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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os | |
| import torch | |
| import numpy as np | |
| import json | |
| from tqdm import tqdm | |
| from sklearn.preprocessing import StandardScaler | |
| from utils.io import save_feature, save_txt, save_torch_audio | |
| from utils.util import has_existed | |
| from utils.tokenizer import extract_encodec_token | |
| from utils.stft import TacotronSTFT | |
| from utils.dsp import compress, audio_to_label | |
| from utils.data_utils import remove_outlier | |
| from preprocessors.metadata import replace_augment_name | |
| from scipy.interpolate import interp1d | |
| ZERO = 1e-12 | |
| def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, n_workers=1): | |
| """Extract acoustic features from utterances using muliprocess | |
| Args: | |
| metadata (dict): dictionary that stores data in train.json and test.json files | |
| dataset_output (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1. | |
| Returns: | |
| list: acoustic features | |
| """ | |
| for utt in tqdm(metadata): | |
| if cfg.task_type == "tts": | |
| extract_utt_acoustic_features_tts(dataset_output, cfg, utt) | |
| if cfg.task_type == "svc": | |
| extract_utt_acoustic_features_svc(dataset_output, cfg, utt) | |
| if cfg.task_type == "vocoder": | |
| extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) | |
| if cfg.task_type == "tta": | |
| extract_utt_acoustic_features_tta(dataset_output, cfg, utt) | |
| def avg_phone_feature(feature, duration, interpolation=False): | |
| feature = feature[: sum(duration)] | |
| if interpolation: | |
| nonzero_ids = np.where(feature != 0)[0] | |
| interp_fn = interp1d( | |
| nonzero_ids, | |
| feature[nonzero_ids], | |
| fill_value=(feature[nonzero_ids[0]], feature[nonzero_ids[-1]]), | |
| bounds_error=False, | |
| ) | |
| feature = interp_fn(np.arange(0, len(feature))) | |
| # Phoneme-level average | |
| pos = 0 | |
| for i, d in enumerate(duration): | |
| if d > 0: | |
| feature[i] = np.mean(feature[pos : pos + d]) | |
| else: | |
| feature[i] = 0 | |
| pos += d | |
| feature = feature[: len(duration)] | |
| return feature | |
| def extract_utt_acoustic_features_serial(metadata, dataset_output, cfg): | |
| """Extract acoustic features from utterances (in single process) | |
| Args: | |
| metadata (dict): dictionary that stores data in train.json and test.json files | |
| dataset_output (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| """ | |
| for utt in tqdm(metadata): | |
| if cfg.task_type == "tts": | |
| extract_utt_acoustic_features_tts(dataset_output, cfg, utt) | |
| if cfg.task_type == "svc": | |
| extract_utt_acoustic_features_svc(dataset_output, cfg, utt) | |
| if cfg.task_type == "vocoder": | |
| extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt) | |
| if cfg.task_type == "tta": | |
| extract_utt_acoustic_features_tta(dataset_output, cfg, utt) | |
| def __extract_utt_acoustic_features(dataset_output, cfg, utt): | |
| """Extract acoustic features from utterances (in single process) | |
| Args: | |
| dataset_output (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, | |
| path to utternace, duration, utternace index | |
| """ | |
| from utils import audio, f0, world, duration | |
| uid = utt["Uid"] | |
| wav_path = utt["Path"] | |
| if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)): | |
| wav_path = os.path.join( | |
| dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav" | |
| ) | |
| with torch.no_grad(): | |
| # Load audio data into tensor with sample rate of the config file | |
| wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) | |
| wav = wav_torch.cpu().numpy() | |
| # extract features | |
| if cfg.preprocess.extract_duration: | |
| durations, phones, start, end = duration.get_duration( | |
| utt, wav, cfg.preprocess | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations) | |
| save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones) | |
| wav = wav[start:end].astype(np.float32) | |
| wav_torch = torch.from_numpy(wav).to(wav_torch.device) | |
| if cfg.preprocess.extract_linear_spec: | |
| from utils.mel import extract_linear_features | |
| linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess) | |
