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prep_data.py
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prep_data.py
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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from tempfile import NamedTemporaryFile
from typing import Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filter_manifest_df,
gen_config_yaml,
gen_vocab,
get_zip_manifest,
load_df_from_tsv,
save_df_to_tsv,
)
from torch import Tensor
from torch.utils.data import Dataset
from tqdm import tqdm
log = logging.getLogger(__name__)
MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"]
class MUSTC(Dataset):
"""
Create a Dataset for MuST-C. Each item is a tuple of the form:
waveform, sample_rate, source utterance, target utterance, speaker_id,
utterance_id
"""
SPLITS = ["train", "dev", "test"]
LANGUAGES = ["de", "es", "fr", "it", "nl", "pt", "ro", "ru", "en"]
def __init__(self, root: str, lang: str, split: str) -> None:
assert split in self.SPLITS and lang in self.LANGUAGES
_root = Path(root) / f"kr-{lang}" / "data" / split
wav_root, txt_root = _root / "wav", _root / "txt"
assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir()
# Load audio segments
try:
import yaml
except ImportError:
print("Please install PyYAML to load the MuST-C YAML files")
with open(txt_root / f"{split}.yaml") as f:
segments = yaml.load(f, Loader=yaml.BaseLoader)
# Load source and target utterances
for _lang in ["kr", lang]:
with open(txt_root / f"{split}.{_lang}") as f:
utterances = [r.strip() for r in f]
assert len(segments) == len(utterances)
for i, u in enumerate(utterances):
segments[i][_lang] = u
# Gather info
self.data = []
for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]):
wav_path = wav_root / wav_filename
sample_rate = torchaudio.info(wav_path.as_posix())[0].rate
seg_group = sorted(_seg_group, key=lambda x: x["offset"])
for i, segment in enumerate(seg_group):
offset = int(float(segment["offset"]) * sample_rate)
n_frames = int(float(segment["duration"]) * sample_rate)
_id = f"{wav_path.stem}_{i}"
self.data.append(
(
wav_path.as_posix(),
offset,
n_frames,
sample_rate,
segment["kr"],
segment[lang],
segment["speaker_id"],
_id,
)
)
def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str, str]:
wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, utt_id = self.data[n]
waveform, _ = torchaudio.load(wav_path, offset=offset, num_frames=n_frames)
return waveform, sr, src_utt, tgt_utt, spk_id, utt_id
def __len__(self) -> int:
return len(self.data)
def process(args):
root = Path(args.data_root).absolute()
for lang in MUSTC.LANGUAGES:
cur_root = root / f"kr-{lang}"
if not cur_root.is_dir():
print(f"{cur_root.as_posix()} does not exist. Skipped.")
continue
# Extract features
feature_root = cur_root / "fbank80"
feature_root.mkdir(exist_ok=True)
for split in MUSTC.SPLITS:
print(f"Fetching split {split}...")
dataset = MUSTC(root.as_posix(), lang, split)
print("Extracting log mel filter bank features...")
for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset):
extract_fbank_features(
waveform, sample_rate, feature_root / f"{utt_id}.npy"
)
# Pack features into ZIP
zip_path = cur_root / "fbank80.zip"
print("ZIPing features...")
create_zip(feature_root, zip_path)
print("Fetching ZIP manifest...")
zip_manifest = get_zip_manifest(zip_path)
# Generate TSV manifest
print("Generating manifest...")
train_text = []
for split in MUSTC.SPLITS:
is_train_split = split.startswith("train")
manifest = {c: [] for c in MANIFEST_COLUMNS}
dataset = MUSTC(args.data_root, lang, split)
for wav, sr, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset):
manifest["id"].append(utt_id)
manifest["audio"].append(zip_manifest[utt_id])
duration_ms = int(wav.size(1) / sr * 1000)
manifest["n_frames"].append(int(1 + (duration_ms - 25) / 10))
manifest["tgt_text"].append(src_utt if args.task == "asr" else tgt_utt)
manifest["speaker"].append(speaker_id)
if is_train_split:
train_text.extend(manifest["tgt_text"])
df = pd.DataFrame.from_dict(manifest)
df = filter_manifest_df(df, is_train_split=is_train_split)
save_df_to_tsv(df, cur_root / f"{split}_{args.task}.tsv")
# Generate vocab
v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}"
with NamedTemporaryFile(mode="w") as f:
for t in train_text:
f.write(t + "\n")
gen_vocab(
Path(f.name),
cur_root / spm_filename_prefix,
args.vocab_type,
args.vocab_size,
)
# Generate config YAML
gen_config_yaml(
cur_root,
spm_filename_prefix + ".model",
yaml_filename=f"config_{args.task}.yaml",
specaugment_policy="lb",
)
# Clean up
shutil.rmtree(feature_root)
def process_joint(args):
cur_root = Path(args.data_root)
assert all((cur_root / f"kr-{lang}").is_dir() for lang in MUSTC.LANGUAGES), \
"do not have downloaded data available for all 8 languages"
# Generate vocab
vocab_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
spm_filename_prefix = f"spm_{args.vocab_type}{vocab_size_str}_{args.task}"
with NamedTemporaryFile(mode="w") as f:
for lang in MUSTC.LANGUAGES:
tsv_path = cur_root / f"kr-{lang}" / f"train_{args.task}.tsv"
df = load_df_from_tsv(tsv_path)
for t in df["tgt_text"]:
f.write(t + "\n")
special_symbols = None
if args.task == 'st':
special_symbols = [f'<lang:{lang}>' for lang in MUSTC.LANGUAGES]
gen_vocab(
Path(f.name),
cur_root / spm_filename_prefix,
args.vocab_type,
args.vocab_size,
special_symbols=special_symbols
)
# Generate config YAML
gen_config_yaml(
cur_root,
spm_filename_prefix + ".model",
yaml_filename=f"config_{args.task}.yaml",
specaugment_policy="ld",
prepend_tgt_lang_tag=(args.task == "st"),
)
# Make symbolic links to manifests
for lang in MUSTC.LANGUAGES:
for split in MUSTC.SPLITS:
src_path = cur_root / f"kr-{lang}" / f"{split}_{args.task}.tsv"
desc_path = cur_root / f"{split}_{lang}_{args.task}.tsv"
if not desc_path.is_symlink():
os.symlink(src_path, desc_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data-root", "-d", required=True, type=str)
parser.add_argument(
"--vocab-type",
default="unigram",
required=True,
type=str,
choices=["bpe", "unigram", "char"],
),
parser.add_argument("--vocab-size", default=8000, type=int)
parser.add_argument("--task", type=str, choices=["asr", "st"])
parser.add_argument("--joint", action="store_true", help="")
args = parser.parse_args()
if args.joint:
process_joint(args)
else:
process(args)
if __name__ == "__main__":
main()