175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import partial
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from typing import List
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from pathlib import Path
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import multiprocessing as mp
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import numpy as np
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from tqdm import tqdm
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from audio_processor import SpeakerVerificationPreprocessor
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def _process_utterance(path_pair, processor: SpeakerVerificationPreprocessor):
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# Load and preprocess the waveform
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input_path, output_path = path_pair
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wav = processor.preprocess_wav(input_path)
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if len(wav) == 0:
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return
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# Create the mel spectrogram, discard those that are too short
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frames = processor.melspectrogram(wav)
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if len(frames) < processor.partial_n_frames:
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return
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np.save(output_path, frames)
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def _process_speaker(speaker_dir: Path,
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processor: SpeakerVerificationPreprocessor,
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datasets_root: Path,
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output_dir: Path,
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pattern: str,
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skip_existing: bool=False):
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# datastes root: a reference path to compute speaker_name
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# we prepand dataset name to speaker_id becase we are mixing serveal
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# multispeaker datasets together
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speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
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speaker_output_dir = output_dir / speaker_name
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speaker_output_dir.mkdir(parents=True, exist_ok=True)
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# load exsiting file set
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sources_fpath = speaker_output_dir / "_sources.txt"
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if sources_fpath.exists():
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try:
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with sources_fpath.open("rt") as sources_file:
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existing_names = {line.split(",")[0] for line in sources_file}
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except Exception as e:
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existing_names = {}
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else:
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existing_names = {}
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sources_file = sources_fpath.open("at" if skip_existing else "wt")
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for in_fpath in speaker_dir.rglob(pattern):
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out_name = "_".join(
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in_fpath.relative_to(speaker_dir).with_suffix(".npy").parts)
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if skip_existing and out_name in existing_names:
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continue
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out_fpath = speaker_output_dir / out_name
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_process_utterance((in_fpath, out_fpath), processor)
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sources_file.write(f"{out_name},{in_fpath}\n")
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sources_file.close()
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def _process_dataset(processor: SpeakerVerificationPreprocessor,
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datasets_root: Path,
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speaker_dirs: List[Path],
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dataset_name: str,
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output_dir: Path,
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pattern: str,
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skip_existing: bool=False):
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print(
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f"{dataset_name}: Preprocessing data for {len(speaker_dirs)} speakers.")
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_func = partial(
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_process_speaker,
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processor=processor,
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datasets_root=datasets_root,
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output_dir=output_dir,
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pattern=pattern,
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skip_existing=skip_existing)
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with mp.Pool(16) as pool:
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list(
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tqdm(
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pool.imap(_func, speaker_dirs),
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dataset_name,
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len(speaker_dirs),
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unit="speakers"))
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print(f"Done preprocessing {dataset_name}.")
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def process_librispeech(processor,
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datasets_root,
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output_dir,
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skip_existing=False):
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dataset_name = "LibriSpeech/train-other-500"
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dataset_root = datasets_root / dataset_name
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speaker_dirs = list(dataset_root.glob("*"))
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_process_dataset(processor, datasets_root, speaker_dirs, dataset_name,
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output_dir, "*.flac", skip_existing)
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def process_voxceleb1(processor, datasets_root, output_dir,
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skip_existing=False):
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dataset_name = "VoxCeleb1"
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dataset_root = datasets_root / dataset_name
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anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]
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with dataset_root.joinpath("vox1_meta.csv").open("rt") as metafile:
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metadata = [line.strip().split("\t") for line in metafile][1:]
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# speaker id -> nationality
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nationalities = {line[0]: line[3] for line in metadata if line[-1] == "dev"}
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keep_speaker_ids = [
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speaker_id for speaker_id, nationality in nationalities.items()
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if nationality.lower() in anglophone_nationalites
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]
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print(
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"VoxCeleb1: using samples from {} (presumed anglophone) speakers out of {}."
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.format(len(keep_speaker_ids), len(nationalities)))
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speaker_dirs = list((dataset_root / "wav").glob("*"))
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speaker_dirs = [
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speaker_dir for speaker_dir in speaker_dirs
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if speaker_dir.name in keep_speaker_ids
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]
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_process_dataset(processor, datasets_root, speaker_dirs, dataset_name,
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output_dir, "*.wav", skip_existing)
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def process_voxceleb2(processor, datasets_root, output_dir,
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skip_existing=False):
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dataset_name = "VoxCeleb2"
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dataset_root = datasets_root / dataset_name
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# There is no nationality in meta data for VoxCeleb2
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speaker_dirs = list((dataset_root / "wav").glob("*"))
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_process_dataset(processor, datasets_root, speaker_dirs, dataset_name,
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output_dir, "*.wav", skip_existing)
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def process_aidatatang_200zh(processor,
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datasets_root,
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output_dir,
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skip_existing=False):
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dataset_name = "aidatatang_200zh/train"
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dataset_root = datasets_root / dataset_name
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speaker_dirs = list((dataset_root).glob("*"))
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_process_dataset(processor, datasets_root, speaker_dirs, dataset_name,
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output_dir, "*.wav", skip_existing)
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def process_magicdata(processor, datasets_root, output_dir,
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skip_existing=False):
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dataset_name = "magicdata/train"
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dataset_root = datasets_root / dataset_name
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speaker_dirs = list((dataset_root).glob("*"))
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_process_dataset(processor, datasets_root, speaker_dirs, dataset_name,
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output_dir, "*.wav", skip_existing)
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