19 Sep 2021 00:09:16 +0800 Subject: [PATCH] [FIX] Fix preprocessing bug for aishell3 --- pre.py | 10 +- synthesizer/preprocess.py | 20 ++-- synthesizer/preprocess_speaker.py | 156 +++------------------------ synthesizer/preprocess_transcript.py | 9 ++ 4 files changed, 42 insertions(+), 153 deletions(-) create mode 100644 synthesizer/preprocess_transcript.py diff --git a/pre.py b/pre.py index 26350a9..fbec51a 100644 --- a/pre.py +++ b/pre.py @@ -28,8 +28,7 @@ if __name__ == "__main__": "Path to the output directory that will contain the mel spectrograms, the audios and the " "embeds. Defaults to /SV2TTS/synthesizer/") parser.add_argument("-n", "--n_processes", type=int, default=1, help=\ - "Number of processes in parallel.An encoder is created for each, so you may need to lower " - "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy") + "Number of processes in parallel.") parser.add_argument("-s", "--skip_existing", action="store_true", help=\ "Whether to overwrite existing files with the same name. Useful if the preprocessing was " "interrupted. ") @@ -40,10 +39,13 @@ if __name__ == "__main__": parser.add_argument("--no_alignments", action="store_true", help=\ "Use this option when dataset does not include alignments\ (these are used to split long audio files into sub-utterances.)") - parser.add_argument("--dataset", type=str, default="aidatatang_200zh", help=\ + parser.add_argument("-d", "--dataset", type=str, default="aidatatang_200zh", help=\ "Name of the dataset to process, allowing values: magicdata, aidatatang_200zh, aishell3.") parser.add_argument("-e", "--encoder_model_fpath", type=Path, default="encoder/saved_models/pretrained.pt", help=\ "Path your trained encoder model.") + parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\ + "Number of processes in parallel.An encoder is created for each, so you may need to lower " + "this value on GPUs with low memory. Set it to 1 if CUDA is unhappy") args = parser.parse_args() # Process the arguments @@ -69,4 +71,4 @@ if __name__ == "__main__": preprocess_dataset(**vars(args)) - create_embeddings(synthesizer_root=args.out_dir, n_processes=args.n_processes, encoder_model_fpath=encoder_model_fpath) + create_embeddings(synthesizer_root=args.out_dir, n_processes=args.n_processes_embed, encoder_model_fpath=encoder_model_fpath) diff --git a/synthesizer/preprocess.py b/synthesizer/preprocess.py index 344a74d..430b037 100644 --- a/synthesizer/preprocess.py +++ b/synthesizer/preprocess.py @@ -7,6 +7,7 @@ from tqdm import tqdm import numpy as np from encoder import inference as encoder from synthesizer.preprocess_speaker import preprocess_speaker_general +from synthesizer.preprocess_transcript import preprocess_transcript_aishell3 data_info = { "aidatatang_200zh": { @@ -22,8 +23,9 @@ data_info = { "aishell3":{ "subfolders": ["train/wav"], "trans_filepath": "train/content.txt", - "speak_func": preprocess_speaker_general - }, + "speak_func": preprocess_speaker_general, + "transcript_func": preprocess_transcript_aishell3, + } } def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int, @@ -49,11 +51,15 @@ def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int, transcript_dirs = dataset_root.joinpath(dataset_info["trans_filepath"]) assert transcript_dirs.exists(), str(transcript_dirs)+" not exist." with open(transcript_dirs, "r", encoding="utf-8") as dict_transcript: - for v in dict_transcript: - if not v: - continue - v = v.strip().replace("\n","").replace("\t"," ").split(" ") - dict_info[v[0]] = " ".join(v[1:]) + # process with specific function for your dataset + if "transcript_func" in dataset_info: + dataset_info["transcript_func"](dict_info, dict_transcript) + else: + for v in dict_transcript: + if not v: + continue + v = v.strip().replace("\n","").replace("\t"," ").split(" ") + dict_info[v[0]] = " ".join(v[1:]) speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs)) func = partial(dataset_info["speak_func"], out_dir=out_dir, skip_existing=skip_existing, diff --git a/synthesizer/preprocess_speaker.py b/synthesizer/preprocess_speaker.py index ed566ad..88fad38 100644 --- a/synthesizer/preprocess_speaker.py +++ b/synthesizer/preprocess_speaker.py @@ -61,7 +61,7 @@ def _process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str, 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): +def _split_on_silences(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] @@ -82,146 +82,18 @@ def _split_on_silences_aidatatang_200zh(wav_fpath, words, hparams): 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 + extensions = ["*.wav", "*.flac", "*.mp3"] + for extension in extensions: + wav_fpath_list = speaker_dir.glob(extension) + # 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 - - # 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)) - + sub_basename = "%s_%02d" % (wav_fpath.name, 0) + wav, text = _split_on_silences(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] - -# 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 \ No newline at end of file diff --git a/synthesizer/preprocess_transcript.py b/synthesizer/preprocess_transcript.py new file mode 100644 index 0000000..8810a92 --- /dev/null +++ b/synthesizer/preprocess_transcript.py @@ -0,0 +1,9 @@ +def preprocess_transcript_aishell3(dict_info, dict_transcript): + for v in dict_transcript: + if not v: + continue + v = v.strip().replace("\n","").replace("\t"," ").split(" ") + transList = [] + for i in range(2, len(v), 2): + transList.append(v[i]) + dict_info[v[0]] = " ".join(transList) \ No newline at end of file