Remove ClassLabel encoding (to make the arrow dataset compatible with raw JSON).

This dataset was originally design for SpeechBrain, which has its own encoders
in the pipeline

librig2p-nostress-space.py

@@ -13,50 +13,8 @@ Grapheme-to-Phoneme training, validation and test sets
 
 _BASE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress-space/resolve/main/dataset"
 
-_HOMEPAGE_URL = "https://github.com/flexthink/librig2p-nostress-space/tree/develop"
+_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress-space"
 
-_PHONEMES = [
-    "AA",
-    "AE",
-    "AH",
-    "AO",
-    "AW",
-    "AY",
-    "B",
-    "CH",
-    "D",
-    "DH",
-    "EH",
-    "ER",
-    "EY",
-    "F",
-    "G",
-    "HH",
-    "IH",
-    "IY",
-    "JH",
-    "K",
-    "L",
-    "M",
-    "N",
-    "NG",
-    "OW",
-    "OY",
-    "P",
-    "R",
-    "S",
-    "SH",
-    "T",
-    "TH",
-    "UH",
-    "UW",
-    "V",
-    "W",
-    "Y",
-    "Z",
-    "ZH",
-    " "
-]
 _ORIGINS = ["librispeech", "librispeech-lex", "wikipedia-homograph"]
 _NA = "N/A"
 _SPLIT_TYPES = ["train", "valid", "test"]
@@ -79,9 +37,9 @@ class GraphemeToPhoneme(datasets.GeneratorBasedBuilder):
                 {
                     "id": datasets.Value("string"),
                     "speaker_id": datasets.Value("string"),
-                    "origin": datasets.ClassLabel(names=_ORIGINS),
+                    "origin": datasets.Value("string"),
                     "char": datasets.Value("string"),
-                    "phn": datasets.Sequence(datasets.ClassLabel(names=_PHONEMES)),
+                    "phn": datasets.Sequence(datasets.Value("string")),
                     "homograph": datasets.Value("string"),
                     "homograph_wordid": datasets.Value("string"),
                     "homograph_char_start": datasets.Value("int32"),
@@ -114,7 +72,7 @@ class GraphemeToPhoneme(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, datapath, datatype):
         with open(datapath, encoding="utf-8") as f:
             data = json.load(f)
-
+        breakpoint()
         for sentence_counter, (item_id, item) in enumerate(data.items()):
             resp = {
                 "id": item_id,