fairseq/examples/speech_to_text/prep_mtedx_data.py

#!/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 soundfile as sf
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,
)
import torch
from torch.utils.data import Dataset
from tqdm import tqdm

from fairseq.data.audio.audio_utils import get_waveform, convert_waveform


log = logging.getLogger(__name__)


MANIFEST_COLUMNS = [
    "id", "audio", "n_frames", "tgt_text", "speaker", "tgt_lang"
]


class mTEDx(Dataset):
    """
    Create a Dataset for Multilingual TEDx.
    Each item is a tuple of the form: waveform, sample_rate, source utterance,
    target utterance, speaker_id, utterance_id
    """

    SPLITS = ["train", "valid", "test"]
    LANGPAIRS = ["es-es", "fr-fr", "pt-pt", "it-it", "ru-ru", "el-el", "ar-ar",
                 "de-de", "es-en", "es-fr", "es-pt", "es-it", "fr-en", "fr-es",
                 "fr-pt", "pt-en", "pt-es", "it-en", "it-es", "ru-en", "el-en"]

    def __init__(self, root: str, lang: str, split: str) -> None:
        assert split in self.SPLITS and lang in self.LANGPAIRS
        _root = Path(root) / f"{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 Multilingual TEDx YAML files"
            )
        with open(txt_root / f"{split}.yaml") as f:
            segments = yaml.load(f, Loader=yaml.BaseLoader)
        # Load source and target utterances
        src, tgt = lang.split("-")
        for _lang in [src, tgt]:
            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_filename = wav_filename.replace(".wav", ".flac")
            wav_path = wav_root / wav_filename
            sample_rate = sf.info(wav_path.as_posix()).samplerate
            seg_group = sorted(_seg_group, key=lambda x: float(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[src],
                        segment[tgt],
                        segment["speaker_id"],
                        tgt,
                        _id,
                    )
                )

    def __getitem__(
            self, n: int
    ) -> Tuple[torch.Tensor, int, str, str, str, str, str]:
        wav_path, offset, n_frames, sr, src_utt, tgt_utt, spk_id, tgt_lang, \
            utt_id = self.data[n]
        waveform, _ = get_waveform(wav_path, frames=n_frames, start=offset)
        waveform = torch.from_numpy(waveform)
        return waveform, sr, src_utt, tgt_utt, spk_id, tgt_lang, utt_id

    def __len__(self) -> int:
        return len(self.data)


def process(args):
    root = Path(args.data_root).absolute()
    for lang in mTEDx.LANGPAIRS:
        cur_root = root / f"{lang}"
        if not cur_root.is_dir():
            print(f"{cur_root.as_posix()} does not exist. Skipped.")
            continue
        # Extract features
        audio_root = cur_root / ("flac" if args.use_audio_input else "fbank80")
        audio_root.mkdir(exist_ok=True)
        for split in mTEDx.SPLITS:
            print(f"Fetching split {split}...")
            dataset = mTEDx(root.as_posix(), lang, split)
            if args.use_audio_input:
                print("Converting audios...")
                for waveform, sample_rate, _, _, _, utt_id in tqdm(dataset):
                    tgt_sample_rate = 16_000
                    _wavform, _ = convert_waveform(
                        waveform, sample_rate, to_mono=True,
                        to_sample_rate=tgt_sample_rate
                    )
                    sf.write(
                        (audio_root / f"{utt_id}.flac").as_posix(),
                        _wavform.numpy(), tgt_sample_rate
                    )
            else:
                print("Extracting log mel filter bank features...")
                for waveform, sample_rate, _, _, _, _, utt_id in tqdm(dataset):
                    extract_fbank_features(
                        waveform, sample_rate, audio_root / f"{utt_id}.npy"
                    )
        # Pack features into ZIP
        zip_path = cur_root / f"{audio_root.name}.zip"
        print("ZIPing audios/features...")
        create_zip(audio_root, zip_path)
        print("Fetching ZIP manifest...")
        audio_paths, audio_lengths = get_zip_manifest(zip_path)
        # Generate TSV manifest
        print("Generating manifest...")
        train_text = []
        for split in mTEDx.SPLITS:
            is_train_split = split.startswith("train")
            manifest = {c: [] for c in MANIFEST_COLUMNS}
            ds = mTEDx(args.data_root, lang, split)
            for _, _, src_utt, tgt_utt, spk_id, tgt_lang, utt_id in tqdm(ds):
                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.task == "asr" else tgt_utt
                )
                manifest["speaker"].append(spk_id)
                manifest["tgt_lang"].append(tgt_lang)
            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
        if args.use_audio_input:
            gen_config_yaml(
                cur_root,
                spm_filename=spm_filename_prefix + ".model",
                yaml_filename=f"config_{args.task}.yaml",
                specaugment_policy=None,
                extra={"use_audio_input": True}
            )
        else:
            gen_config_yaml(
                cur_root,
                spm_filename=spm_filename_prefix + ".model",
                yaml_filename=f"config_{args.task}.yaml",
                specaugment_policy="lb",
            )
        # Clean up
        shutil.rmtree(audio_root)


def process_joint(args):
    cur_root = Path(args.data_root)
    assert all((cur_root / f"{lang}").is_dir() for lang in mTEDx.LANGPAIRS), \
        "do not have downloaded data available for all 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 mTEDx.LANGPAIRS:
            tsv_path = cur_root / f"{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.joint:
            # Add tgt_lang tags to dict
            special_symbols = list(
                {f'<lang:{lang.split("-")[1]}>' for lang in mTEDx.LANGPAIRS}
            )
        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=spm_filename_prefix + ".model",
        yaml_filename=f"config_{args.task}.yaml",
        specaugment_policy="ld",
        prepend_tgt_lang_tag=(args.joint),
    )
    # Make symbolic links to manifests
    for lang in mTEDx.LANGPAIRS:
        for split in mTEDx.SPLITS:
            src_path = cur_root / f"{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="")
    parser.add_argument("--use-audio-input", action="store_true")
    args = parser.parse_args()

    if args.joint:
        process_joint(args)
    else:
        process(args)


if __name__ == "__main__":
    main()