File size: 1,438 Bytes
845fb83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | from datasets import load_from_disk, Audio
import re
# ==========================
# Config
# ==========================
DATA_DIR = "data/dataset_hindi_6k"
HF_REPO ="PharynxAI/merged_multilingual_tts_6k_each"
SAMPLING_RATE = 24000
# ==========================
# Load merged dataset
# ==========================
print("📌 Loading merged dataset from disk...")
ds = load_from_disk(DATA_DIR)
print(ds)
print(ds.features)
# ==========================
# Apply SAME preprocessing as example
# ==========================
def apply_preprocessing(example):
text = example["text"].strip()
# Match example: add Speaker prefix
if not text.startswith("Speaker"):
text = f"Speaker 0: {text}"
return {"text": text}
ds = ds.map(apply_preprocessing, num_proc=4)
# EXACT equivalent of:
# dataset = dataset.cast_column("audio", Audio(sampling_rate=24000))
ds = ds.cast_column("audio", Audio(sampling_rate=SAMPLING_RATE))
# ==========================
# Final verification
# ==========================
print("✅ Final features:")
print(ds.features)
print("📝 Sample:", ds[0]["text"])
print("🔊 SR:", ds[0]["audio"]["sampling_rate"])
# ==========================
# Push to Hub
# ==========================
#print(f"🚀 Pushing dataset to: {HF_REPO}")
#ds.push_to_hub(HF_REPO, max_shard_size="500MB",num_proc=1)
ds.save_to_disk("data/dataset_hindi_6k_processed")
print("Saved dataset locally")
print("🎉 Done.")
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