Create libritts-phones-and-mel.py

libritts-phones-and-mel.py

@@ -0,0 +1,159 @@
+"""LibriSpeech dataset with phone alignments, prosody and mel spectrograms."""
+
+import os
+from pathlib import Path
+import hashlib
+import pickle
+
+import datasets
+import pandas as pd
+import numpy as np
+from tqdm.contrib.concurrent import process_map
+from tqdm.auto import tqdm
+from multiprocessing import cpu_count
+from PIL import Image
+
+logger = datasets.logging.get_logger(__name__)
+
+_VERSION = "0.0.1"
+
+_CITATION = """\
+@inproceedings{48008,
+title	= {LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
+author	= {Heiga Zen and Rob Clark and Ron J. Weiss and Viet Dang and Ye Jia and Yonghui Wu and Yu Zhang and Zhifeng Chen},
+year	= {2019},
+URL	= {https://arxiv.org/abs/1904.02882},
+booktitle	= {Interspeech}
+}
+"""
+
+_DESCRIPTION = """\
+Dataset containing Mel Spectrograms, Prosody and Phone Alignments for the LibriTTS dataset.
+"""
+
+_URLS = {
+    "dev.clean": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/dev_clean.tar.gz",
+    "dev.other": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/dev_other.tar.gz",
+    "test.clean": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/test_clean.tar.gz",
+    "test.other": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/test_other.tar.gz",
+    "train.clean.100": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_clean_100.tar.gz",
+    "train.clean.360": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_clean_360.tar.gz",
+    "train.other.500": "https://huggingface.co/datasets/cdminix/libritts-phones-and-mel/resolve/main/data/train_other_500.tar.gz",
+}
+
+
+class LibriTTSConfig(datasets.BuilderConfig):
+
+    def __init__(self, **kwargs):
+        """
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(LibrispeechConfig, self).__init__(**kwargs)
+
+
+class LibriTTS(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        LibriTTSConfig(
+            name="libritts",
+            version=datasets.Version(_VERSION, ""),
+        ),
+    ]
+
+    def _info(self):
+        features = {
+            "id": datasets.Value("string"),
+            "speaker_id": datasets.Value("string"),
+            "chapter_id": datasets.Value("string"),
+            "phones": datasets.Value("string"),
+            "mel": datasets.Value("string"),
+            "prosody": datasets.Value("string"),
+            "speaker_utterance": datasets.Value("string"),
+            "mean_speaker_utterance": datasets.Value("string"),
+            "mean_speaker": datasets.Value("string"),
+            "text": datasets.Value("string"),
+        }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/cdminix/libritts-phones-and-mel",
+            citation=_CITATION,
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        splits = [
+            datasets.SplitGenerator(
+                name=key,
+                gen_kwargs={"data_path": dl_manager.download_and_extract(value)},
+            )
+            for key, value in _URLS.items()
+        ]
+
+        return splits
+
+    def _df_from_path(self, path):
+        mel_path = Path(path)
+        prosody_path = Path(str(mel_path).replace("_mel.png", "_prosody.png"))
+        phones_path = Path(str(mel_path).replace("_mel.png", "_phones.npy"))
+        overall_speaker_path = Path(str(mel_path).replace("_mel.png", "_speaker.npy"))
+        temporal_speaker_path = Path(str(mel_path).replace("_mel.png", "_speaker.png"))
+        mean_speaker_path = mel_path.parent.parent / "mean_speaker.npy"
+        text = Path(str(mel_path).replace("_mel.png", "_text.txt")).read_text().lower()
+
+        speaker_id = mel_path.parent.parent.name
+        chapter_id = mel_path.parent.name
+        _id = str(mel_path).replace("_mel.png", "")
+
+        return {
+            "id": _id,
+            "speaker_id": speaker_id,
+            "chapter_id": chapter_id,
+            "phones": phones_path,
+            "mel": mel_path,
+            "prosody": prosody_path,
+            "speaker_utterance": temporal_speaker_path,
+            "mean_speaker_utterance": overall_speaker_path,
+            "mean_speaker": mean_speaker_path,
+            "text": text,
+        }
+
+    def _df_from_paths_mp(self, paths):
+        return pd.DataFrame(
+            process_map(
+                self._df_from_path,
+                paths,
+                desc="Reading files",
+                max_workers=cpu_count(),
+                chunksize=100,
+            )
+        )
+
+    def _create_mean_speaker(self, df):
+        # Create mean speaker for each speaker
+        for _, speaker_df in df.groupby("speaker_id"):
+            if not Path(speaker_df["mean_speaker"].iloc[0]).exists():
+                mean_speaker = np.mean(
+                    np.array(
+                        [np.load(path) for path in speaker_df["mean_speaker_utterance"]]
+                    ),
+                    axis=0,
+                )
+                np.save(speaker_df["mean_speaker"].iloc[0], mean_speaker)
+
+    def _generate_examples(self, data_path):
+        """Generate examples."""
+        logger.info("⏳ Generating examples from = %s", data_path)
+
+        paths = sorted(list(Path(data_path).rglob("*_mel.png")))
+
+        df = self._df_from_paths_mp(paths)
+
+        self._create_mean_speaker(df)
+
+        for i, row in tqdm(df.iterrows(), desc="Generating examples"):
+            yield row["id"], row.to_dict()