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| import tempfile | |
| import gradio as gr | |
| import os | |
| import tensorflow_hub as hub | |
| import tensorflow as tf | |
| import tensorflow_text as text | |
| import sys | |
| import numpy as np | |
| import faiss | |
| import csv | |
| import datetime | |
| import joblib | |
| from huggingface_hub import hf_hub_download | |
| encoder_text_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['ENCODER_TEXT'], | |
| use_auth_token=os.environ['TOKEN']) | |
| print("DEBUG ", encoder_text_path) | |
| # NO GPU | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' | |
| # Cacher le nom du repo | |
| python_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['MODEL_FILE'], | |
| use_auth_token=os.environ['TOKEN']) | |
| print(python_path) | |
| os.system('ls -la') | |
| sys.path.append(os.environ['PRIVATE_DIR']) | |
| from models import * | |
| preprocess_model, model = get_models() | |
| index_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['INDEX'], | |
| use_auth_token=os.environ['TOKEN']) | |
| indexnames_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['INDEX_NAMES'], | |
| use_auth_token=os.environ['TOKEN']) ######### | |
| #catalog_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['CATALOG'], | |
| # use_auth_token=os.environ['TOKEN']) ############### | |
| catalog_path = get_catalog() | |
| url_dict=get_durl(catalog_path) ############ | |
| audio_names = joblib.load(open(indexnames_path, 'rb')) ############ | |
| index = faiss.read_index(index_path) | |
| encoder_text = tf.keras.models.load_model(encoder_text_path) | |
| def process(prompt, lang): | |
| now = datetime.datetime.now() | |
| print() | |
| print('*************') | |
| print("Current Time: ", str(now)) | |
| print("Text input : ", prompt) | |
| print('*************') | |
| print() | |
| # Embed text | |
| embed_query = get_predict(encoder_text, prompt, preprocess_model, model) | |
| faiss.normalize_L2(embed_query) | |
| print(" text embed computed.") | |
| # distance computing | |
| D, I = index.search(embed_query, TOP) | |
| # output : top N audio file names | |
| print(I) | |
| print(D) | |
| print("----") | |
| for i in range(len(I[0])): | |
| print(audio_names[I[0][i]], " with distance ", D[0][i]) | |
| print(" url : ", get_url(I[0][i], audio_names, url_dict)) | |
| return [get_url(I[0][0], audio_names, url_dict), get_url(I[0][1], audio_names, url_dict), get_url(I[0][2], audio_names, url_dict), get_url(I[0][3], audio_names, url_dict), get_url(I[0][4], audio_names, url_dict)] ####### | |
| inputs = [gr.Textbox(label="Input", value="type your description", max_lines=2), | |
| gr.Radio(label="Language", choices=["en"], value="en")] | |
| poc_examples = [#[["I love learning machine learning"],["autre"]] | |
| ["Mysterious filmscore with Arabic influenced instruments","en"], | |
| ["Let's go on a magical adventure with wizzards, dragons and castles","en"], | |
| ["Creepy piano opening evolves and speeds up into a cinematic orchestral piece","en"], | |
| ["Halloween rock with creepy organ","en"], | |
| ["Rhythmic electro dance track for sport, motivation and sweating","en"], | |
| ["soundtrack for an action movie from the eighties in a retro synth wave style","en"], | |
| ["Choral female singing is rhythmically accompanied in a church with medieval instruments","en"], | |
| ["Christmas","en"], | |
| ["love romantic with piano, strings and vocals","en"], | |
| ["Electronic soundscapes for chilling and relaxing","en"], | |
| ["Minimal, emotional, melancholic piano","en"], | |
| # ["Minimal, happy, joyful piano","en"], | |
| ["A calm and romantic acoustic guitar melody","en"], | |
| ["horror suspense piano","en"], | |
| ["Big Band","en"], | |
| ["90 eurodance beat","en"], | |
| ] | |
| # cacher ces textes aussi pour pas que le user puisse afficher des choses.... | |
| outputs = [gr.Audio(label="Track 1"), gr.Audio(label="Track 2"), gr.Audio(label="Track 3"), gr.Audio(label="Track 4"), gr.Audio(label="Track 5")] | |
| demo1 = gr.Interface(fn=process, inputs=inputs, outputs=outputs, examples=poc_examples, cache_examples=False) | |
| demo1.launch(debug=True) | |
| #demo1.launch(debug=True, enable_queue = False, auth=(os.environ['DEMO_LOGIN'], os.environ['DEMO_PWD']),auth_message = "Contact Simbals to get login/pwd")#, share=True) | |