| save_feature( | |
| dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy() | |
| ) | |
| if cfg.preprocess.extract_mel: | |
| from utils.mel import extract_mel_features | |
| if cfg.preprocess.mel_extract_mode == "taco": | |
| _stft = TacotronSTFT( | |
| sampling_rate=cfg.preprocess.sample_rate, | |
| win_length=cfg.preprocess.win_size, | |
| hop_length=cfg.preprocess.hop_size, | |
| filter_length=cfg.preprocess.n_fft, | |
| n_mel_channels=cfg.preprocess.n_mel, | |
| mel_fmin=cfg.preprocess.fmin, | |
| mel_fmax=cfg.preprocess.fmax, | |
| ) | |
| mel = extract_mel_features( | |
| wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft | |
| ) | |
| if cfg.preprocess.extract_duration: | |
| mel = mel[:, : sum(durations)] | |
| else: | |
| mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) | |
| save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) | |
| if cfg.preprocess.extract_energy: | |
| if ( | |
| cfg.preprocess.energy_extract_mode == "from_mel" | |
| and cfg.preprocess.extract_mel | |
| ): | |
| energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() | |
| elif cfg.preprocess.energy_extract_mode == "from_waveform": | |
| energy = audio.energy(wav, cfg.preprocess) | |
| elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft": | |
| _stft = TacotronSTFT( | |
| sampling_rate=cfg.preprocess.sample_rate, | |
| win_length=cfg.preprocess.win_size, | |
| hop_length=cfg.preprocess.hop_size, | |
| filter_length=cfg.preprocess.n_fft, | |
| n_mel_channels=cfg.preprocess.n_mel, | |
| mel_fmin=cfg.preprocess.fmin, | |
| mel_fmax=cfg.preprocess.fmax, | |
| ) | |
| _, energy = audio.get_energy_from_tacotron(wav, _stft) | |
| else: | |
| assert cfg.preprocess.energy_extract_mode in [ | |
| "from_mel", | |
| "from_waveform", | |
| "from_tacotron_stft", | |
| ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" | |
| if cfg.preprocess.extract_duration: | |
| energy = energy[: sum(durations)] | |
| phone_energy = avg_phone_feature(energy, durations) | |
| save_feature( | |
| dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) | |
| if cfg.preprocess.extract_pitch: | |
| pitch = f0.get_f0(wav, cfg.preprocess) | |
| if cfg.preprocess.extract_duration: | |
| pitch = pitch[: sum(durations)] | |
| phone_pitch = avg_phone_feature(pitch, durations, interpolation=True) | |
| save_feature( | |
| dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) | |
| if cfg.preprocess.extract_uv: | |
| assert isinstance(pitch, np.ndarray) | |
| uv = pitch != 0 | |
| save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) | |
| if cfg.preprocess.extract_audio: | |
| save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) | |
| if cfg.preprocess.extract_label: | |
| if cfg.preprocess.is_mu_law: | |
| # compress audio | |
| wav = compress(wav, cfg.preprocess.bits) | |
| label = audio_to_label(wav, cfg.preprocess.bits) | |
| save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) | |
| if cfg.preprocess.extract_acoustic_token: | |
| if cfg.preprocess.acoustic_token_extractor == "Encodec": | |
| codes = extract_encodec_token(wav_path) | |
| save_feature( | |
| dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes | |
| ) | |
| # TODO: refactor extract_utt_acoustic_features_task function due to many duplicated code | |
| def extract_utt_acoustic_features_tts(dataset_output, cfg, utt): | |
| """Extract acoustic features from utterances (in single process) | |
| Args: | |
| dataset_output (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, | |
| path to utternace, duration, utternace index | |
| """ | |
| from utils import audio, f0, world, duration | |
| uid = utt["Uid"] | |
| wav_path = utt["Path"] | |
| if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)): | |
| wav_path = os.path.join( | |
| dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav" | |
| ) | |
| if not os.path.exists(wav_path): | |
| wav_path = os.path.join( | |
| dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".flac" | |
| ) | |
| assert os.path.exists(wav_path) | |
| with torch.no_grad(): | |
| # Load audio data into tensor with sample rate of the config file | |
| wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) | |
| wav = wav_torch.cpu().numpy() | |
| # extract features | |
