Update sharded gzip paths

tedlium.py

@@ -11,9 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""The Switchboard conversational dataset for automatic speech recognition."""
+
+"""TED-LIUM speech recognition dataset."""
 
 import os
+import re
+from collections import defaultdict
+from io import BytesIO
 from pathlib import Path
 
 import numpy as np
@@ -23,107 +27,359 @@ import datasets
 from datasets.tasks import AutomaticSpeechRecognition
 
 
-_CITATION = """\
-@INPROCEEDINGS{225858,
-  author={Godfrey, J.J. and Holliman, E.C. and McDaniel, J.},
-  booktitle={[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing}, 
-  title={SWITCHBOARD: telephone speech corpus for research and development}, 
-  year={1992},
-  volume={1},
-  number={},
-  pages={517-520 vol.1},
-  doi={10.1109/ICASSP.1992.225858}}
+_DL_URL = "https://huggingface.co/datasets/LIUM/tedlium/resolve/main/"
+
+_LICENSE = "licensed under Creative Commons BY-NC-ND 3.0 (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en)"
+
+
+class TedliumReleaseConfig(datasets.BuilderConfig):
+    """BuilderConfig for a release of the TED-LIUM dataset."""
+
+    def __init__(self, *, url, download_urls, split_paths, citation, **kwargs):
+        super(TedliumReleaseConfig, self).__init__(version=datasets.Version("1.0.1"), **kwargs)
+        self.url = url
+        self.download_urls = download_urls
+        # List of split, path pairs containing the relative path within the
+        # extracted tarball to the data for each split.
+        self.split_paths = split_paths
+        self.citation = citation
+
+
+def _make_builder_configs():
+    """Creates builder configs for all supported Tedlium dataset releases."""
+    release1 = TedliumReleaseConfig(
+        name="release1",
+        description="""\
+        The TED-LIUM corpus is English-language TED talks, with transcriptions,
+        sampled at 16kHz. It contains about 118 hours of speech.
+
+        This is the TED-LIUM corpus release 1,
+        licensed under Creative Commons BY-NC-ND 3.0
+        (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).
+        """,
+        citation="""\
+        @inproceedings{rousseau2012tedlium,
+          title={TED-LIUM: an Automatic Speech Recognition dedicated corpus},
+          author={Rousseau, Anthony and Del{\\'e}glise, Paul and Est{\\`e}ve, Yannick},
+          booktitle={Conference on Language Resources and Evaluation (LREC)},
+          pages={125--129},
+          year={2012}
+        }
+        """,
+        url="https://www.openslr.org/7/",
+        download_urls={
+            "train": [_DL_URL + os.path.join("TEDLIUM_release1", "train.tar.gz")],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release1", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release1", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    release2 = TedliumReleaseConfig(
+        name="release2",
+        description="""\
+        This is the TED-LIUM corpus release 2,
+        licensed under Creative Commons BY-NC-ND 3.0
+        (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en).
+
+        All talks and text are property of TED Conferences LLC.
+
+        The TED-LIUM corpus was made from audio talks and their transcriptions
+        available on the TED website. We have prepared and filtered these data
+        in order to train acoustic models to participate to the International
+        Workshop on Spoken Language Translation 2011 (the LIUM English/French
+        SLT system reached the first rank in the SLT task).
+
+        Contains 1495 talks and transcripts.
+        """,
+        citation="""\
+        @inproceedings{rousseau2014tedlium2,
+          title={Enhancing the {TED-LIUM} Corpus with Selected Data for Language Modeling and More {TED} Talks},
+          author={Rousseau, Anthony and Del{\\'e}glise, Paul and Est{\\`e}ve, Yannick},
+          booktitle={Conference on Language Resources and Evaluation (LREC)},
+          year={2014}
+        }
+        """,
+        url="https://www.openslr.org/19/",
+        download_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release2", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release2", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release2", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release2", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    release3 = TedliumReleaseConfig(
+        name="release3",
+        description="""\
+        This is the TED-LIUM corpus release 3, licensed under Creative Commons
+        BY-NC-ND 3.0. This is the 'legacy' version of the corpus, in which the dev and test datasets are the same as in
+        TED-LIUM 2 (and TED-LIUM 1).
 
