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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
from pathlib import Path
import shutil
from tempfile import NamedTemporaryFile
from typing import Optional, 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 torchaudio.datasets.utils import download_url, extract_archive
from tqdm import tqdm
log = logging.getLogger(__name__)
MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"]
class CoVoST(Dataset):
"""Create a Dataset for CoVoST (https://github.com/facebookresearch/covost).
Args:
root (str): root path to the dataset and generated manifests/features
source_language (str): source (audio) language
target_language (str, optional): target (text) language,
None for no translation (default: None)
version (int, optional): CoVoST version. (default: 2)
download (bool, optional): Whether to download the dataset if it is not
found at root path. (default: ``False``).
"""
COVOST_URL_TEMPLATE = (
"https://dl.fbaipublicfiles.com/covost/"
"covost_v2.{src_lang}_{tgt_lang}.tsv.tar.gz"
)
VERSIONS = {2}
SPLITS = ["train", "dev", "test"]
XX_EN_LANGUAGES = {
1: ["fr", "de", "nl", "ru", "es", "it", "tr", "fa", "sv-SE", "mn", "zh-CN"],
2: [
"fr",
"de",
"es",
"ca",
"it",
"ru",
"zh-CN",
"pt",
"fa",
"et",
"mn",
"nl",
"tr",
"ar",
"sv-SE",
"lv",
"sl",
"ta",
"ja",
"id",
"cy",
],
}
EN_XX_LANGUAGES = {
1: [],
2: [
"de",
"tr",
"fa",
"sv-SE",
"mn",
"zh-CN",
"cy",
"ca",
"sl",
"et",
"id",
"ar",
"ta",
"lv",
"ja",
],
}
def __init__(
self,
root: str,
split: str,
source_language: str,
target_language: Optional[str] = None,
version: int = 2,
) -> None:
assert version in self.VERSIONS and split in self.SPLITS
assert source_language is not None
self.no_translation = target_language is None
if not self.no_translation:
assert "en" in {source_language, target_language}
if source_language == "en":
assert target_language in self.EN_XX_LANGUAGES[version]
else:
assert source_language in self.XX_EN_LANGUAGES[version]
else:
# Hack here so that we can get "split" column from CoVoST TSV.
# Note that we use CoVoST train split for ASR which is an extension
# to Common Voice train split.
target_language = "de" if source_language == "en" else "en"
self.root: Path = Path(root)
cv_tsv_path = self.root / "validated.tsv"
assert cv_tsv_path.is_file()
covost_url = self.COVOST_URL_TEMPLATE.format(
src_lang=source_language, tgt_lang=target_language
)
covost_archive = self.root / Path(covost_url).name
if not covost_archive.is_file():
download_url(covost_url, self.root.as_posix(), hash_value=None)
extract_archive(covost_archive.as_posix())
cv_tsv = load_df_from_tsv(cv_tsv_path)
covost_tsv = load_df_from_tsv(
self.root / Path(covost_url).name.replace(".tar.gz", "")
)
df = pd.merge(
left=cv_tsv[["path", "sentence", "client_id"]],
right=covost_tsv[["path", "translation", "split"]],
how="inner",
on="path",
)
if split == "train":
df = df[(df["split"] == split) | (df["split"] == f"{split}_covost")]
else:
df = df[df["split"] == split]
data = df.to_dict(orient="index").items()
data = [v for k, v in sorted(data, key=lambda x: x[0])]
self.data = []
for e in data:
try:
path = self.root / "clips" / e["path"]
_ = torchaudio.info(path.as_posix())
self.data.append(e)
except RuntimeError:
pass
def __getitem__(
self, n: int
) -> Tuple[Tensor, int, str, str, Optional[str], str, str]:
"""Load the n-th sample from the dataset.
Args:
n (int): The index of the sample to be loaded
Returns:
tuple: ``(waveform, sample_rate, sentence, translation, speaker_id,
sample_id)``
"""
data = self.data[n]
path = self.root / "clips" / data["path"]
waveform, sample_rate = torchaudio.load(path)
sentence = data["sentence"]
translation = None if self.no_translation else data["translation"]
speaker_id = data["client_id"]
_id = data["path"].replace(".mp3", "")
return waveform, sample_rate, sentence, translation, speaker_id, _id
def __len__(self) -> int:
return len(self.data)
def process(args):
root = Path(args.data_root).absolute() / args.src_lang
if not root.is_dir():
raise NotADirectoryError(f"{root} does not exist")
# Extract features
feature_root = root / "fbank80"
feature_root.mkdir(exist_ok=True)
for split in CoVoST.SPLITS:
print(f"Fetching split {split}...")
dataset = CoVoST(root, split, args.src_lang, args.tgt_lang)
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 = root / "fbank80.zip"
print("ZIPing features...")
create_zip(feature_root, zip_path)
print("Fetching ZIP manifest...")
audio_paths, audio_lengths = get_zip_manifest(zip_path)
# Generate TSV manifest
print("Generating manifest...")
train_text = []
task = f"asr_{args.src_lang}"
if args.tgt_lang is not None:
task = f"st_{args.src_lang}_{args.tgt_lang}"
for split in CoVoST.SPLITS:
manifest = {c: [] for c in MANIFEST_COLUMNS}
dataset = CoVoST(root, split, args.src_lang, args.tgt_lang)
for _, _, src_utt, tgt_utt, speaker_id, utt_id in tqdm(dataset):
manifest["id"].append(utt_id)
manifest["audio"].append(audio_paths[utt_id])
manifest["n_frames"].append(audio_lengths[utt_id])
manifest["tgt_text"].append(src_utt if args.tgt_lang is None else tgt_utt)
manifest["speaker"].append(speaker_id)
is_train_split = split.startswith("train")
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, root / f"{split}_{task}.tsv")
# 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}_{task}"
with NamedTemporaryFile(mode="w") as f:
for t in train_text:
f.write(t + "\n")
gen_vocab(
Path(f.name),
root / spm_filename_prefix,
args.vocab_type,
args.vocab_size
)
# Generate config YAML
gen_config_yaml(
root,
spm_filename=spm_filename_prefix + ".model",
yaml_filename=f"config_{task}.yaml",
specaugment_policy="lb",
)
# Clean up
shutil.rmtree(feature_root)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data-root", "-d", required=True, type=str,
help="data root with sub-folders for each language <root>/<src_lang>"
)
parser.add_argument(
"--vocab-type",
default="unigram",
required=True,
type=str,
choices=["bpe", "unigram", "char"],
),
parser.add_argument("--vocab-size", default=1000, type=int)
parser.add_argument("--src-lang", "-s", required=True, type=str)
parser.add_argument("--tgt-lang", "-t", type=str)
args = parser.parse_args()
process(args)
if __name__ == "__main__":
main()
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