| if cfg.preprocess.extract_duration: | |
| durations, phones, start, end = duration.get_duration( | |
| utt, wav, cfg.preprocess | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations) | |
| save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones) | |
| wav = wav[start:end].astype(np.float32) | |
| wav_torch = torch.from_numpy(wav).to(wav_torch.device) | |
| if cfg.preprocess.extract_linear_spec: | |
| from utils.mel import extract_linear_features | |
| linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess) | |
| save_feature( | |
| dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy() | |
| ) | |
| if cfg.preprocess.extract_mel: | |
| from utils.mel import extract_mel_features | |
| if cfg.preprocess.mel_extract_mode == "taco": | |
| _stft = TacotronSTFT( | |
| sampling_rate=cfg.preprocess.sample_rate, | |
| win_length=cfg.preprocess.win_size, | |
| hop_length=cfg.preprocess.hop_size, | |
| filter_length=cfg.preprocess.n_fft, | |
| n_mel_channels=cfg.preprocess.n_mel, | |
| mel_fmin=cfg.preprocess.fmin, | |
| mel_fmax=cfg.preprocess.fmax, | |
| ) | |
| mel = extract_mel_features( | |
| wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft | |
| ) | |
| if cfg.preprocess.extract_duration: | |
| mel = mel[:, : sum(durations)] | |
| else: | |
| mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) | |
| save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) | |
| if cfg.preprocess.extract_energy: | |
| if ( | |
| cfg.preprocess.energy_extract_mode == "from_mel" | |
| and cfg.preprocess.extract_mel | |
| ): | |
| energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() | |
| elif cfg.preprocess.energy_extract_mode == "from_waveform": | |
| energy = audio.energy(wav, cfg.preprocess) | |
| elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft": | |
| _stft = TacotronSTFT( | |
| sampling_rate=cfg.preprocess.sample_rate, | |
| win_length=cfg.preprocess.win_size, | |
| hop_length=cfg.preprocess.hop_size, | |
| filter_length=cfg.preprocess.n_fft, | |
| n_mel_channels=cfg.preprocess.n_mel, | |
| mel_fmin=cfg.preprocess.fmin, | |
| mel_fmax=cfg.preprocess.fmax, | |
| ) | |
| _, energy = audio.get_energy_from_tacotron(wav, _stft) | |
| else: | |
| assert cfg.preprocess.energy_extract_mode in [ | |
| "from_mel", | |
| "from_waveform", | |
| "from_tacotron_stft", | |
| ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" | |
| if cfg.preprocess.extract_duration: | |
| energy = energy[: sum(durations)] | |
| phone_energy = avg_phone_feature(energy, durations) | |
| save_feature( | |
| dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) | |
| if cfg.preprocess.extract_pitch: | |
| pitch = f0.get_f0(wav, cfg.preprocess) | |
| if cfg.preprocess.extract_duration: | |
| pitch = pitch[: sum(durations)] | |
| phone_pitch = avg_phone_feature(pitch, durations, interpolation=True) | |
| save_feature( | |
| dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) | |
| if cfg.preprocess.extract_uv: | |
| assert isinstance(pitch, np.ndarray) | |
| uv = pitch != 0 | |
| save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) | |
| if cfg.preprocess.extract_audio: | |
| save_torch_audio( | |
| dataset_output, | |
| cfg.preprocess.audio_dir, | |
| uid, | |
| wav_torch, | |
| cfg.preprocess.sample_rate, | |
| ) | |
| if cfg.preprocess.extract_label: | |
| if cfg.preprocess.is_mu_law: | |
| # compress audio | |
| wav = compress(wav, cfg.preprocess.bits) | |
| label = audio_to_label(wav, cfg.preprocess.bits) | |
| save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) | |
| if cfg.preprocess.extract_acoustic_token: | |
| if cfg.preprocess.acoustic_token_extractor == "Encodec": | |
| codes = extract_encodec_token(wav_path) | |
| save_feature( | |
| dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes | |
| ) | |
| def extract_utt_acoustic_features_svc(dataset_output, cfg, utt): | |
| __extract_utt_acoustic_features(dataset_output, cfg, utt) | |
| def extract_utt_acoustic_features_tta(dataset_output, cfg, utt): | |
| __extract_utt_acoustic_features(dataset_output, cfg, utt) | |
| def extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt): | |
| """Extract acoustic features from utterances (in single process) | |
| Args: | |
