Upload extract_features.py with huggingface_hub

extract_features.py

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+import torch
+import pickle
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoProcessor, AutoModel
+from huggingface_hub import HfApi
+from tqdm import tqdm
+
+api = HfApi()
+
+print("Cargando dataset desde HuggingFace...")
+dataset = load_dataset(
+    "MrlolDev/voxtral-emotion-speech",
+    split="train+validation+test",
+)
+print(f"Total clips: {len(dataset)}")
+
+print("Cargando Voxtral encoder...")
+MODEL_ID = "mistralai/Voxtral-Mini-4B-Realtime-2602"
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModel.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
+model.eval()
+device = torch.device("cuda")
+model = model.to(device)
+print(f"Modelo en GPU: {next(model.parameters()).device}")
+
+EMOTIONS = ["neutral", "happy", "sad", "angry", "fear", "surprise"]
+
+
+def extract_features(audio_array, sampling_rate):
+    inputs = processor(audio_array, sampling_rate=sampling_rate, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        encoder_output = model.encoder(**inputs)
+        hidden = encoder_output.last_hidden_state  # (1, T, 1280)
+        features = hidden.mean(dim=1).squeeze(0)  # (1280,)
+
+    return features.cpu().float().numpy()
+
+
+records = []
+errors = 0
+
+print("Extrayendo features...")
+for sample in tqdm(dataset):
+    try:
+        audio_array = np.array(sample["audio"]["array"], dtype=np.float32)
+        sr = sample["audio"]["sampling_rate"]
+
+        features = extract_features(audio_array, sr)
+
+        records.append(
+            {
+                "features": features,
+                "label": EMOTIONS.index(sample["emotion"]),
+                "emotion": sample["emotion"],
+                "split": sample.get("split", "train"),
+                "sensevoice_score": sample.get("sensevoice_score", 0.0),
+            }
+        )
+    except Exception as e:
+        errors += 1
+        print(f"Error en sample: {e}")
+
+print(f"\nExtracted: {len(records)} features | Errors: {errors}")
+
+with open("features.pkl", "wb") as f:
+    pickle.dump(records, f)
+
+print("Saved to features.pkl")
+
+from collections import Counter
+
+dist = Counter(r["emotion"] for r in records)
+for emotion, count in sorted(dist.items()):
+    print(f"  {emotion}: {count}")
+
+print("\nUploading features.pkl to HuggingFace...")
+api.upload_file(
+    path_or_fileobj="features.pkl",
+    path_in_repo="features.pkl",
+    repo_id="MrlolDev/voxtral-emotion-speech",
+    repo_type="dataset",
+)
+print("✅ features.pkl uploaded to HuggingFace")
+
+print("\nUploading README.md to HuggingFace...")
+api.upload_file(
+    path_or_fileobj="README.md",
+    path_in_repo="README.md",
+    repo_id="MrlolDev/voxtral-emotion-speech",
+    repo_type="dataset",
+)
+print("✅ README.md uploaded to HuggingFace")