Upload folder using huggingface_hub

fleurs_cleaned_kk_not_translated.tar.gz.partaa

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55e04d91d51f06a8d0cd0f4b24f2e2c53161444c01510bfe111f9a3ff8872970
+size 2753859229

readme.md

@@ -0,0 +1,87 @@
+# tar
+```bash
+cd /home/vladimir_albrekht/projects/2025_sep_22_qwen3omni/ms_swift_training/approach_2_transformers_based/data/fleurs_data
+tar -czf - fleurs_cleaned_kk_not_translated | split -b 4G - fleurs_cleaned_kk_not_translated.tar.gz.part
+```
+
+# cat
+
+```bash
+cat fleurs_cleaned_kk_not_translated.tar.gz.part* | tar -xzf -
+```
+
+
+## processing logic
+
+```python
+# 1. data part
+import pandas as pd
+from pathlib import Path
+
+def load_fleurs_split(split_name, base_path="/home/vladimir_albrekht/projects/2025_sep_22_qwen3omni/ms_swift_training/approach_2_transformers_based/data/fleurs_data/fleurs/data/kk_kz"):
+    df = pd.read_csv(
+        f"{base_path}/{split_name}.tsv",
+        sep="\t",
+        header=None,
+        names=["id", "file_name", "raw_transcription", "transcription", "phonemes", "num_samples", "gender"]
+    )
+    df['num_samples'] = pd.to_numeric(df['num_samples'], errors='coerce')
+    df['duration_seconds'] = df['num_samples'] / 16000
+    df['dataset_type'] = split_name
+    df['audio_dir'] = f"{base_path}/audio/{split_name}"
+    return df
+
+df_test = load_fleurs_split("test")
+df_dev = load_fleurs_split("dev")
+df_train = load_fleurs_split("train")
+
+df = pd.concat([df_test, df_dev, df_train], ignore_index=True)
+df_cleaned = df[df['duration_seconds'] <= 29.9]
+
+duplicate_files = df_cleaned['file_name'].duplicated().sum()
+unique_files = df_cleaned['file_name'].nunique()
+total_files = len(df_cleaned)
+print(f"Total samples: {total_files}")
+df_cleaned = df_cleaned.drop(columns=['phonemes', 'num_samples'])
+df_cleaned = df_cleaned.rename(columns={
+    'raw_transcription': 'transcription',
+    'transcription': 'raw_transcription'
+})
+
+
+# 2. convert to .jsonl format
+import shutil
+from pathlib import Path
+
+output_dir = Path("/home/vladimir_albrekht/projects/2025_sep_22_qwen3omni/ms_swift_training/approach_2_transformers_based/data/fleurs_data/fleurs_cleaned_kk")
+output_dir.mkdir(exist_ok=True)
+audio_output_dir = output_dir / "audios"
+audio_output_dir.mkdir(exist_ok=True)
+
+jsonl_data = []
+for idx, row in df_cleaned.iterrows():
+    src_audio = Path(row['audio_dir']) / row['file_name']
+    new_audio_name = f"{row['dataset_type']}_{row['file_name']}"
+    dst_audio = audio_output_dir / new_audio_name
+    
+    shutil.copy(src_audio, dst_audio)
+    
+    jsonl_data.append({
+        "transcription": row['transcription'],
+        "audio_path": f"audios/{new_audio_name}",
+        "meta_data": {
+            "id": row['id'],
+            "raw_transcription": row['raw_transcription'],
+            "duration_seconds": row['duration_seconds'],
+            "gender": row['gender'],
+            "dataset_type": row['dataset_type']
+        }
+    })
+
+import json
+with open(output_dir / "data.jsonl", 'w', encoding='utf-8') as f:
+    for item in jsonl_data:
+        f.write(json.dumps(item, ensure_ascii=False) + '\n')
+
+print(f"Saved {len(jsonl_data)} samples to {output_dir}")
+```