| dataset_output (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index}, | |
| path to utternace, duration, utternace index | |
| """ | |
| from utils import audio, f0, world, duration | |
| uid = utt["Uid"] | |
| wav_path = utt["Path"] | |
| with torch.no_grad(): | |
| # Load audio data into tensor with sample rate of the config file | |
| wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate) | |
| wav = wav_torch.cpu().numpy() | |
| # extract features | |
| if cfg.preprocess.extract_mel: | |
| from utils.mel import extract_mel_features | |
| mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess) | |
| save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy()) | |
| if cfg.preprocess.extract_energy: | |
| if ( | |
| cfg.preprocess.energy_extract_mode == "from_mel" | |
| and cfg.preprocess.extract_mel | |
| ): | |
| energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy() | |
| elif cfg.preprocess.energy_extract_mode == "from_waveform": | |
| energy = audio.energy(wav, cfg.preprocess) | |
| else: | |
| assert cfg.preprocess.energy_extract_mode in [ | |
| "from_mel", | |
| "from_waveform", | |
| ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]" | |
| save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy) | |
| if cfg.preprocess.extract_pitch: | |
| pitch = f0.get_f0(wav, cfg.preprocess) | |
| save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch) | |
| if cfg.preprocess.extract_uv: | |
| assert isinstance(pitch, np.ndarray) | |
| uv = pitch != 0 | |
| save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv) | |
| if cfg.preprocess.extract_amplitude_phase: | |
| from utils.mel import amplitude_phase_spectrum | |
| log_amplitude, phase, real, imaginary = amplitude_phase_spectrum( | |
| wav_torch.unsqueeze(0), cfg.preprocess | |
| ) | |
| save_feature( | |
| dataset_output, cfg.preprocess.log_amplitude_dir, uid, log_amplitude | |
| ) | |
| save_feature(dataset_output, cfg.preprocess.phase_dir, uid, phase) | |
| save_feature(dataset_output, cfg.preprocess.real_dir, uid, real) | |
| save_feature(dataset_output, cfg.preprocess.imaginary_dir, uid, imaginary) | |
| if cfg.preprocess.extract_audio: | |
| save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav) | |
| if cfg.preprocess.extract_label: | |
| if cfg.preprocess.is_mu_law: | |
| # compress audio | |
| wav = compress(wav, cfg.preprocess.bits) | |
| label = audio_to_label(wav, cfg.preprocess.bits) | |
| save_feature(dataset_output, cfg.preprocess.label_dir, uid, label) | |
| def cal_normalized_mel(mel, dataset_name, cfg): | |
| mel_min, mel_max = load_mel_extrema(cfg, dataset_name) | |
| mel_norm = normalize_mel_channel(mel, mel_min, mel_max) | |
| return mel_norm | |
| def cal_mel_min_max(dataset, output_path, cfg, metadata=None): | |
| dataset_output = os.path.join(output_path, dataset) | |
| if metadata is None: | |
| metadata = [] | |
| for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: | |
| dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type)) | |
| with open(dataset_file, "r") as f: | |
| metadata.extend(json.load(f)) | |
| tmp_mel_min = [] | |
| tmp_mel_max = [] | |
| for item in metadata: | |
| mel_path = os.path.join( | |
| dataset_output, cfg.preprocess.mel_dir, item["Uid"] + ".npy" | |
| ) | |
| if not os.path.exists(mel_path): | |
| continue | |
| mel = np.load(mel_path) | |
| if mel.shape[0] != cfg.preprocess.n_mel: | |
| mel = mel.T | |
| # mel: (n_mels, T) | |
| assert mel.shape[0] == cfg.preprocess.n_mel | |
| tmp_mel_min.append(np.min(mel, axis=-1)) | |
| tmp_mel_max.append(np.max(mel, axis=-1)) | |
| mel_min = np.min(tmp_mel_min, axis=0) | |
| mel_max = np.max(tmp_mel_max, axis=0) | |
| ## save mel min max data | |
| mel_min_max_dir = os.path.join(dataset_output, cfg.preprocess.mel_min_max_stats_dir) | |
| os.makedirs(mel_min_max_dir, exist_ok=True) | |
| mel_min_path = os.path.join(mel_min_max_dir, "mel_min.npy") | |
| mel_max_path = os.path.join(mel_min_max_dir, "mel_max.npy") | |
| np.save(mel_min_path, mel_min) | |
| np.save(mel_max_path, mel_max) | |
| def denorm_for_pred_mels(cfg, dataset_name, split, pred): | |
| """ | |
| Args: | |
| pred: a list whose every element is (frame_len, n_mels) | |
| Return: | |
| similar like pred | |
| """ | |
