update

README.md

@@ -46,4 +46,37 @@ dataset = load_dataset(
     split="train",
     trust_remote_code=True
 )
-```
+```
+
+## Overview
+
+|    | Language                 | alpha3     |   train |   test |   dev |   total |
+|---:|:-------------------------|:-----------|--------:|-------:|------:|--------:|
+|  0 | Vietnamese               | vie        |     856 |    111 |   106 |    1073 |
+|  1 | French                   | fra        |     851 |    108 |   106 |    1065 |
+|  2 | Russian                  | rus        |     822 |    107 |   102 |    1031 |
+|  3 | Ukrainian                | ukr        |     751 |     97 |    89 |     937 |
+|  4 | Kannada                  | kan        |     740 |    100 |    89 |     929 |
+|  5 | Gujarati                 | guj        |     740 |    100 |    89 |     929 |
+|  6 | Yoruba                   | yor        |     739 |    100 |    88 |     927 |
+|  7 | Punjabi                  | pan        |     739 |    100 |    88 |     927 |
+|  8 | Naga Pidgin              | nag        |     739 |    100 |    89 |     928 |
+|  9 | Luo (Kenya and Tanzania) | luo        |     738 |    100 |    88 |     926 |
+| 10 | Tamil                    | tam        |     733 |    100 |    89 |     922 |
+| 11 | Marathi                  | mar        |     733 |     99 |    87 |     919 |
+| 12 | Assamese                 | asm        |     732 |     98 |    88 |     918 |
+| 13 | Haryanvi                 | bgc        |     729 |    100 |    87 |     916 |
+| 14 | Bhattiyali               | bht        |     726 |     98 |    88 |     912 |
+| 15 | Malayalam                | mal        |     724 |    100 |    89 |     913 |
+| 16 | Ewe                      | ewe        |     724 |     98 |    86 |     908 |
+| 17 | Central Kurdish          | ckb        |     723 |     93 |    82 |     898 |
+| 18 | Telugu                   | tel        |     722 |     96 |    85 |     903 |
+| 19 | Igbo                     | ibo        |     720 |     96 |    87 |     903 |
+| 20 | Pengo                    | peg        |     707 |     94 |    86 |     887 |
+| 21 | Ndebele                  | nde        |     699 |     88 |    85 |     872 |
+| 22 | Asante Twi               | tw-asante  |     693 |     92 |    88 |     873 |
+| 23 | Akuapem Twi              | tw-akuapem |     692 |     91 |    84 |     867 |
+| 24 | Urdu                     | urd        |     674 |     95 |    80 |     849 |
+| 25 | Nahali                   | nlx        |     672 |     92 |    85 |     849 |
+| 26 | English                  | eng        |     569 |     81 |    74 |     724 |
+| 27 | Lingala                  | lin        |     560 |     75 |    61 |     696 |

create_release.py

@@ -9,6 +9,8 @@ import jinja2
 import pandas as pd
 import uroman as ur
 from unidecode import unidecode
+from lnn.utils import load_audio
+from tqdm.auto import tqdm
 
 # Define some variables
 NAME = "SpeechTaxi"
@@ -277,6 +279,19 @@ def read_bible_scrape_instance(audio_file: str | Path) -> dict:
     return data
 
 
+def is_audio_valid(audio_path: str | Path):
+    audio_path = Path(audio_path)
+    if not audio_path.exists():
+        return False
+    try:
+        wv, sr = load_audio(audio_path, return_tensor="torch")
+        wv = wv[0].reshape(-1)
+    except Exception:
+        return False
+    # We need *at least* one second of audio
+    return wv.numel() >= sr
+
+
 # Main logic
 if __name__ == "__main__":
     dataset_root = SAVE_DIR / NAME
@@ -291,7 +306,7 @@ if __name__ == "__main__":
     data_dir.mkdir(parents=True)
     table_dir = dataset_root / "tables"
     table_dir.mkdir(parents=True)
-    
+
     # Copy this script to dataset dir for reproducibility
     copy_self(dataset_root)
 
