mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
116 lines
5.2 KiB
Python
116 lines
5.2 KiB
Python
from multiprocessing.pool import Pool
|
|
|
|
from functools import partial
|
|
from itertools import chain
|
|
from pathlib import Path
|
|
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, preprocess_transcript_magicdata
|
|
|
|
data_info = {
|
|
"aidatatang_200zh": {
|
|
"subfolders": ["corpus/train"],
|
|
"trans_filepath": "transcript/aidatatang_200_zh_transcript.txt",
|
|
"speak_func": preprocess_speaker_general
|
|
},
|
|
"magicdata": {
|
|
"subfolders": ["train"],
|
|
"trans_filepath": "train/TRANS.txt",
|
|
"speak_func": preprocess_speaker_general,
|
|
"transcript_func": preprocess_transcript_magicdata,
|
|
},
|
|
"aishell3":{
|
|
"subfolders": ["train/wav"],
|
|
"trans_filepath": "train/content.txt",
|
|
"speak_func": preprocess_speaker_general,
|
|
"transcript_func": preprocess_transcript_aishell3,
|
|
}
|
|
}
|
|
|
|
def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int,
|
|
skip_existing: bool, hparams, no_alignments: bool,
|
|
dataset: str):
|
|
dataset_info = data_info[dataset]
|
|
# Gather the input directories
|
|
dataset_root = datasets_root.joinpath(dataset)
|
|
input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in dataset_info["subfolders"]]
|
|
print("\n ".join(map(str, ["Using data from:"] + input_dirs)))
|
|
assert all(input_dir.exists() for input_dir in input_dirs)
|
|
|
|
# Create the output directories for each output file type
|
|
out_dir.joinpath("mels").mkdir(exist_ok=True)
|
|
out_dir.joinpath("audio").mkdir(exist_ok=True)
|
|
|
|
# Create a metadata file
|
|
metadata_fpath = out_dir.joinpath("train.txt")
|
|
metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8")
|
|
|
|
# Preprocess the dataset
|
|
dict_info = {}
|
|
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:
|
|
# 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,
|
|
hparams=hparams, dict_info=dict_info, no_alignments=no_alignments)
|
|
job = Pool(n_processes).imap(func, speaker_dirs)
|
|
for speaker_metadata in tqdm(job, dataset, len(speaker_dirs), unit="speakers"):
|
|
for metadatum in speaker_metadata:
|
|
metadata_file.write("|".join(str(x) for x in metadatum) + "\n")
|
|
metadata_file.close()
|
|
|
|
# Verify the contents of the metadata file
|
|
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
|
|
metadata = [line.split("|") for line in metadata_file]
|
|
mel_frames = sum([int(m[4]) for m in metadata])
|
|
timesteps = sum([int(m[3]) for m in metadata])
|
|
sample_rate = hparams.sample_rate
|
|
hours = (timesteps / sample_rate) / 3600
|
|
print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." %
|
|
(len(metadata), mel_frames, timesteps, hours))
|
|
print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
|
|
print("Max mel frames length: %d" % max(int(m[4]) for m in metadata))
|
|
print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata))
|
|
|
|
def embed_utterance(fpaths, encoder_model_fpath):
|
|
if not encoder.is_loaded():
|
|
encoder.load_model(encoder_model_fpath)
|
|
|
|
# Compute the speaker embedding of the utterance
|
|
wav_fpath, embed_fpath = fpaths
|
|
wav = np.load(wav_fpath)
|
|
wav = encoder.preprocess_wav(wav)
|
|
embed = encoder.embed_utterance(wav)
|
|
np.save(embed_fpath, embed, allow_pickle=False)
|
|
|
|
|
|
def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int):
|
|
wav_dir = synthesizer_root.joinpath("audio")
|
|
metadata_fpath = synthesizer_root.joinpath("train.txt")
|
|
assert wav_dir.exists() and metadata_fpath.exists()
|
|
embed_dir = synthesizer_root.joinpath("embeds")
|
|
embed_dir.mkdir(exist_ok=True)
|
|
|
|
# Gather the input wave filepath and the target output embed filepath
|
|
with metadata_fpath.open("r", encoding="utf-8") as metadata_file:
|
|
metadata = [line.split("|") for line in metadata_file]
|
|
fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
|
|
|
|
# TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
|
|
# Embed the utterances in separate threads
|
|
func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
|
|
job = Pool(n_processes).imap(func, fpaths)
|
|
list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
|