Ajouter le script Gradio et les dépendances

app.py

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+import gradio as gr
+import laion_clap
+from qdrant_client import QdrantClient
+
+# Loading the Qdrant DB in local ###################################################################
+client = QdrantClient("localhost", port=6333)
+print("[INFO] Client created...")
+
+# loading the model
+print("[INFO] Loading the model...")
+model_name = "laion/larger_clap_music"
+model = laion_clap.CLAP_Module(enable_fusion=False)
+model.load_ckpt()  # download the default pretrained checkpoint.
+
+# Gradio Interface #################################################################################
+max_results = 10
+
+
+def sound_search(query):
+    text_embed = model.get_text_embedding([query, ''])[0]  # trick because can't accept singleton
+    hits = client.search(
+        collection_name="demo_db7",
+        query_vector=text_embed,
+        limit=max_results,
+    )
+    return [
+        gr.Audio(
+            hit.payload['audio_path'],
+            label=f"style: {hit.payload['style']} -- score: {hit.score}")
+        for hit in hits
+    ]
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# Sound search database """
+    )
+    inp = gr.Textbox(placeholder="What sound are you looking for ?")
+    out = [gr.Audio(label=f"{x}") for x in range(max_results)]  # Necessary to have different objs
+    inp.change(sound_search, inp, out)
+
+demo.launch()

database.py

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+import hashlib
+import os
+from glob import glob
+
+import laion_clap
+from diskcache import Cache
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from tqdm import tqdm
+
+
+# Functions utils ##################################################################################
+def get_md5(fpath):
+    with open(fpath, "rb") as f:
+        file_hash = hashlib.md5()
+        while chunk := f.read(8192):
+            file_hash.update(chunk)
+    return file_hash.hexdigest()
+
+
+# PARAMETERS #######################################################################################
+CACHE_FOLDER = '/home/nahia/data/audio/'
+KAGGLE_TRAIN_PATH = '/home/nahia/Documents/audio/actor/Actor_01/'
+
+# ################## Loading the CLAP model ###################
+print("[INFO] Loading the model...")
+model_name = 'music_speech_epoch_15_esc_89.25.pt'
+model = laion_clap.CLAP_Module(enable_fusion=False)
+model.load_ckpt()  # download the default pretrained checkpoint.
+
+# Initialize the cache
+os.makedirs(CACHE_FOLDER, exist_ok=True)
+cache = Cache(CACHE_FOLDER)
+
+# Embed the audio files !
+audio_files = [p for p in glob(os.path.join(KAGGLE_TRAIN_PATH, '*.wav'))]
+audio_embeddings = []
+chunk_size = 100
+total_chunks = int(len(audio_files) / chunk_size)
+
+# Use tqdm for a progress bar
+for i in tqdm(range(0, len(audio_files), chunk_size), total=total_chunks):
+    chunk = audio_files[i:i + chunk_size]  # Get a chunk of audio files
+    chunk_embeddings = []
+
+    for audio_file in chunk:
+        # Compute a unique hash for the audio file
+        file_key = get_md5(audio_file)
+
+        if file_key in cache:
+            # If the embedding for this file is cached, retrieve it
+            embedding = cache[file_key]
+        else:
+            # Otherwise, compute the embedding and cache it
+            embedding = model.get_audio_embedding_from_filelist(x=[audio_file], use_tensor=False)[
+                0]  # Assuming the model returns a list
+            cache[file_key] = embedding
+        chunk_embeddings.append(embedding)
+    audio_embeddings.extend(chunk_embeddings)
+
+# It's a good practice to close the cache when done
+cache.close()
+
+# Creating a qdrant collection #####################################################################
+client = QdrantClient("localhost", port=6333)
+print("[INFO] Client created...")
+
+print("[INFO] Creating qdrant data collection...")
+client.create_collection(
+     collection_name="demo_db7",
+     vectors_config=models.VectorParams(
+         size=audio_embeddings[0].shape[0],
+         distance=models.Distance.COSINE
+     ),
+ )
+
+# Créer des enregistrements Qdrant à partir des embeddings
+records = []
+for idx, (audio_path, embedding) in enumerate(zip(audio_files, audio_embeddings)):
+    record = models.PointStruct(
+        id=idx,
+        vector=embedding,
+        payload={"audio_path": audio_path, "style": audio_path.split('/')[-2]}
+    )
+    records.append(record)
+
+# Téléverser les enregistrements dans la collection Qdrant
+print("[INFO] Uploading data records to data collection...")
+client.upload_points(collection_name="demo_db7", points=records)
+print("[INFO] Successfully uploaded data records to data collection!")