@@ -303,7 +318,9 @@ if __name__ == "__main__":
 
     # Write code file
     success_languages = []
-    for alignment_table in language_tables:
+    pbar = tqdm(language_tables, desc="Creating dataset...")
+    invalid_counter = 0
+    for alignment_table in pbar:
         df = pd.read_table(alignment_table)
         try:
             df = filter_instances(df, alignment_table)
@@ -317,10 +334,19 @@ if __name__ == "__main__":
         for _, row in df.iterrows():
             if alignment_table.stem not in MASS_LANGUAGES:
                 audio_file = rewrite_path(Path(row["audio_path"]))
+                if not is_audio_valid(audio_file):
+                    invalid_counter += 1
+                    continue
                 copied_audio_file = copy_bible_scrape_audio(audio_file, root=data_dir)
                 english_text = row["en_text"]
-                data = read_bible_scrape_instance(audio_file)
-                # copied_audio = copy_file(audio_file, data_dir)
+                try:
+                    data = read_bible_scrape_instance(audio_file)
+                except Exception as e:
+                    print("Error reading file", audio_file)
+                    print(e)
+                    print("_"*30)
+                    continue
+                # Files that can't be read have zero alignment scores anyways so in most cases this isn't an issue
                 data = (
                     {"verse_ref": data.pop("verse_ref")}
                     | {"text_en": english_text}
@@ -331,8 +357,18 @@ if __name__ == "__main__":
                 final_table_data.append(data)
             elif INCLUDE_MASS_LANGUAGES:
                 audio_file = rewrite_mass_path(Path(row["audio_path"]))
+                if not is_audio_valid(audio_file):
+                    invalid_counter += 1
+                    continue
                 copied_audio_file = copy_mass_audio(audio_file, root=data_dir)
-                data = read_mass_instance(audio_file)
+                try:
+                    data = read_mass_instance(audio_file)
+                except Exception as e:
+                    print("Error reading file", audio_file)
+                    print(e)
+                    print("_"*30)
+                    continue
+                # Files that can't be read have zero alignment scores anyways so in most cases this isn't an issue
                 data = data | {
                     "split": row["split"],
                     "label": row["label"],
@@ -340,22 +376,10 @@ if __name__ == "__main__":
                     "audio": copied_audio_file.relative_to(data_dir).as_posix(),
                 }
                 final_table_data.append(data)
-        fina_table = pd.DataFrame.from_records(final_table_data)
-        # Reorder columns
-        # cols = [
-        #     "verse_ref",
-        #     "text_en",
-        #     "split",
-        #     "label",
-        #     "transcription",
-        #     "transcription_romanized",
-        #     "transription_mms-zeroshot-300m",
-        #     "transription_whisper-large-v3",
-        #     "transription_mms-1b-all",
-        #     "audio",
-        # ]
-        # fina_table = fina_table[cols]
-        fina_table.to_csv(table_dir / alignment_table.name, index=False, sep="\t")
+            pbar.set_description(f"{str(alignment_table)} | Total invalid: {invalid_counter}")
+        
+        final_table = pd.DataFrame.from_records(final_table_data)
+        final_table.to_csv(table_dir / alignment_table.name, index=False, sep="\t")
 
     # Now render dataset loading script and write to dataset dir
     code_file = dataset_root / f"{NAME}.py"

data.zip

@@ -1,3 +1,3 @@
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+oid sha256:e0199424abee149174d9ea5eb0a12b4828ff21d1ffd6670cbd4f2244e43e305e
+size 2905453052

tables.zip

@@ -1,3 +1,3 @@
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-size 7457350
+oid sha256:6b4ac7fc8d111d463295402b1469117ef74ba5bdfa7ddc5b9a2fae3fef2f4306
+size 7586373