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import tempfile | |
import gradio as gr | |
import os | |
import tensorflow as tf | |
import sys | |
import numpy as np | |
import csv | |
import datetime | |
import joblib | |
from huggingface_hub import hf_hub_download | |
# 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) | |
sys.path.append(os.environ['PRIVATE_DIR']) | |
from models import * | |
preprocess_model, model = get_models() | |
url_dict = get_durl() | |
audio_names = get_audio_names() | |
index = get_index() | |
#encoder_text = get_encoder_text() | |
encoder_text = tf.keras.models.load_model("encoder_text_retrievaltext_bmg_221022_54_clean") | |
def process(prompt, lang): | |
now = datetime.datetime.now() | |
print() | |
print('*************') | |
print("Current Time: ", str(now)) | |
print("Text input : ", prompt) | |
print('*************') | |
print() | |
embed_query = get_predict(encoder_text, prompt, preprocess_model, model) | |
do_normalize(embed_query) | |
D, I = get_distance(index, embed_query, TOP) | |
#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 = [ | |
["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"], | |
["Chilled electronic","en"], | |
#["","en"], | |
["Relax piano","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"], | |
["A calm and romantic acoustic guitar melody","en"], | |
["horror suspense piano","en"], | |
["Big Band","en"], | |
["90 eurodance beat","en"], | |
] | |
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, examples_per_page=20) | |
demo1.launch(debug=False) | |