Minor change on script

alffamharic_asr.py

@@ -31,7 +31,7 @@ _CITATION = """\
   ldc_catalog_no={LDC93S1},
   DOI={https://doi.org/10.35111/17gk-bn40},
   journal={Linguistic Data Consortium, Philadelphia},
-  year={2023}
+  year={1983}
 }
 """
 
@@ -39,8 +39,8 @@ _DESCRIPTION = """\
 The ALFFAAmharic corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
 and for the evaluation of automatic speech recognition systems.
 
-ALFFAAmharic contains high quality recordings of 630 individuals/speakers with 8 different American English dialects,
-with each individual reading upto 10 phonetically rich sentences.
+ALFFAAmharic contains high-quality recordings of 630 individuals/speakers with 8 different American English dialects,
+with each individual reading up to 10 phonetically rich sentences.
 
 More info on ALFFAAmharic dataset can be understood from the "README" which can be found here:
 https://catalog.ldc.upenn.edu/docs/LDC93S1/readme.txt
@@ -65,17 +65,17 @@ class ALFFAAmharicASRConfig(datasets.BuilderConfig):
 
 
 class ALFFAAmharic(datasets.GeneratorBasedBuilder):
-    """TimitASR dataset."""
+    """ALFFAAmharicASR dataset."""
 
-    BUILDER_CONFIGS = [TimitASRConfig(name="clean", description="'Clean' speech.")]
+    BUILDER_CONFIGS = [ALFFAAmharicASRConfig(name="clean", description="'Clean' speech.")]
 
     @property
     def manual_download_instructions(self):
         return (
-            "To use TIMIT you have to download it manually. "
+            "To use ALFFAAmharic you have to download it manually. "
             "Please create an account and download the dataset from https://catalog.ldc.upenn.edu/LDC93S1 \n"
             "Then extract all files in one folder and load the dataset with: "
-            "`datasets.load_dataset('timit_asr', data_dir='path/to/folder/folder_name')`"
+            "`datasets.load_dataset('ALFFAAmharic_asr', data_dir='path/to/folder/folder_name')`"
         )
 
     def _info(self):
@@ -86,7 +86,6 @@ class ALFFAAmharic(datasets.GeneratorBasedBuilder):
                     "file": datasets.Value("string"),
                     "audio": datasets.Audio(sampling_rate=16_000),
                     "text": datasets.Value("string"),
-                    "speaker_id": datasets.Value("string"),
                     "id": datasets.Value("string"),
                 }
             ),
@@ -102,7 +101,7 @@ class ALFFAAmharic(datasets.GeneratorBasedBuilder):
 
         if not os.path.exists(data_dir):
             raise FileNotFoundError(
-                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('timit_asr', data_dir=...)` that includes files unzipped from the TIMIT zip. Manual download instructions: {self.manual_download_instructions}"
+                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('ALFFAAmharic_asr', data_dir=...)` that includes files unzipped from the ALFFAAmharic zip. Manual download instructions: {self.manual_download_instructions}"
             )
 
         return [
@@ -111,33 +110,28 @@ class ALFFAAmharic(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, split, data_dir):
-        """Generate examples from TIMIT archive_path based on the test/train csv information."""
-        
-        
-        
+        """Generate examples from ALFFAAmharic archive_path based on the test/train csv information."""
+        file = open(f"{data_dir}{split}/text.txt", "r", encoding="utf-8")
+        file.close()
+        lines = file.readlines()
         # Iterating the contents of the data to extract the relevant information
-        wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav"))
-        wav_paths = wav_paths if wav_paths else sorted(Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV"))
-        for key, wav_path in enumerate(wav_paths):
-
-            # extract transcript
-            txt_path = with_case_insensitive_suffix(wav_path, ".txt")
-            with txt_path.open(encoding="utf-8") as op:
-                transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number
-
-            speaker_id = wav_path.parents[0].name[1:]
+        
+        for i in range(len(lines)):
+            splited = lines[i].split(" ")
+            if len(splited)==0:
+                continue
+            wav_path = f"{data_dir}{split}/wav/{splited[0]}.wav"
+            transcript = " ".join(splited[0:])
+            
             id_ = wav_path.stem
-
-            example = {
+            
+            yield i, {
                 "file": str(wav_path),
                 "audio": str(wav_path),
                 "text": transcript,
-                "speaker_id": speaker_id,
                 "id": id_,
             }
 
-            yield key, example
-
 
 def with_case_insensitive_suffix(path: Path, suffix: str):
     path = path.with_suffix(suffix.lower())