| mel_min, mel_max = load_mel_extrema(cfg.preprocess, dataset_name) | |
| recovered_mels = [ | |
| denormalize_mel_channel(mel.T, mel_min, mel_max).T for mel in pred | |
| ] | |
| return recovered_mels | |
| def load_mel_extrema(cfg, dataset_name): | |
| data_dir = os.path.join(cfg.processed_dir, dataset_name, cfg.mel_min_max_stats_dir) | |
| min_file = os.path.join(data_dir, "mel_min.npy") | |
| max_file = os.path.join(data_dir, "mel_max.npy") | |
| mel_min = np.load(min_file) | |
| mel_max = np.load(max_file) | |
| return mel_min, mel_max | |
| def denormalize_mel_channel(mel, mel_min, mel_max): | |
| mel_min = np.expand_dims(mel_min, -1) | |
| mel_max = np.expand_dims(mel_max, -1) | |
| return (mel + 1) / 2 * (mel_max - mel_min + ZERO) + mel_min | |
| def normalize_mel_channel(mel, mel_min, mel_max): | |
| mel_min = np.expand_dims(mel_min, -1) | |
| mel_max = np.expand_dims(mel_max, -1) | |
| return (mel - mel_min) / (mel_max - mel_min + ZERO) * 2 - 1 | |
| def normalize(dataset, feat_dir, cfg): | |
| dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset) | |
| print(f"normalize {feat_dir}") | |
| max_value = np.finfo(np.float64).min | |
| min_value = np.finfo(np.float64).max | |
| scaler = StandardScaler() | |
| feat_files = os.listdir(os.path.join(dataset_output, feat_dir)) | |
| for feat_file in tqdm(feat_files): | |
| feat_file = os.path.join(dataset_output, feat_dir, feat_file) | |
| if not feat_file.endswith(".npy"): | |
| continue | |
| feat = np.load(feat_file) | |
| max_value = max(max_value, max(feat)) | |
| min_value = min(min_value, min(feat)) | |
| scaler.partial_fit(feat.reshape((-1, 1))) | |
| mean = scaler.mean_[0] | |
| std = scaler.scale_[0] | |
| stat = np.array([min_value, max_value, mean, std]) | |
| stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") | |
| np.save(stat_npy, stat) | |
| return mean, std, min_value, max_value | |
| def load_normalized(feat_dir, dataset_name, cfg): | |
| dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset_name) | |
| stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy") | |
| min_value, max_value, mean, std = np.load(stat_npy) | |
| return mean, std, min_value, max_value | |
| def cal_pitch_statistics_svc(dataset, output_path, cfg, metadata=None): | |
| # path of dataset | |
| dataset_dir = os.path.join(output_path, dataset) | |
| save_dir = os.path.join(dataset_dir, cfg.preprocess.pitch_dir) | |
| os.makedirs(save_dir, exist_ok=True) | |
| if has_existed(os.path.join(save_dir, "statistics.json")): | |
| return | |
| if metadata is None: | |
| # load singers and ids | |
| singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) | |
| # combine train and test metadata | |
| metadata = [] | |
| for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: | |
| dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) | |
| with open(dataset_file, "r") as f: | |
| metadata.extend(json.load(f)) | |
| else: | |
| singers = list(set([item["Singer"] for item in metadata])) | |
| singers = { | |
| "{}_{}".format(dataset, name): idx for idx, name in enumerate(singers) | |
| } | |
| # use different scalers for each singer | |
| pitch_scalers = [[] for _ in range(len(singers))] | |
| total_pitch_scalers = [[] for _ in range(len(singers))] | |
| for utt_info in tqdm(metadata, desc="Loading F0..."): | |
| # utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' | |
| singer = utt_info["Singer"] | |
| pitch_path = os.path.join( | |
| dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # total_pitch contains all pitch including unvoiced frames | |
| if not os.path.exists(pitch_path): | |
| continue | |
| total_pitch = np.load(pitch_path) | |
| assert len(total_pitch) > 0 | |
| # pitch contains only voiced frames | |
| pitch = total_pitch[total_pitch != 0] | |
| spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] | |
| # update pitch scalers | |
| pitch_scalers[spkid].extend(pitch.tolist()) | |
| # update total pitch scalers | |
| total_pitch_scalers[spkid].extend(total_pitch.tolist()) | |
| # save pitch statistics for each singer in dict | |
| sta_dict = {} | |
| for singer in tqdm(singers, desc="Singers statistics"): | |
| spkid = singers[singer] | |
| # voiced pitch statistics | |
| mean, std, min, max, median = ( | |
