added data collator

README.md

@@ -20,4 +20,84 @@ extra_gated_prompt: "When using this dataset to download LibriTTS, you agree to
 
 This dataset downloads LibriTTS and preprocesses it on your machine to create alignments using [montreal forced aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/).
 You need to run ``pip install alignments phones`` before using this dataset.
-When running this the first time, it can take an hour or two, but subsequent runs will be lightning fast.
+When running this the first time, it can take an hour or two, but subsequent runs will be lightning fast.
+
+## Splits
+
+This dataset has the following splits:
+- ``train``: All the training data, except one sample per speaker which is used for validation.
+- ``dev``: The validation data, one sample per speaker.
+- ``train.clean.100``: Training set derived from the original materials of the train-clean-100 subset of LibriSpeech.
+- ``train.clean.360``: Training set derived from the original materials of the train-clean-360 subset of LibriSpeech.
+- ``train.other.500``: Training set derived from the original materials of the train-other-500 subset of LibriSpeech.
+- ``dev.clean``: Validation set derived from the original materials of the dev-clean subset of LibriSpeech.
+- ``dev.other``: Validation set derived from the original materials of the dev-other subset of LibriSpeech.
+- ``test.clean``: Test set derived from the original materials of the test-clean subset of LibriSpeech.
+- ``test.other``: Test set derived from the original materials of the test-other subset of LibriSpeech.
+
+## Example Item
+
+```json
+{
+    'id': '100_122655_000073_000002.wav',
+    'speaker': '100',
+    'text': 'the day after, diana and mary quitted it for distant b.',
+    'start': 0.0,
+    'end': 3.6500000953674316, 
+    'phones': ['[SILENCE]', 'ð', 'ʌ', '[SILENCE]', 'd', 'eɪ', '[SILENCE]', 'æ', 'f', 't', 'ɜ˞', '[COMMA]', 'd', 'aɪ', 'æ', 'n', 'ʌ', '[SILENCE]', 'æ', 'n', 'd', '[SILENCE]', 'm', 'ɛ', 'ɹ', 'i', '[SILENCE]', 'k', 'w', 'ɪ', 't', 'ɪ', 'd', '[SILENCE]', 'ɪ', 't', '[SILENCE]', 'f', 'ɜ˞', '[SILENCE]', 'd', 'ɪ', 's', 't', 'ʌ', 'n', 't', '[SILENCE]', 'b', 'i', '[FULL STOP]'], 'phone_durations': [5, 2, 4, 0, 5, 13, 0, 16, 7, 5, 20, 2, 6, 9, 15, 4, 2, 0, 11, 3, 5, 0, 3, 8, 9, 8, 0, 13, 3, 5, 3, 6, 4, 0, 8, 5, 0, 9, 5, 0, 7, 5, 6, 7, 4, 5, 10, 0, 3, 35, 9],
+    'audio': '/dev/shm/metts/train-clean-360-alignments/100/100_122655_000073_000002.wav'
+}
+```
+
+The phones are IPA phones, and the phone durations are in frames (assuming a hop length of 256, sample rate of 22050 and window length of 1024). These attributes can be changed using the ``hop_length``, ``sample_rate`` and ``window_length`` arguments to ``LibriTTSAlign``.
+
+## Environment Variables
+
+There are a few environment variable which can be set.
+
+- ``LIBRITTS_VERBOSE``: If set, will print out more information about the dataset creation process.
+- ``LIBRITTS_MAX_WORKERS``: The number of workers to use when creating the alignments. Defaults to ``cpu_count()``.
+- ``LIBRITTS_PATH``: The path to download LibriTTS to. Defaults to the value of ``HF_DATASETS_CACHE``.
+
+## Data Collator
+
+This dataset comes with a data collator which can be used to create batches of data for training.
+It can be installed using ``pip install speech-collator`` ([MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator)) and can be used as follows:
+
+```python
+import json
+from datasets import load_dataset
+from speech_collator import SpeechCollator
+from torch.utils.data import DataLoader
+
+dataset = load_dataset('libritts_align', split="train")
+
+speaker2ixd = json.load(open("speaker2ixd.json"))
+phone2ixd = json.load(open("phone2ixd.json"))
+
+collator = SpeechCollator(
+    speaker2ixd=speaker2ixd,
+    phone2ixd=phone2ixd,
+)
+dataloader = DataLoader(dataset, collate_fn=collator.collate_fn, batch_size=8)
+```
+
+You can either download the ``speaker2ixd.json`` and ``phone2ixd.json`` files from [here](https://huggingface.co/datasets/cdminix/libritts-aligned/tree/main/data) or create them yourself using the following code:
+
+```python
+import json
+from datasets import load_dataset
+from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx
+
+dataset = load_dataset("cdminix/libritts-aligned", split="train")
+
+# Create speaker2idx and phone2idx
+speaker2idx = create_speaker2idx(dataset, unk_idx=0)
+phone2idx = create_phone2idx(dataset, unk_idx=0)
+
+# save to json
+with open("speaker2idx.json", "w") as f:
+    json.dump(speaker2idx, f)
+with open("phone2idx.json", "w") as f:
+    json.dump(phone2idx, f)
+```