-"""
+        All talks and text are property of TED Conferences LLC.
 
-_DESCRIPTION = """\
-Switchboard is a collection of about 2,400 two-sided telephone conversations among 543 speakers (302 male, 241 female) from all areas of the United States. A computer-driven robot operator system handled the calls, giving the caller appropriate recorded prompts, selecting and dialing another person (the callee) to take part in a conversation, introducing a topic for discussion and recording the speech from the two subjects into separate channels until the conversation was finished. About 70 topics were provided, of which about 50 were used frequently. Selection of topics and callees was constrained so that: (1) no two speakers would converse together more than once and (2) no one spoke more than once on a given topic.
-"""
+        This new TED-LIUM release was made through a collaboration between the
+        Ubiqus company and the LIUM (University of Le Mans, France)
 
-_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC97S62"
+        Contents:
 
-_LICENSE = "https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf"
+        - 2351 audio talks in NIST sphere format (SPH), including talks from
+          TED-LIUM 2: be careful, same talks but not same audio files (only
+          these audio file must be used with the TED-LIUM 3 STM files)
+        - 452 hours of audio
+        - 2351 aligned automatic transcripts in STM format
+        - TEDLIUM 2 dev and test data: 19 TED talks in SPH format with
+          corresponding manual transcriptions.
+        - Dictionary with pronunciations (159848 entries), same file as the one
+          included in TED-LIUM 2
+        - Selected monolingual data for language modeling from WMT12 publicly
+          available corpora: these files come from the TED-LIUM 2 release, but
+          have been modified to get a tokenization more relevant for English
+          language
 
-# Replace with the URL of your download link
-_URL = "https://huggingface.co/datasets/speech-seq2seq/switchboard/resolve/main/switchboard.tar.gz"
+        """,
+        citation="""\
+        @inproceedings{hernandez2018tedlium3,
+          title={TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation},
+          author={Hernandez, Fran{\\c{c}}ois and Nguyen, Vincent and Ghannay, Sahar and Tomashenko, Natalia and Est{\\`e}ve, Yannick},
+          booktitle={International Conference on Speech and Computer},
+          pages={198--208},
+          year={2018},
+          organization={Springer}
+        }
+        """,
+        url="https://www.openslr.org/51/",
+        download_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release3", "legacy", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release3", "legacy", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release3", "legacy", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release3", "legacy", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
 
+    release3_speaker_adaptation = TedliumReleaseConfig(
+        name="release3-speaker-adaptation",
+        description="""\
+            This is the TED-LIUM corpus release 3, licensed under Creative Commons
+            BY-NC-ND 3.0. This is the 'speaker adaptation' version of the corpus, specially designed for experiments on
+            speaker adaptation.
 
-class Switchboard(datasets.GeneratorBasedBuilder):
-    """The Switchboard corpus is a collection of about 2,400 two-sided telephone conversations."""
+            All talks and text are property of TED Conferences LLC.
+
+            This new TED-LIUM release was made through a collaboration between the
+            Ubiqus company and the LIUM (University of Le Mans, France)
+            """,
+        citation="""\
+            @inproceedings{hernandez2018tedlium3,
+              title={TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation},
+              author={Hernandez, Fran{\\c{c}}ois and Nguyen, Vincent and Ghannay, Sahar and Tomashenko, Natalia and Est{\\`e}ve, Yannick},
+              booktitle={International Conference on Speech and Computer},
+              pages={198--208},
+              year={2018},
+              organization={Springer}
+            }
+            """,
+        url="https://www.openslr.org/51/",
+        download_urls={
+            "train": [
+                _DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "train_1.tar.gz"),
+                _DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "train_2.tar.gz"),
+            ],
+            "validation": [_DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "dev.tar.gz")],
+            "test": [_DL_URL + os.path.join("TEDLIUM_release3", "speaker-adaptation", "test.tar.gz")],
+        },
+        split_paths=[
+            (datasets.Split.TRAIN, "train"),
+            (datasets.Split.VALIDATION, "dev"),
+            (datasets.Split.TEST, "test"),
+        ],
+    )
+
+    return [release1, release2, release3, release3_speaker_adaptation]
+
+
+class TedLium(datasets.GeneratorBasedBuilder):
+    """The TED-LIUM corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech."""
 
