Add files using upload-large-folder tool

.cache/embeddings_mert_all_datasets.csv

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+

.gitattributes

@@ -59,3 +59,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 dataset_audio_long_0.5_3.0s/metadata.csv filter=lfs diff=lfs merge=lfs -text
 dataset_audio_long_10_60s/metadata.csv filter=lfs diff=lfs merge=lfs -text
+dataset_audio_long_10_60s/embeddings_mert_all_datasets.csv filter=lfs diff=lfs merge=lfs -text
+dataset_audio_long_0.5_3.0s/embeddings_mert_all_datasets.csv filter=lfs diff=lfs merge=lfs -text

How2Uploade.txt

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+(jupyter) mshamsi@lst:~/expe/goto_raid_b/Song_Popularity$ env PATH="/lium/home/mshamsi/.local/bin:$PATH" hf upload-large-folder ENSIM/FreeSound_Popularity . --repo-type=dataset

dataset_audio_long_0.5_3.0s/embeddings_mert_all_datasets.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fab4057a372ee28185255dc0b1805985f082e19bd1733448cd7da1bfa3f8486f
+size 262250743

dataset_audio_long_10_60s/embeddings_mert_all_datasets.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4148c314e3091b44f8ff6ba0564f22931176d2d25c4037f2527c922551314ae4
+size 262920352

extract_mert_embedding.py

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+import os
+import torch
+import soundfile as sf
+import librosa
+import pandas as pd
+import numpy as np
+from transformers import AutoProcessor, AutoModel
+from tqdm import tqdm
+from datetime import datetime
+
+# =============================
+# CONFIG
+# =============================
+ROOT_DATA = "/lium/raid-b/mshamsi/FreeSound_Popularity/"
+OUTPUT_PATH = "embeddings_mert_all_datasets.csv"
+LOG_PATH = "errors_mert.log"
+
+TARGET_SR = 24000
+MAX_DURATION = 60  # secondes
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+AUDIO_EXTENSIONS = (".wav", ".WAV", ".mp3", ".flac", ".ogg", ".m4a")
+
+# =============================
+# INIT LOG
+# =============================
+with open(LOG_PATH, "w") as f:
+    f.write("=== MERT EXTRACTION LOG ===\n")
+    f.write(str(datetime.now()) + "\n\n")
+
+def log_error(msg):
+    with open(LOG_PATH, "a") as f:
+        f.write(msg + "\n")
+
+# =============================
+# LOAD MODEL
+# =============================
+try:
+    processor = AutoProcessor.from_pretrained(
+        "m-a-p/MERT-v1-330M",
+        trust_remote_code=True
+    )
+
+    model = AutoModel.from_pretrained(
+        "m-a-p/MERT-v1-330M",
+        trust_remote_code=True
+    ).to(DEVICE)
+
+    model.eval()
+
+except Exception as e:
+    log_error(f"[FATAL] Model loading failed: {e}")
+    raise RuntimeError("Impossible de charger le modèle MERT")
+
+
+# =============================
+# LOOP OVER DATASETS
+# =============================
+datasets = [
+    d for d in os.listdir(ROOT_DATA)
+    if os.path.isdir(os.path.join(ROOT_DATA, d))
+]
+
+for dataset_name in datasets:
+    dataset_path = os.path.join(ROOT_DATA, dataset_name)
+
+    # =============================
+    # STORAGE
+    # =============================
+    rows = []
+    processed = 0
+    skipped = 0
+
+    for batch in ["batch_001", "batch_002"]:
+        batch_path = os.path.join(dataset_path, batch)
+
+        if not os.path.exists(batch_path):
+            log_error(f"[INFO] Missing folder: {batch_path}")
+            continue
+
+        audio_files = [
+            f for f in os.listdir(batch_path)
+            if f.lower().endswith(AUDIO_EXTENSIONS)
+        ]
+
+        for audio_file in tqdm(audio_files, desc=f"{dataset_name}/{batch}"):
+            audio_path = os.path.join(batch_path, audio_file)
+
+            try:
+                # =============================
+                # LOAD AUDIO (SAFE)
+                # =============================
+                audio, sr = sf.read(audio_path, always_2d=False)
+
+                if audio is None or len(audio) == 0:
+                    raise ValueError("Empty audio file")
+
+                # Stereo → mono
+                if audio.ndim > 1:
+                    audio = np.mean(audio, axis=1)
+
+                # Convert to float32
+                audio = audio.astype(np.float32)
+
+                # Resample
+                if sr != TARGET_SR:
+                    audio = librosa.resample(
+                        audio,
+                        orig_sr=sr,
+                        target_sr=TARGET_SR
+                    )
+
+                # Trim / pad duration
+                max_len = TARGET_SR * MAX_DURATION
+                audio = audio[:max_len]
+
+                # =============================
+                # PROCESS WITH MERT
+                # =============================
+                inputs = processor(
+                    audio,
+                    sampling_rate=TARGET_SR,
+                    return_tensors="pt"
+                )
+                inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+
+                with torch.no_grad():
+                    outputs = model(**inputs)
+
+                if not hasattr(outputs, "last_hidden_state"):
+                    raise RuntimeError("Invalid model output")
+
+                embedding = (
+                    outputs.last_hidden_state
+                    .mean(dim=1)
+                    .squeeze()
+                    .cpu()
+                    .numpy()
+                )
+
+                # =============================
+                # SAVE ROW
+                # =============================
+                row = {
+                    "dataset": dataset_name,
+                    "batch": batch,
+                    "filename": audio_file
+                }
+
+                for i, val in enumerate(embedding):
+                    row[f"mert_{i}"] = float(val)
+
+                rows.append(row)
+                processed += 1
+
+            except Exception as e:
+                skipped += 1
+                log_error(f"[ERROR] {audio_path} -> {e}")
+
+    # =============================
+    # SAVE CSV
+    # =============================
+    df = pd.DataFrame(rows)
+    df.to_csv(os.path.join(dataset_path, OUTPUT_PATH), index=False)
+
+    print("\n=== EXTRACTION TERMINÉE ===")
+    print(f"Dataset : {dataset_name}")
+    print(f"Fichiers traités : {processed}")
+    print(f"Fichiers ignorés : {skipped}")
+    print(f"CSV sauvegardé : {OUTPUT_PATH}")
+    print(f"Log erreurs : {LOG_PATH}")