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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, time | |
| 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' | |
| max_results = 100 | |
| max_output = 5 | |
| # 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_myma() | |
| #audio_names = get_audio_names() | |
| audio_names = get_audio_names_pickle() | |
| 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() | |
| a=time.time() | |
| #try : | |
| embed_query = get_predict(encoder_text, prompt, preprocess_model, model) | |
| print("Embed time : ", time.time() - a) | |
| do_normalize(embed_query) | |
| D, I = get_distance(index, embed_query, TOP) | |
| print("Search + Embed time : ", time.time() - a) | |
| #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_myma(I[0][i], audio_names, url_dict)) | |
| formated = [] | |
| output_csv = f"{prompt}_results_text.csv" | |
| with open(output_csv, "w") as w: | |
| writer = csv.writer(w) | |
| count = 0 | |
| for top in I[0]: | |
| if count > max_output: | |
| break | |
| #formated.append(get_url_myma(top, audio_names, url_dict) | |
| #formated.append(audio_names[top].split('.')[0]) | |
| #writer.writerow(dict_catalog[file].values()) | |
| count += 1 | |
| out = [output_csv] | |
| for i in range(max_output): | |
| out.append(audio_names[I[0][i]].split('.')[0]) | |
| out.append(get_url_myma(I[0][i], audio_names, url_dict)) | |
| print("Total time : ", time.time() - a) | |
| return out | |
| '''return [output_csv, | |
| audio_names[I[0][0]].split('.')[0], get_url_myma(I[0][0], audio_names, url_dict), | |
| audio_names[I[0][1]].split('.')[0], get_url_myma(I[0][1], audio_names, url_dict), | |
| audio_names[I[0][2]].split('.')[0], get_url_myma(I[0][2], audio_names, url_dict), | |
| audio_names[I[0][3]].split('.')[0], get_url_myma(I[0][3], audio_names, url_dict), | |
| audio_names[I[0][4]].split('.')[0], get_url_myma(I[0][4], audio_names, url_dict)] | |
| ''' | |
| ''' | |
| except: | |
| return ["", | |
| "Error input - please try again", "", | |
| "Error input - please try again", "", | |
| "Error input - please try again", "", | |
| "Error input - please try again", "", | |
| "Error input - please try again", "",] | |
| ''' | |
| 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.File()] | |
| for i in range(max_output): | |
| outputs.append(gr.Textbox(label=f"top{i} track name", show_label=True)) | |
| outputs.append(gr.Audio(label=f"top{i}", show_label=False)) | |
| '''outputs = [gr.File(), | |
| gr.Textbox(label="Track name 1"), gr.Audio(label="Track 1", show_label=False), | |
| gr.Textbox(label="Track name 2"), gr.Audio(label="Track 2", show_label=False), | |
| gr.Textbox(label="Track name 3"), gr.Audio(label="Track 3", show_label=False), | |
| gr.Textbox(label="Track name 4"), gr.Audio(label="Track 4", show_label=False), | |
| gr.Textbox(label="Track name 5"), gr.Audio(label="Track 5", show_label=False)] | |
| ''' | |
| demo1 = gr.Interface(fn=process, inputs=inputs, outputs=outputs, examples=poc_examples, cache_examples=False, examples_per_page=20) | |
| demo1.launch(debug=False) | |