Create dataset_download.py

dataset_download.py

@@ -0,0 +1,122 @@
+
+from datasets import load_dataset, Audio
+import soundfile as sf, os, re, neologdn, librosa
+from tqdm import tqdm
+import shutil
+
+def have(a):
+    return a is not None
+
+def aorb(a, b):
+    return a if have(a) else b
+
+dataset = load_dataset("Sin2pi/JA_audio_JA_text_180k_samples", trust_remote_code=True)["train"].filter(lambda sample: bool(sample["sentence" if "sentence" in sample else aorb("text", "transcription")]))
+name = "JA_audio_JA_text_180k"
+
+ouput_dir = "./datasets/"
+out_file = 'metadata.csv'
+os.makedirs(ouput_dir + name, exist_ok=True)
+folder_path = ouput_dir + name
+
+top_db=30
+
+def is_silent(mp3_file, threshold=0.025):
+    if not os.path.exists(mp3_file):
+        return True 
+    y, sr = librosa.load(mp3_file, sr=None)
+    rms = librosa.feature.rms(y=y)[0]
+    return all(value < threshold for value in rms)
+
+def remove_silence(input_file, output_file, top_db=top_db):
+        y, sr = sf.read(input_file)
+        intervals = librosa.effects.split(y, top_db=top_db)
+        y_trimmed = []
+        for start, end in intervals:
+            y_trimmed.extend(y[start:end])          
+        if not os.path.exists(output_file):    
+            sf.write(output_file, y_trimmed, sr)
+            with open(csv_file2, "a", encoding='utf-8') as f:
+                file_name = os.path.basename(output_file)
+                f.write(file_name + "\n")
+
+def process_directory(input_dir, output_dir, top_db=top_db):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    if not os.path.exists(removed_dir):
+        os.makedirs(removed_dir)
+    open(csv_file, 'w', encoding='utf-8').close()
+    open(csv_file2, 'w', encoding='utf-8').close()
+    
+    for filename in os.listdir(input_dir):
+        if filename.endswith(".mp3"):
+            input_file = os.path.join(input_dir, filename)
+            output_file = os.path.join(output_dir, filename)
+            removed_file = os.path.join(removed_dir, filename)
+            
+            if not os.path.exists(output_file):
+                remove_silence(input_file, output_file, top_db)
+
+            if os.path.exists(output_file) and is_silent(output_file):
+                with open(csv_file, "a", encoding='utf-8') as f: 
+                    f.write(os.path.basename(output_file) + "\n") 
+                shutil.move(output_file, removed_file) 
+            
+            if os.path.exists(input_file):
+                os.remove(input_file)
+
+input_dir = folder_path
+output_dir = folder_path + "/trimmed/"
+removed_dir = folder_path + "/removed/"
+csv_file = folder_path + "/removed.csv"   
+csv_file2 = folder_path + "/not_removed.csv"  
+
+min_char = 4
+max = 20.0
+min = 1.0
+
+char = '[　０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890♬♪♩♫]'
+special_characters = '[“%‘”~゛＃＄％＆（）＊＋：；〈＝〉＠＾＿｛｜｝～"█』『.;:<>_()*&^$#@`, ]'
+
+# dataset = dataset.cast_column("file_url", datasets.Audio())
+dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+sentence_map = {}
+
+open(os.path.join(folder_path, out_file), 'w', encoding='utf-8').close()
+
+for i, sample in tqdm(enumerate(dataset)):
+    if sample["sentence"] != "":
+        audio_sample_name = name + f'_{i}.mp3' 
+        audio_path_original = os.path.join(folder_path, audio_sample_name)
+
+        patterns = [(r"…",'。'), (r"!!",'!'), (special_characters,""), (r"\s+", "")]
+        for pattern, replace in patterns:
+
+            sample["sentence"] = re.sub(pattern, replace, sample["sentence"])
+        sample["sentence"] = (neologdn.normalize(sample["sentence"], repeat=1))
+        if sample["sentence"][-1] not in ["!", "?", "。"]:
+            sample["sentence"] += "。"
+        
+        sentence_length = len(sample["sentence"])
+        audio_length = len(sample['file_url' if "file_url" in sample else "audio"]["array"]) / sample['file_url' if "file_url" in sample else "audio"]["sampling_rate"]
+
+        if max > audio_length > min and not re.search(char, sample["sentence"]) and sentence_length > min_char and bool(sample["sentence"]):
+            if not os.path.exists(audio_path_original):
+                sf.write(audio_path_original, sample['file_url' if "file_url" in sample else "audio"]["array"], sample['file_url' if "file_url" in sample else "audio"]["sampling_rate"])
+            sentence_map[audio_sample_name] = sample['sentence']
+            
+print(f"Downloaded {len(sentence_map)} audio files to {folder_path}. Starting silence trimming...")
+process_directory(input_dir, output_dir)
+print(f"Silence trimming complete. Trimmed files are in {output_dir}, silent files moved to {removed_dir}.")
+
+print(f"Generating final metadata.csv in {folder_path}...")
+with open(csv_file2, 'r', encoding='utf-8') as f_not_removed:
+    for line in f_not_removed:
+        trimmed_filename = line.strip() 
+        
+        if trimmed_filename in sentence_map:
+            sentence = sentence_map[trimmed_filename]
+            with open(os.path.join(folder_path, out_file), 'a', encoding='utf-8') as transcription_file:
+                transcription_file.write(trimmed_filename + ",") 
+                transcription_file.write(sentence)
+                transcription_file.write('\n')
+print(f"Metadata.csv generated for {os.path.join(folder_path, out_file)}.")