data/phone2idx.json

@@ -0,0 +1 @@
+{"<unk>": 0, "[SILENCE]": 6, "e\u026a": 12, "v": 27, "\u025b": 36, "\u0279": 40, "i": 15, "\u026a": 39, "m": 19, "p": 22, "\u0254": 34, "t": 24, "\u028c": 43, "n": 20, "\u0254\u026a": 35, "[COMMA]": 1, "\u00e6": 30, "d": 10, "z": 29, "b": 9, "l": 18, "k": 17, "u": 26, "\u0292": 44, "a\u026a": 7, "f": 13, "s": 23, "w": 28, "[FULL STOP]": 3, "j": 16, "o\u028a": 21, "\u0261": 38, "\u00f0": 31, "\u0283": 41, "\u014b": 32, "\u025c\u02de": 37, "h": 14, "\u0251": 33, "\u02cc\u026a": 57, "\u03b8": 60, "d\u0292": 11, "t\u0283": 25, "\u02cci": 48, "\u02cco\u028a": 49, "a\u028a": 8, "\u02cc\u025c\u02de": 56, "\u02cc\u028a": 58, "\u028a": 42, "[EXCLAMATION MARK]": 2, "\u02ccu": 50, "\u02cca\u026a": 45, "\u02cc\u025b": 55, "[QUESTION MARK]": 4, "\u02cc\u028c": 59, "[SEMICOLON]": 5, "\u02cc\u0254": 53, "\u02cce\u026a": 47, "\u02cc\u00e6": 51, "\u02cca\u028a": 46, "\u02cc\u0251": 52, "\u02cc\u0254\u026a": 54}

