multilingual-TEDX-fr.py

import os
import re
from ctypes import Array
from dataclasses import dataclass
from typing import List, Tuple
from pathlib import Path
import xml.etree.ElementTree as ET
import csv
import datasets
import numpy as np
try:
    import ffmpeg
    FFMPEG_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
    import librosa
    FFMPEG_AVAILABLE = False

_CITATION = """\
  @inproceedings{salesky2021mtedx,
    title={Multilingual TEDx Corpus for Speech Recognition and Translation},
    author={Elizabeth Salesky and Matthew Wiesner and Jacob Bremerman and Roldano Cattoni and Matteo Negri and Marco Turchi and Douglas W. Oard and Matt Post},
    booktitle={Proceedings of Interspeech},
    year={2021},
  }
"""

_DESCRIPTION = """\
French subpart of the multilingual TEDX dataset 
"""
SAMPLING_RATE = 16_000

@dataclass
class Utterance:
    speaker_id: str
    index: int
    sentence: str
    start_timestamp: float
    end_timestamp: float


class TEDXConfig(datasets.BuilderConfig):
    """BuilderConfig for TEDX."""

    def __init__(self, name, **kwargs):
        """
        Args:
          name: `string`, name of dataset config (=language)
          **kwargs: keyword arguments forwarded to super.
        """
        super(TEDXConfig, self).__init__(
            version=datasets.Version("2.14.5", ""), name=name, **kwargs
        )
        self.single_samples = (name == "single_samples")
        self.max = (name == "max")
        if not self.single_samples and not self.max:
            self.max_duration = float(name.split("=")[1][:-1])
        else:
            self.max_duration = np.inf


class TEDX(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        TEDXConfig(name="single_samples", description="all samples taken separately, can be very short and imprecise"),
        TEDXConfig(name="max", description="all samples of a talk are merged together"),
        TEDXConfig(name="max=30s", description="samples are merged in order to reach a max duration of 30 seconds."
                                               "Does not remove single utterances that may exceed "
                                               "the maximum duration"),

        TEDXConfig(name="max=10s", description="samples are merged in order to reach a max duration of 10 seconds"
                                               "Does not remove single utterances that may exceed "
                                               "the maximum duration"),
    ]

    DEFAULT_CONFIG_NAME = "single_samples"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=SAMPLING_RATE),
                    "sentence": datasets.Value("string"),
                    "speaker_id": datasets.Value("string"),
                    "start_timestamp": datasets.Value("float"),
                    "end_timestamp": datasets.Value("float"),
                    "index": datasets.Value("int32"),
                }
            ),
            citation=_CITATION,
        )

    def _split_by_audio_file(self, segments_path: str, sentences_path: str, split_name: str) -> Tuple[List[str], List[List[Utterance]]]:
        speaker_paths = []
        seen_ids = set()
        segments_by_speaker = []
        with open(segments_path, "r") as segments, open(sentences_path) as sentences:
            segments_reader = csv.DictReader(segments, delimiter=' ', fieldnames=["segment_id", "speaker_id", "start_timestamp", "end_timestamp"])
            sentences_list = sentences.readlines()
            for segment, sentence in zip(segments_reader, sentences_list):
                if segment["speaker_id"] not in seen_ids:
                    seen_ids.add(segment["speaker_id"])
                    speaker_paths.append(Path("data") / Path(split_name) / Path("wav") / Path(f"{segment['speaker_id']}.flac"))
                    segments_by_speaker.append([])
                segments_by_speaker[-1].append(Utterance(speaker_id=segment["speaker_id"],
                                              index=int(segment["segment_id"].split("_")[-1]),
                                              sentence=sentence,
                                              start_timestamp=float(segment["start_timestamp"]),
                                              end_timestamp=float(segment["end_timestamp"])
                                              ))
        return speaker_paths, segments_by_speaker
                