     VERSION = datasets.Version("1.1.0")
 
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="switchboard", version=VERSION, description=_DESCRIPTION)]
+    BUILDER_CONFIGS = _make_builder_configs()
 
     def _info(self):
         features = datasets.Features(
             {
-                "audio": datasets.features.Audio(sampling_rate=8_000),
+                "audio": datasets.features.Audio(sampling_rate=16_000),
                 "text": datasets.Value("string"),
+                "speaker_id": datasets.Value("string"),
+                "gender": datasets.features.ClassLabel(names=["unknown", "female", "male"]),
                 "file": datasets.Value("string"),
                 "id": datasets.Value("string"),
             }
         )
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
+            description=self.config.description,
             features=features,
             supervised_keys=("audio", "text"),
-            homepage=_HOMEPAGE,
+            homepage=self.config.url,
             license=_LICENSE,
-            citation=_CITATION,
+            citation=self.config.citation,
             task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
         )
 
     def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_dir,  # later: change this to train/dev/test (e.g. os.path.join(data_dir, "train"))
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Generate examples from a Switchboard filepath."""
-        txt_files = list()
-        sph_filenames = list()
-        sph_files = list()
-
-        for (dirpath, dirnames, filenames) in os.walk(filepath):
-            txt_files += [os.path.join(dirpath, file) for file in filenames if file.endswith("-trans.text")]
-            sph_filenames += [file[3:7] for file in filenames if file.endswith(".sph")]
-            sph_files += [os.path.join(dirpath, file) for file in filenames if file.endswith(".sph")]
-
-        for file in txt_files:
-            # the .sph speaker file almost always has the same file name as the .stm file
-            speaker_file = Path(file).stem
-            idx = sph_filenames.index(speaker_file[2:6])
-            segment, sampling_rate = sf.read(sph_files[idx], dtype=np.int16)
-            with open(file) as f:
-                for line in f:
-                    line = line.strip()
-                    id, start, end, transcript = line.split(" ", 3)
-                    if speaker_file != id[2:6]:
-                        # handle the case where the stm file does not have the same file name as the transcript
-                        idx = sph_filenames.index(id[2:6])
-                        segment, sampling_rate = sf.read(sph_files[idx], dtype=np.int16)
-                    samples = _extract_audio_segment(segment, sampling_rate, float(start), float(end))
-                    key = "-".join([id, start, end])
-                    example = {
-                        "audio": {"path": file, "array": samples, "sampling_rate": sampling_rate},
-                        "text": transcript,
-                        "file": file,
-                        "id": key,
-                    }
-                    yield key, example
+        archive_path = dl_manager.download(self.config.download_urls)
+        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
+        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
+        splits = []
+        for split, path in self.config.split_paths:
+            kwargs = {
+                "filepath": [dl_manager.iter_archive(sharded_path) for sharded_path in archive_path[split]],
+                "local_extracted_archive": local_extracted_archive.get(split),
+                "split_path": path,
+            }
+            splits.append(datasets.SplitGenerator(name=split, gen_kwargs=kwargs))
+        return splits
+
+    def _generate_examples(self, filepath, local_extracted_archive, split_path):
+        """Generate examples from a TED-LIUM stm file."""
+        if local_extracted_archive:
+            for local_archive in local_extracted_archive:
+                # The stm directory houses the speaker and transcription information in .stm format
+                split_dir = os.path.join(local_archive, split_path)
+                stm_files = [os.path.join(split_dir, f) for f in os.listdir(split_dir) if f.endswith(".stm")]
+                for file in stm_files:
+                    # the .sph speaker file almost always has the same file name as the .stm file
+                    speaker_file = Path(file).stem
+                    audio_file = os.path.join(split_dir, speaker_file + ".sph")
+                    segment, sampling_rate = sf.read(audio_file, dtype=np.int16)
+                    with open(file) as f:
+                        for line in f:
+                            line = line.strip()
+                            fn, channel, speaker, start, end, label, transcript = line.split(" ", 6)
+                            transcript = _maybe_trim_suffix(transcript)
+                            if speaker_file != fn:
+                                # handle the case where the stm file does not have the same file name as the transcript
+                                speaker_file = fn
+                                audio_file = os.path.join(split_dir, speaker_file + ".sph")
