Upload README.md with huggingface_hub

README.md

@@ -59,23 +59,37 @@ Each example contains:
 
 ### Recommended: Download All Files First
 
-To ensure all audio files are available, download the entire dataset first:
+To ensure all audio files are available and avoid rate limits, authenticate first:
 
 ```python
-from huggingface_hub import snapshot_download, login
+from huggingface_hub import snapshot_download, HfApi
 from datasets import load_dataset, Audio
 import os
+import time
 
-# Login to HuggingFace (to avoid rate limits)
-# Get your token from: https://huggingface.co/settings/tokens
-login()  # Will prompt for token in Colab, or use: login(token="your_token_here")
+# IMPORTANT: Login FIRST and WAIT for confirmation
+# Method 1: Using token directly (RECOMMENDED for Colab)
+from huggingface_hub import login
+HF_TOKEN = "hf_YourTokenHere"  # Get from https://huggingface.co/settings/tokens
+login(token=HF_TOKEN)
+
+# Verify login worked
+api = HfApi()
+user_info = api.whoami(token=HF_TOKEN)
+print(f"✓ Logged in as: {user_info['name']}")
+
+# Small delay to ensure auth propagates
+time.sleep(2)
 
 # Download entire dataset (parquet + all audio files)
-print("Downloading dataset (~2GB)...")
+print("\nDownloading dataset (~2GB)...")
 local_dir = snapshot_download(
     "Aybee5/HausaTTSEmbed",
     repo_type="dataset",
-    local_dir="hausa_tts_data"
+    local_dir="hausa_tts_data",
+    token=HF_TOKEN,  # Pass token explicitly
+    max_workers=1,   # Reduce concurrent requests to avoid rate limits
+    resume_download=True  # Resume if interrupted
 )
 
 # Load from downloaded files
@@ -99,34 +113,76 @@ print(f"Text: {sample['text']}")
 print(f"Audio shape: {sample['audio']['array'].shape}")
 ```
 
+**Alternative: Interactive Login (prompts for token)**
+
+```python
+from huggingface_hub import login, snapshot_download
+import time
+
+# This will prompt you to paste your token
+login()
+time.sleep(2)  # Wait for auth to propagate
+
+# Then download
+local_dir = snapshot_download(
+    "Aybee5/HausaTTSEmbed",
+    repo_type="dataset",
+    local_dir="hausa_tts_data",
+    max_workers=1  # Reduce concurrent requests
+)
+```
+
 ### For Unsloth TTS Training (Complete Code)
 
 Use this complete code in your Unsloth/Colab notebook:
 
 ```python
-from huggingface_hub import snapshot_download
+from huggingface_hub import snapshot_download, login, HfApi
 from datasets import load_dataset, Audio
 import os
+import time
+
+# ==================== STEP 1: AUTHENTICATE ====================
+# Replace with your actual token from https://huggingface.co/settings/tokens
+HF_TOKEN = "hf_YourTokenHere"
+
+print("Authenticating with HuggingFace...")
+login(token=HF_TOKEN)
+
+# Verify authentication
+api = HfApi()
+user_info = api.whoami(token=HF_TOKEN)
+print(f"✓ Logged in as: {user_info['name']}\n")
+
+# Wait for auth to propagate
+time.sleep(2)
+
+# ==================== STEP 2: DOWNLOAD DATASET ====================
+print("Downloading Hausa TTS dataset (~2GB)...")
+print("Using reduced concurrency to avoid rate limits...\n")
 
-# Step 1: Download entire dataset
-print("Downloading Hausa TTS dataset...")
 local_dir = snapshot_download(
     "Aybee5/HausaTTSEmbed",
     repo_type="dataset",
-    local_dir="/content/hausa_tts"  # Use /content/ for Colab
+    local_dir="/content/hausa_tts",  # Use /content/ for Colab
+    token=HF_TOKEN,  # Pass token explicitly
+    max_workers=1,   # Single threaded to avoid rate limits
+    resume_download=True
 )
 
-# Step 2: Load from downloaded files
+print(f"✓ Downloaded to: {local_dir}\n")
+
+# ==================== STEP 3: LOAD DATASET ====================
 raw_ds = load_dataset(
     "parquet",
     data_files=f"{local_dir}/data/*.parquet",
     split="train"
 )
 
-# Step 3: Fix audio paths
+# ==================== STEP 4: FIX AUDIO PATHS ====================
 raw_ds = raw_ds.map(lambda x: {"audio": os.path.join(local_dir, x["audio"]), **x})
 
-# Step 4: Speaker handling (Unsloth's exact code)
+# ==================== STEP 5: HANDLE SPEAKERS ====================
 speaker_key = "source"
 if "source" not in raw_ds.column_names and "speaker_id" not in raw_ds.column_names:
     print("Unsloth: No speaker found, adding default source")
@@ -135,22 +191,30 @@ if "source" not in raw_ds.column_names and "speaker_id" not in raw_ds.column_nam
 elif "source" not in raw_ds.column_names and "speaker_id" in raw_ds.column_names:
     speaker_key = "speaker_id"
 
-# Step 5: Resample to target sample rate
+# ==================== STEP 6: RESAMPLE AUDIO ====================
 target_sampling_rate = 24000
 raw_ds = raw_ds.cast_column("audio", Audio(sampling_rate=target_sampling_rate))
 
 print(f"✓ Dataset ready: {len(raw_ds)} samples")
+print(f"✓ Speaker column: {speaker_key}\n")
 
-# Step 6: Optional - Split into train/validation
+# ==================== STEP 7: OPTIONAL SPLIT ====================
 split_ds = raw_ds.train_test_split(test_size=0.1, seed=42)
 train_ds = split_ds['train']
 val_ds = split_ds['test']
 
-# Step 7: Continue with your Unsloth training!
-# processed_ds = raw_ds.map(preprocess_example, ...)
+print(f"✓ Train: {len(train_ds)} samples")
+print(f"✓ Validation: {len(val_ds)} samples")
+
+# Continue with your Unsloth training!
 ```
 
-This will work without FileNotFoundError! 🎉
+**Key Changes to Avoid Rate Limits:**
+1. ✅ Pass `token=HF_TOKEN` explicitly to `snapshot_download()`
+2. ✅ Set `max_workers=1` to reduce concurrent requests
+3. ✅ Add `time.sleep(2)` after login to ensure auth propagates
+4. ✅ Verify authentication with `api.whoami()` before downloading
+5. ✅ Use `resume_download=True` to handle interruptions
 
 ### With Transformers