data/speaker2idx.json

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1860, "49": 24, "8250": 2292, "7654": 2122, "8302": 2306, "5567": 1545, "2990": 916, "5910": 1627, "2100": 717, "2042": 696, "3334": 1011, "1765": 616, "8466": 2351, "3871": 1138, "5285": 1489, "3990": 1173, "3125": 958, "8587": 2376, "4965": 1405, "6951": 1921, "742": 302, "1704": 592, "6726": 1861, "7433": 2063, "8112": 2250, "5791": 1601, "4931": 1396, "4042": 1190, "5005": 1416, "3097": 949, "727": 297, "4193": 1226, "228": 118, "6221": 1717, "7255": 2011, "3100": 951, "3793": 1118, "104": 59, "6568": 1823, "1492": 520, "3606": 1076, "2407": 794, "3567": 1065, "3079": 941, "3779": 1112, "7073": 1951, "4738": 1352, "366": 178, "1938": 666, "1681": 581, "8164": 2263, "876": 338, "85": 46, "4841": 1378, "37": 16, "3867": 1136, "3400": 1026, "8262": 2295, "4993": 1413, "4712": 1347, "1291": 460, "2606": 843, "7552": 2097, "2374": 784, "29": 11, "331": 165, "402": 187, "1374": 487, "3909": 1148, "45": 21, "5439": 1521, "4273": 1250, "6687": 1849, "7644": 2119, "6962": 1924, "2504": 815, "1373": 486, "5781": 1598, "1767": 617, "6199": 1712, "2544": 826, "7988": 2215, "6794": 1881, "7147": 1978, "1166": 419, "1559": 536, "474": 213, "1870": 650, "3132": 959, "6875": 1896, "6923": 1910, "3138": 962, "2522": 821, "6076": 1669, "3488": 1042, "1250": 441, "167": 87, "1544": 530, "4520": 1313, "8240": 2288, "6254": 1728, "3290": 1002, "6227": 1719, "8414": 2331, "5952": 1638, "5350": 1506, "2998": 919, "985": 372, "1563": 537, "8808": 2421, "8321": 2310, "3271": 996, "2526": 822, "2346": 778, "3143": 964, "8005": 2220, "5321": 1497, "265": 141, "4420": 1285, "3656": 1087, "4750": 1357, "1804": 629, "5424": 1519, "1225": 434, "5299": 1494, "3285": 999, "851": 336, "517": 230, "1347": 477, "923": 352, "7491": 2078, "7898": 2190, "811": 322, "8367": 2320, "7026": 1940, "4156": 1217, "4174": 1221, "7338": 2040, "7333": 2037, "1924": 662, "1341": 474, "5506": 1535, "8291": 2301, "7786": 2159, "1621": 555, "3144": 965, "4423": 1287, "4211": 1231, "4948": 1399, "8447": 2344, "3356": 1014, "5132": 1448, "4591": 1325, "5979": 1643, "7946": 2204, "5244": 1478, "25": 7, "8197": 2276, "5545": 1542, "2985": 913, "2301": 772, "1756": 613, "4059": 1195, "264": 140, "7131": 1970, "1184": 425, "4699": 1341, "2671": 853, "3647": 1084, "5199": 1469, "1985": 676, "6947": 1919, "5271": 1486, "6436": 1782, "2026": 693, "6758": 1873, "3747": 1108, "2541": 825, "3675": 1093, "6482": 1791, "448": 203, "319": 161, "6399": 1773, "6242": 1724, "6807": 1884, "5765": 1593, "8632": 2387, "133": 76, "2762": 872, "5796": 1602, "1977": 675, "8040": 2231, "5977": 1642, "336": 168, "4015": 1182, "8392": 2324, "3796": 1119, "2288": 765, "6535": 1810, "7679": 2126, "7197": 1990, "679": 277, "6368": 1763, "20": 5, "1726": 600, "6668": 1841, "7756": 2151, "6660": 1840, "3238": 986, "3848": 1129, "7218": 1996, "1664": 573, "5278": 1487, "3595": 1074, "3798": 1120, "2208": 744, "6548": 1815, "7408": 2059, "1124": 412, "4791": 1365, "3288": 1000, "3926": 1156, "6051": 1661, "392": 185, "7199": 1992, "2148": 729, "6391": 1771, "3470": 1037, "5825": 1607, "3757": 1110, "3021": 929, "6533": 1808, "1051": 385, "44": 20, "7510": 2085, "7307": 2026, "6804": 1883, "7360": 2047, "147": 77, "5141": 1452, "7522": 2093, "2735": 865, "8867": 2430, "8199": 2277, "3244": 989, "6705": 1856, "6784": 1878, "2351": 779, "6025": 1655, "102": 57, "4667": 1336, "921": 350, "7708": 2137, "2309": 773, "969": 366, "6184": 1708, "4958": 1402, "681": 278, "2733": 864, "6733": 1863, "7512": 2087, "5784": 1599, "2279": 762, "4659": 1334, "3227": 982, "3598": 1075, "6035": 1658, "8356": 2319, "7502": 2082, "6224": 1718, "6512": 1802, "5459": 1526, "1710": 595, "6111": 1684, "4020": 1186, "8273": 2298, "8023": 2228, "6072": 1667, "1813": 633, "8897": 2434, "1258": 444, "613": 262, "432": 199, "5082": 1438, "6249": 1726, "542": 233, "5287": 1490, "2445": 803, "7392": 2055, "7480": 2075, "2730": 863, "52": 26, "7299": 2023, "7618": 2115, "2292": 767, "5641": 1559, "8322": 2311, "6747": 1868, "5252": 1482, "7238": 2002, "2946": 905, "3962": 1164, "4104": 1204, "6323": 1746, "4017": 1183, "65": 35, "895": 343, "3172": 971, "7250": 2010, "8675": 2394, "2748": 868, "77": 39, "4701": 1342, "4447": 1297, "4205": 1230, "8015": 2227, "7387": 2052, "5181": 1461, "2740": 866, "1920": 660, "6333": 1750, "6506": 1799, "1049": 383, "6087": 1676, "7649": 2121, "7138": 1973, "6735": 1864, "6954": 1922, "3020": 928, "3657": 1088, "6077": 1670, "8710": 2401, "3135": 960, "1261": 447, "6709": 1858, "5700": 1575, "317": 159, "6370": 1764, "5890": 1621, "4085": 1200, "7223": 1998, "57": 30, "2356": 780, "8044": 2233, "466": 210, "779": 310, "2975": 911, "2496": 812, "7823": 2168, "5340": 1505, "7507": 2084, "6614": 1832, "8543": 2367, "238": 123, "2902": 895, "3536": 1051, "1462": 511, "3081": 943, "251": 132, "2086": 711, "652": 270, "6241": 1723, "6313": 1743, "3000": 921, "3853": 1132, "5895": 1624, "6295": 1739, "84": 45, "5338": 1503, "3170": 969, "7976": 2212, "1673": 576, "1919": 659, 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libritts-aligned.py