+                                segment, sampling_rate = sf.read(audio_file, dtype=np.int16)
+                            samples = _extract_audio_segment(segment, sampling_rate, float(start), float(end))
+                            key = "-".join([speaker, start, end, label])
+                            example = {
+                                "audio": {"path": audio_file, "array": samples, "sampling_rate": sampling_rate},
+                                "text": transcript,
+                                "speaker_id": speaker,
+                                "gender": _parse_gender(label),
+                                "file": audio_file,
+                                "id": key,
+                            }
+                            yield key, example
+
+        else:
+            audio_data = {}
+            transcripts = defaultdict(list)
+            for file in filepath:
+                for path, f in file:
+                    if path.endswith(".sph"):
+                        # get the speaker id
+                        fn = path.split("/")[-1].strip(".sph")
+                        # read the audio data from raw byte form and add key-value pair to dict
+                        audio_data[fn] = sf.read(BytesIO(f.read()), dtype=np.int16)
+                    elif path.endswith(".stm"):
+                        for line in f:
+                            if line:
+                                line = line.decode("utf-8").strip()
+                                fn, channel, speaker, start, end, label, transcript = line.split(" ", 6)
+                                transcript = _maybe_trim_suffix(transcript)
+                                audio_file = path.replace("stm", "sph")
+                                key = "-".join([speaker, start, end, label])
+                                # append metadata information to the dict of transcripts for the associated speaker
+                                transcripts[fn].append(
+                                    {
+                                        "text": transcript,
+                                        "speaker_id": speaker,
+                                        "gender": _parse_gender(label),
+                                        "file": audio_file,
+                                        "id": key,
+                                        "start": start,
+                                        "end": end,
+                                        "channel": channel,
+                                        "fn": fn,
+                                    }
+                                )
+
+                    if audio_data and audio_data.keys() == transcripts.keys():
+                        for fn, speaker in transcripts.items():
+                            for transcript in speaker:
+                                segment, sampling_rate = audio_data[transcript["fn"]]
+                                samples = _extract_audio_segment(
+                                    segment,
+                                    int(transcript["channel"]),
+                                    float(transcript["start"]),
+                                    float(transcript["end"]),
+                                )
+                                audio = {"path": transcript["file"], "array": samples, "sampling_rate": sampling_rate}
+                                key = transcript["id"]
+                                yield key, {
+                                    "audio": audio,
+                                    "text": transcript["text"],
+                                    "speaker_id": transcript["speaker_id"],
+                                    "gender": transcript["gender"],
+                                    "file": transcript["file"],
+                                    "id": transcript["id"],
+                                }
+                        audio_data = {}
+                        transcripts = defaultdict(list)
+
+
+def _maybe_trim_suffix(transcript):
+    # stm files for the TEDLIUM release 1 train split contain a key (enclosed in
+    # parens) at the end.
+    splits = transcript.rsplit(" ", 1)
+    transcript = splits[0]
+    if len(splits) > 1:
+        suffix = splits[-1]
+        if not suffix.startswith("("):
+            transcript += " " + suffix
+    return transcript
 
 
 def _extract_audio_segment(segment, sampling_rate, start_sec, end_sec):
     """Extracts segment of audio samples (as an ndarray) from the given segment."""
+    # The dataset only contains mono audio.
     start_sample = int(start_sec * sampling_rate)
     end_sample = min(int(end_sec * sampling_rate), segment.shape[0])
     samples = segment[start_sample:end_sample]
     return samples
+
+
+def _parse_gender(label_str):
+    """Parse gender string from STM "<label>" field."""
+    gender = re.split(",|_", label_str)[-1][:-1]
+    # Fix inconsistencies in the data.
+    if not gender:
+        gender = -1  # Missing label.
+    elif gender == "<NA":  # In TEDLIUM release 3 training data.
+        gender = -1  # Missing label.
+    elif gender == "F":
+        gender = "female"
+    elif gender == "M":
+        gender = "male"
+    return gender