| np.mean(pitch_scalers[spkid]), | |
| np.std(pitch_scalers[spkid]), | |
| np.min(pitch_scalers[spkid]), | |
| np.max(pitch_scalers[spkid]), | |
| np.median(pitch_scalers[spkid]), | |
| ) | |
| # total pitch statistics | |
| mean_t, std_t, min_t, max_t, median_t = ( | |
| np.mean(total_pitch_scalers[spkid]), | |
| np.std(total_pitch_scalers[spkid]), | |
| np.min(total_pitch_scalers[spkid]), | |
| np.max(total_pitch_scalers[spkid]), | |
| np.median(total_pitch_scalers[spkid]), | |
| ) | |
| sta_dict[singer] = { | |
| "voiced_positions": { | |
| "mean": mean, | |
| "std": std, | |
| "median": median, | |
| "min": min, | |
| "max": max, | |
| }, | |
| "total_positions": { | |
| "mean": mean_t, | |
| "std": std_t, | |
| "median": median_t, | |
| "min": min_t, | |
| "max": max_t, | |
| }, | |
| } | |
| # save statistics | |
| with open(os.path.join(save_dir, "statistics.json"), "w") as f: | |
| json.dump(sta_dict, f, indent=4, ensure_ascii=False) | |
| def cal_pitch_statistics(dataset, output_path, cfg): | |
| # path of dataset | |
| dataset_dir = os.path.join(output_path, dataset) | |
| if cfg.preprocess.use_phone_pitch: | |
| pitch_dir = cfg.preprocess.phone_pitch_dir | |
| else: | |
| pitch_dir = cfg.preprocess.pitch_dir | |
| save_dir = os.path.join(dataset_dir, pitch_dir) | |
| os.makedirs(save_dir, exist_ok=True) | |
| if has_existed(os.path.join(save_dir, "statistics.json")): | |
| return | |
| # load singers and ids | |
| singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) | |
| # combine train and test metadata | |
| metadata = [] | |
| for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: | |
| dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) | |
| with open(dataset_file, "r") as f: | |
| metadata.extend(json.load(f)) | |
| # use different scalers for each singer | |
| pitch_scalers = [[] for _ in range(len(singers))] | |
| total_pitch_scalers = [[] for _ in range(len(singers))] | |
| for utt_info in metadata: | |
| utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' | |
| singer = utt_info["Singer"] | |
| pitch_path = os.path.join(dataset_dir, pitch_dir, utt_info["Uid"] + ".npy") | |
| # total_pitch contains all pitch including unvoiced frames | |
| if not os.path.exists(pitch_path): | |
| continue | |
| total_pitch = np.load(pitch_path) | |
| assert len(total_pitch) > 0 | |
| # pitch contains only voiced frames | |
| # pitch = total_pitch[total_pitch != 0] | |
| if cfg.preprocess.pitch_remove_outlier: | |
| pitch = remove_outlier(total_pitch) | |
| spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] | |
| # update pitch scalers | |
| pitch_scalers[spkid].extend(pitch.tolist()) | |
| # update total pitch scalers | |
| total_pitch_scalers[spkid].extend(total_pitch.tolist()) | |
| # save pitch statistics for each singer in dict | |
| sta_dict = {} | |
| for singer in singers: | |
| spkid = singers[singer] | |
| # voiced pitch statistics | |
| mean, std, min, max, median = ( | |
| np.mean(pitch_scalers[spkid]), | |
| np.std(pitch_scalers[spkid]), | |
| np.min(pitch_scalers[spkid]), | |
| np.max(pitch_scalers[spkid]), | |
| np.median(pitch_scalers[spkid]), | |
| ) | |
| # total pitch statistics | |
| mean_t, std_t, min_t, max_t, median_t = ( | |
| np.mean(total_pitch_scalers[spkid]), | |
| np.std(total_pitch_scalers[spkid]), | |
| np.min(total_pitch_scalers[spkid]), | |
| np.max(total_pitch_scalers[spkid]), | |
| np.median(total_pitch_scalers[spkid]), | |
| ) | |
| sta_dict[singer] = { | |
| "voiced_positions": { | |
| "mean": mean, | |
| "std": std, | |
| "median": median, | |
| "min": min, | |
| "max": max, | |
| }, | |
| "total_positions": { | |
| "mean": mean_t, | |
| "std": std_t, | |
| "median": median_t, | |
| "min": min_t, | |
| "max": max_t, | |
| }, | |
| } | |
| # save statistics | |
| with open(os.path.join(save_dir, "statistics.json"), "w") as f: | |
| json.dump(sta_dict, f, indent=4, ensure_ascii=False) | |
| def cal_energy_statistics(dataset, output_path, cfg): | |
| # path of dataset | |
| dataset_dir = os.path.join(output_path, dataset) | |
| if cfg.preprocess.use_phone_energy: | |
| energy_dir = cfg.preprocess.phone_energy_dir | |
| else: | |
| energy_dir = cfg.preprocess.energy_dir | |
