Update audio_data.py

audio_data.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 
 # Lint as: python3
-"""TIMIT automatic speech recognition dataset."""
+"""AudioData dataset."""
 
 
 import os
@@ -26,54 +26,48 @@ from datasets.tasks import AutomaticSpeechRecognition
 
 _CITATION = """\
 @inproceedings{
-  title={TIMIT Acoustic-Phonetic Continuous Speech Corpus},
-  author={Garofolo, John S., et al},
-  ldc_catalog_no={LDC93S1},
-  DOI={https://doi.org/10.35111/17gk-bn40},
-  journal={Linguistic Data Consortium, Philadelphia},
-  year={1983}
+  title={AudioData Speech Corpus},
+  author={Your Name},
+  year={Year}
 }
 """
 
 _DESCRIPTION = """\
-The TIMIT corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
+The AudioData 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.
-TIMIT contains high quality recordings of 630 individuals/speakers with 8 different American English dialects,
-with each individual reading upto 10 phonetically rich sentences.
-More info on TIMIT dataset can be understood from the "README" which can be found here:
-https://catalog.ldc.upenn.edu/docs/LDC93S1/readme.txt
+More info on AudioData dataset can be understood from the "README" which can be found here:
+https://example.com/path/to/readme.txt
 """
 
-_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC93S1"
+# _HOMEPAGE = "https://example.com/path/to/dataset"
 
 
-class TimitASRConfig(datasets.BuilderConfig):
-    """BuilderConfig for TimitASR."""
+class AudioDataConfig(datasets.BuilderConfig):
+    """BuilderConfig for AudioData."""
 
     def __init__(self, **kwargs):
         """
         Args:
-          data_dir: `string`, the path to the folder containing the files in the
-            downloaded .tar
+          data_dir: `string`, the path to the folder containing the audio files
           citation: `string`, citation for the data set
           url: `string`, url for information about the data set
           **kwargs: keyword arguments forwarded to super.
         """
-        super(TimitASRConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs)
+        super(AudioDataConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
 
 
-class TimitASR(datasets.GeneratorBasedBuilder):
-    """TimitASR dataset."""
+class AudioData(datasets.GeneratorBasedBuilder):
+    """AudioData dataset."""
 
-    BUILDER_CONFIGS = [TimitASRConfig(name="clean", description="'Clean' speech.")]
+    BUILDER_CONFIGS = [AudioDataConfig(name="clean", description="'Clean' speech.")]
 
     @property
     def manual_download_instructions(self):
         return (
-            "To use TIMIT 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')`"
+            "To use AudioData you have to download it manually. "
+            "Please download the dataset from https://example.com/path/to/dataset \n"
+            "Then extract all audio files in one folder and load the dataset with: "
+            "`datasets.load_dataset('audio_data', data_dir='path/to/audio/folder')`"
         )
 
     def _info(self):
@@ -84,12 +78,10 @@ class TimitASR(datasets.GeneratorBasedBuilder):
                     "file": datasets.Value("string"),
                     "audio": datasets.Audio(sampling_rate=16_000),
                     "text": datasets.Value("string"),
-                    
-                    
                 }
             ),
             supervised_keys=("file", "text"),
-            homepage=_HOMEPAGE,
+            # homepage=_HOMEPAGE,
             citation=_CITATION,
             task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
         )
@@ -100,7 +92,7 @@ class TimitASR(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('audio_data', data_dir=...)` that includes audio files. Manual download instructions: {self.manual_download_instructions}"
             )
 
         return [
@@ -109,10 +101,9 @@ class TimitASR(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 AudioData based on the test/train csv information."""
         # 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
@@ -120,18 +111,10 @@ class TimitASR(datasets.GeneratorBasedBuilder):
             with txt_path.open(encoding="utf-8") as op:
                 transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number
 
-            # extract phonemes
-            
-            # extract words
-            
-
-            
-
             example = {
                 "file": str(wav_path),
                 "audio": str(wav_path),
                 "text": transcript,
-                
             }
 
             yield key, example