@@ -20,17 +20,13 @@ logger = datasets.logging.get_logger(__name__)
 
 _PHONESET = "arpabet"
 
-_VERBOSE = os.environ.get("METTS_VERBOSE", True)
-
-_MAX_WORKERS = os.environ.get("METTS_MAX_WORKERS", cpu_count())
-
-_VERSION = "1.0.0"
-
-_PATH = os.environ.get("METTS_PATH", os.environ.get("HF_DATASETS_CACHE", None))
+_VERBOSE = os.environ.get("LIBRITTS_VERBOSE", True)
+_MAX_WORKERS = os.environ.get("LIBRITTS_MAX_WORKERS", cpu_count())
+_PATH = os.environ.get("LIBRITTS_PATH", os.environ.get("HF_DATASETS_CACHE", None))
 if _PATH is not None and not os.path.exists(_PATH):
     os.makedirs(_PATH)
 
-_NO_MEASURES = os.environ.get("METTS_NO_MEASURES", False)
+_VERSION = "1.0.1"
 
 _CITATION = """\
 @article{zen2019libritts,
@@ -62,31 +58,31 @@ _URLS = {
 }
 
 
-class MeTTSConfig(datasets.BuilderConfig):
-    """BuilderConfig for MeTTS."""
+class LibriTTSAlignConfig(datasets.BuilderConfig):
+    """BuilderConfig for LibriTTSAlign."""
 
     def __init__(self, sampling_rate=22050, hop_length=256, win_length=1024, **kwargs):
-        """BuilderConfig for MeTTS.
+        """BuilderConfig for LibriTTSAlign.
 
         Args:
           **kwargs: keyword arguments forwarded to super.
         """
-        super(MeTTSConfig, self).__init__(**kwargs)
+        super(LibriTTSAlignConfig, self).__init__(**kwargs)
 
         self.sampling_rate = sampling_rate
         self.hop_length = hop_length
         self.win_length = win_length
         
         if _PATH is None:
-            raise ValueError("Please set the environment variable METTS_PATH to point to the MeTTS dataset directory.")
+            raise ValueError("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory.")
         elif _PATH == os.environ.get("HF_DATASETS_CACHE", None):
-            logger.warning("Please set the environment variable METTS_PATH to point to the MeTTS dataset directory. Using HF_DATASETS_CACHE as a fallback.")
+            logger.warning("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory. Using HF_DATASETS_CACHE as a fallback.")
 
-class MeTTS(datasets.GeneratorBasedBuilder):
-    """MeTTS dataset."""
+class LibriTTSAlign(datasets.GeneratorBasedBuilder):
+    """LibriTTSAlign dataset."""
 
     BUILDER_CONFIGS = [
-        MeTTSConfig(
+        LibriTTSAlignConfig(
             name="libritts",
             version=datasets.Version(_VERSION, ""),
         ),
@@ -103,14 +99,14 @@ class MeTTS(datasets.GeneratorBasedBuilder):
             "phones": datasets.Sequence(datasets.Value("string")),
             "phone_durations": datasets.Sequence(datasets.Value("int32")),
             # audio feature
-            "audio": datasets.Value("string") # datasets.Audio(sampling_rate=self.config.sampling_rate),
+            "audio": datasets.Value("string")
         }
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(features),
             supervised_keys=None,
-            homepage="https://github.com/MiniXC/MeTTS",
+            homepage="https://github.com/MiniXC/MeasureCollator",
             citation=_CITATION,
             task_templates=None,
         )