| save_dir = os.path.join(dataset_dir, energy_dir) | |
| os.makedirs(save_dir, exist_ok=True) | |
| print(os.path.join(save_dir, "statistics.json")) | |
| if has_existed(os.path.join(save_dir, "statistics.json")): | |
| return | |
| # load singers and ids | |
| singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r")) | |
| # combine train and test metadata | |
| metadata = [] | |
| for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: | |
| dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) | |
| with open(dataset_file, "r") as f: | |
| metadata.extend(json.load(f)) | |
| # use different scalers for each singer | |
| energy_scalers = [[] for _ in range(len(singers))] | |
| total_energy_scalers = [[] for _ in range(len(singers))] | |
| for utt_info in metadata: | |
| utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}' | |
| singer = utt_info["Singer"] | |
| energy_path = os.path.join(dataset_dir, energy_dir, utt_info["Uid"] + ".npy") | |
| # total_energy contains all energy including unvoiced frames | |
| if not os.path.exists(energy_path): | |
| continue | |
| total_energy = np.load(energy_path) | |
| assert len(total_energy) > 0 | |
| # energy contains only voiced frames | |
| # energy = total_energy[total_energy != 0] | |
| if cfg.preprocess.energy_remove_outlier: | |
| energy = remove_outlier(total_energy) | |
| spkid = singers[f"{replace_augment_name(dataset)}_{singer}"] | |
| # update energy scalers | |
| energy_scalers[spkid].extend(energy.tolist()) | |
| # update total energyscalers | |
| total_energy_scalers[spkid].extend(total_energy.tolist()) | |
| # save energy statistics for each singer in dict | |
| sta_dict = {} | |
| for singer in singers: | |
| spkid = singers[singer] | |
| # voiced energy statistics | |
| mean, std, min, max, median = ( | |
| np.mean(energy_scalers[spkid]), | |
| np.std(energy_scalers[spkid]), | |
| np.min(energy_scalers[spkid]), | |
| np.max(energy_scalers[spkid]), | |
| np.median(energy_scalers[spkid]), | |
| ) | |
| # total energy statistics | |
| mean_t, std_t, min_t, max_t, median_t = ( | |
| np.mean(total_energy_scalers[spkid]), | |
| np.std(total_energy_scalers[spkid]), | |
| np.min(total_energy_scalers[spkid]), | |
| np.max(total_energy_scalers[spkid]), | |
| np.median(total_energy_scalers[spkid]), | |
| ) | |
| sta_dict[singer] = { | |
| "voiced_positions": { | |
| "mean": mean, | |
| "std": std, | |
| "median": median, | |
| "min": min, | |
| "max": max, | |
| }, | |
| "total_positions": { | |
| "mean": mean_t, | |
| "std": std_t, | |
| "median": median_t, | |
| "min": min_t, | |
| "max": max_t, | |
| }, | |
| } | |
| # save statistics | |
| with open(os.path.join(save_dir, "statistics.json"), "w") as f: | |
| json.dump(sta_dict, f, indent=4, ensure_ascii=False) | |
| def copy_acoustic_features(metadata, dataset_dir, src_dataset_dir, cfg): | |
| """Copy acoustic features from src_dataset_dir to dataset_dir | |
| Args: | |
| metadata (dict): dictionary that stores data in train.json and test.json files | |
| dataset_dir (str): directory to store acoustic features | |
| src_dataset_dir (str): directory to store acoustic features | |
| cfg (dict): dictionary that stores configurations | |
| """ | |
| if cfg.preprocess.extract_mel: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.mel_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.mel_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying mel features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| src_mel_path = os.path.join( | |
| src_dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| dst_mel_path = os.path.join( | |
| dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_mel_path): | |
| os.symlink(src_mel_path, dst_mel_path) | |
| if cfg.preprocess.extract_energy: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.energy_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.energy_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying energy features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| src_energy_path = os.path.join( | |
| src_dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| dst_energy_path = os.path.join( | |
| dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_energy_path): | |
| os.symlink(src_energy_path, dst_energy_path) | |
