Update README.md

README.md

@@ -192,10 +192,12 @@ In addition, reference transcripts provided by Earnings22 were manually revalida
 
 ## Dataset Description
 
-HALAS consists of human-annotated hallucinations occurring in ASR transcriptions generated from recordings in the Earnings22 dataset:
+HALAS consists of human-annotated hallucinations occurring in ASR transcriptions generated from recordings in the Earnings22 dataset.
 
 https://huggingface.co/datasets/distil-whisper/earnings22
 
+
+
 The dataset was created using outputs from state-of-the-art ASR systems applied to naturally occurring earnings-call recordings containing speakers from 27 countries and a wide range of recording conditions.
 
 To maximize the prevalence of hallucinations in the annotation pool, candidate audio segments were selected using **inter-model disagreement**. Predictions from multiple ASR systems were compared using average pairwise Word Error Rate (WER), and segments with the highest disagreement were prioritized for annotation.
@@ -267,6 +269,7 @@ https://creativecommons.org/licenses/by-sa/4.0/
 HALAS contains original human annotations produced by the authors. Unless otherwise specified in the official repository, these annotations are intended to be distributed under the same license as the source dataset for compatibility with the underlying Earnings22 data.
 
 **Note:** Users should consult the official HALAS repository for the authoritative licensing terms of the released version.
+
 ---
 
 ## Dataset Sources
@@ -388,6 +391,48 @@ The source recordings originate from the Earnings22 dataset:
 
 Candidate segments were selected from portions exhibiting high disagreement between ASR systems.
 
+Audio files can be downloaded using following script:
+
+```
+import csv
+from datasets import load_dataset
+import soundfile as sf
+import os 
+
+# Load dataset
+filtered_dataset = load_dataset("distil-whisper/earnings22", 'chunked', split='test')
+
+# Create output directory
+output_dir = "e22"
+os.makedirs(output_dir, exist_ok=True)
+
+# CSV file setup
+csv_file = os.path.join(output_dir, "metadata.csv")
+
+with open(csv_file, mode='w', newline='', encoding='utf-8') as file:
+    writer = csv.writer(file)
+    writer.writerow(["filepath", "text"])  # Write header
+    
+    for audio in filtered_dataset:
+        seg_id = audio["segment_id"]
+        audio_array = audio["audio"]["array"]
+        sample_rate = audio["audio"]["sampling_rate"]
+        name = audio["file_id"]
+        name = "_".join([seg_id, name])
+        file_path = os.path.join(output_dir, name + ".wav")
+        text = audio['transcription']
+        
+        # Save audio file
+        sf.write(file_path, audio_array, sample_rate)
+        
+        # Write metadata to CSV
+        writer.writerow([file_path, text])
+        print(f"Saved: {file_path}")
+
+print("All files and metadata saved successfully.")
+
+```
+
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 ## Bias, Risks, and Limitations