    def _split_generators(self, dl_manager):
        segments = {
            "train": dl_manager.download("data/train/txt/segments"),
            "test": dl_manager.download("data/test/txt/segments"),
            "valid": dl_manager.download("data/valid/txt/segments")
        }
        sentences = {
            "train": dl_manager.download("data/train/txt/train.fr"),
            "test": dl_manager.download("data/test/txt/test.fr"),
            "valid": dl_manager.download("data/valid/txt/valid.fr"),
        }

        splitted_dataset = {}
        segments = dl_manager.download(segments)
        sentences = dl_manager.download(sentences)
        for split in segments:
            audios_path, utterances = self._split_by_audio_file(segments[split], sentences[split], split)
            audios_path = dl_manager.download(audios_path)
            splitted_dataset[split] = {
                "audios_path": audios_path,
                "utterances": utterances
                }

        splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs= splitted_dataset["train"]
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs=splitted_dataset["test"]
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs=splitted_dataset["test"]
            ),
        ]

        return splits
    
    @staticmethod
    def merge_utterances(utterance1: Utterance, utterance2: Utterance) -> Utterance:
        assert(utterance1.speaker_id == utterance2.speaker_id)
        assert(utterance2.index > utterance1.index)
        return Utterance(
            speaker_id=utterance1.speaker_id,
            sentence=re.sub(r"\s+", " ", utterance1.sentence + " " + utterance2.sentence),
            start_timestamp=utterance1.start_timestamp,
            end_timestamp=utterance2.end_timestamp,
            index = utterance1.index
        )



    def _merged_utterances_iterator(self, utterances: List[Utterance]):
        utterances = iter(utterances)
        if self.config.single_samples:
            yield from utterances
            return
        merged_utterance = next(utterances)
        start_time = merged_utterance.start_timestamp
        while True:
            try:
                new_utterance = next(utterances)
            except StopIteration:
                yield merged_utterance
                return
            end_time = new_utterance.end_timestamp
            if end_time - start_time > self.config.max_duration:
                yield merged_utterance
                merged_utterance = new_utterance
                start_time = merged_utterance.start_timestamp
            else:
                merged_utterance = TEDX.merge_utterances(merged_utterance, new_utterance)



    @staticmethod
    def load_audio(file: str, sr: int = SAMPLING_RATE):
        """
        Open an audio file and read as mono waveform, resampling as necessary
        Parameters
        ----------
        file:vThe audio file to read
        sr: int
            The sample rate to resample the audio if necessary
        Returns
        -------
        A NumPy array containing the audio waveform, in float32 dtype.
        """
        #import librosa
        #with open(file, "rb") as f:
        #    return librosa.load(f, sr=sr)
        if FFMPEG_AVAILABLE:
            try:
                # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
                # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
                out, _ = (
                    ffmpeg.input(file)
                    .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=sr)
                    .run(capture_stdout=True, capture_stderr=True)
                )
            except ffmpeg.Error as e:
                raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
            return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
        else:
            with open(file, "rb") as f:
                return librosa.load(f, sr=sr)


    @staticmethod
    def _cut_audio(audio: Array, start_timestamp: float, end_timestamp: float):
        return audio[int(round(start_timestamp * SAMPLING_RATE)): int(round(end_timestamp * SAMPLING_RATE)) + 1]

    def _generate_examples(self, audios_path: List[str], utterances: List[List[Utterance]]):
        """Generate examples from a Multilingual LibriSpeech data dir."""
        for audio_path, utterances in zip(audios_path, utterances):
            audio = self.load_audio(audio_path)
            for utterance in self._merged_utterances_iterator(utterances):
                transcript_name = f"{utterance.speaker_id}-{utterance.index}"
                start_timestamp = float(utterance.start_timestamp)
                end_timestamp = float(utterance.end_timestamp)
                yield transcript_name, {
                    "file": transcript_name,
                    "index": utterance.index,
                    "sentence": utterance.sentence,
                    "start_timestamp": start_timestamp,
                    "end_timestamp": end_timestamp,
                    "speaker_id": utterance.speaker_id,
                    "audio": {"path": transcript_name,
                                "array": self._cut_audio(audio, start_timestamp, end_timestamp),
                                "sampling_rate": SAMPLING_RATE}}