| if cfg.preprocess.extract_pitch: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.pitch_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.pitch_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying pitch features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| src_pitch_path = os.path.join( | |
| src_dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| dst_pitch_path = os.path.join( | |
| dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_pitch_path): | |
| os.symlink(src_pitch_path, dst_pitch_path) | |
| if cfg.preprocess.extract_uv: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.uv_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.uv_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying uv features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| src_uv_path = os.path.join( | |
| src_dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| dst_uv_path = os.path.join( | |
| dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_uv_path): | |
| os.symlink(src_uv_path, dst_uv_path) | |
| if cfg.preprocess.extract_audio: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.audio_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.audio_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying audio features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| if cfg.task_type == "tts": | |
| src_audio_path = os.path.join( | |
| src_dataset_dir, | |
| cfg.preprocess.audio_dir, | |
| utt_info["Uid"] + ".wav", | |
| ) | |
| else: | |
| src_audio_path = os.path.join( | |
| src_dataset_dir, | |
| cfg.preprocess.audio_dir, | |
| utt_info["Uid"] + ".npy", | |
| ) | |
| if cfg.task_type == "tts": | |
| dst_audio_path = os.path.join( | |
| dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".wav" | |
| ) | |
| else: | |
| dst_audio_path = os.path.join( | |
| dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_audio_path): | |
| os.symlink(src_audio_path, dst_audio_path) | |
| if cfg.preprocess.extract_label: | |
| if not has_existed(os.path.join(dataset_dir, cfg.preprocess.label_dir)): | |
| os.makedirs( | |
| os.path.join(dataset_dir, cfg.preprocess.label_dir), exist_ok=True | |
| ) | |
| print( | |
| "Copying label features from {} to {}...".format( | |
| src_dataset_dir, dataset_dir | |
| ) | |
| ) | |
| for utt_info in tqdm(metadata): | |
| src_label_path = os.path.join( | |
| src_dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| dst_label_path = os.path.join( | |
| dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy" | |
| ) | |
| # create soft-links | |
| if not os.path.exists(dst_label_path): | |
| os.symlink(src_label_path, dst_label_path) | |
| def align_duration_mel(dataset, output_path, cfg): | |
| print("align the duration and mel") | |
| dataset_dir = os.path.join(output_path, dataset) | |
| metadata = [] | |
| for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]: | |
| dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type)) | |
| with open(dataset_file, "r") as f: | |
| metadata.extend(json.load(f)) | |
| utt2dur = {} | |
| for index in tqdm(range(len(metadata))): | |
| utt_info = metadata[index] | |
| dataset = utt_info["Dataset"] | |
| uid = utt_info["Uid"] | |
| utt = "{}_{}".format(dataset, uid) | |
| mel_path = os.path.join(dataset_dir, cfg.preprocess.mel_dir, uid + ".npy") | |
| mel = np.load(mel_path).transpose(1, 0) | |
| duration_path = os.path.join( | |
| dataset_dir, cfg.preprocess.duration_dir, uid + ".npy" | |
| ) | |
| duration = np.load(duration_path) | |
| if sum(duration) != mel.shape[0]: | |
| duration_sum = sum(duration) | |
| mel_len = mel.shape[0] | |
| mismatch = abs(duration_sum - mel_len) | |
| assert mismatch <= 5, "duration and mel length mismatch!" | |
| cloned = np.array(duration, copy=True) | |
| if duration_sum > mel_len: | |
| for j in range(1, len(duration) - 1): | |
| if mismatch == 0: | |
| break | |
| dur_val = cloned[-j] | |
| if dur_val >= mismatch: | |
| cloned[-j] -= mismatch | |
| mismatch -= dur_val | |
| break | |
| else: | |
| cloned[-j] = 0 | |
| mismatch -= dur_val | |
| elif duration_sum < mel_len: | |
| cloned[-1] += mismatch | |
| duration = cloned | |
| utt2dur[utt] = duration | |
| np.save(duration_path, duration) | |
| return utt2dur | |