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| """## Imports""" | |
| import tensorflow as tf | |
| config = tf.compat.v1.ConfigProto() | |
| config.gpu_options.allow_growth = True | |
| session = tf.compat.v1.Session(config=config) | |
| import gradio as gr | |
| from pathlib import Path | |
| import subprocess | |
| from transformers import AutoTokenizer | |
| from transformers import TFAutoModelForCausalLM | |
| from transformers import TFAutoModelForSequenceClassification, AutoTokenizer | |
| import numpy as np | |
| from musicautobot.music_transformer.transform import idxenc2stream, midi2idxenc | |
| from musicautobot.vocab import MusicVocab | |
| import keras | |
| """## Model Loads""" | |
| #load poem generation model | |
| model_poem_gn = TFAutoModelForCausalLM.from_pretrained('merged-ui/models-misc/peom_gn') | |
| base_model_poem_gn = "distilgpt2" | |
| tokenizer_poem_gn = AutoTokenizer.from_pretrained(base_model_poem_gn) | |
| #load sentiment analysis | |
| model_sa = TFAutoModelForSequenceClassification.from_pretrained('merged-ui/models-misc/sen_analysis/bert') | |
| base_model_sa = "distilbert-base-uncased" | |
| tokenizer_sa = AutoTokenizer.from_pretrained(base_model_sa) | |
| #music generation | |
| """ | |
| base_path = "/content/drive/MyDrive/FIRE_3rd Sem/music_gn/" | |
| #path_mid_file -> Replace this with model generated file path | |
| path_mid_file = base_path + "Comic_Relief.mid" | |
| path_wav_file = base_path + "output_comic.wav" | |
| subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file])""" | |
| music_gen_base_path = "merged-ui/music_gen/" | |
| model_music_gen = keras.models.load_model("transformer-final") | |
| """## Music Generation""" | |
| def predict_music(model, input_vector, num): | |
| normalized = input_vector / 311 | |
| for i in range(num): | |
| predict = model.predict(np.reshape(normalized[-100:], (1,100)), verbose = 0) | |
| normalized = np.append(normalized, predict) | |
| result = np.rint(normalized * 311) | |
| # edits to prediction | |
| for i in range(100, len(result)): | |
| if i % 2 == 0: | |
| if abs(result[i] - 8) < 5 and result[i] != 8: | |
| result[i] = 8 | |
| else: | |
| if result[i] < 137: | |
| result[i] = 137 | |
| return result | |
| # this function takes a 100 length encoded song beginning as an input and | |
| def midi_predict(model, test, num_notes): | |
| test_midi = idxenc2stream(test.astype("int"), MusicVocab.create()) | |
| test_midi.write('midi',music_gen_base_path+"input_demo.mid") | |
| res = predict_music(model, test, num_notes) | |
| output = idxenc2stream(res.astype("int"), MusicVocab.create()) | |
| output.write('midi',music_gen_base_path+"output_demo.mid") | |
| path_mid_file = music_gen_base_path + "output_demo.mid" | |
| path_wav_file = music_gen_base_path + "output_demo.wav" | |
| # need timidity for this | |
| subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file]) | |
| return | |
| def inference_music_gen(audio, num_notes): | |
| data_e = midi2idxenc(audio.name, MusicVocab.create()) | |
| midi_predict(model_music_gen, data_e[:100], int(num_notes)) | |
| return | |
| music_gen_interface = gr.Interface( | |
| inference_music_gen, | |
| inputs = [gr.inputs.File(type="file", label="Input"), gr.Textbox(lines = 1, placeholder = "Enter number of notes here")], | |
| examples=[[music_gen_base_path + "mid_file/Comic_Relief.mid", 300]], | |
| outputs = gr.outputs.Audio(type="filepath", label="Output") | |
| ) | |
| """## Sentiment Analysis""" | |
| def inference_sentiment_analysis(sen): | |
| tokenized_v1 = tokenizer_sa([sen], return_tensors="np", padding="longest") | |
| outputs_v1 = model_sa(tokenized_v1).logits | |
| classifications_v1 = np.argmax(outputs_v1, axis=1) | |
| if classifications_v1[0] == 1: | |
| res = "Positive :)" | |
| else: | |
| res = "Negative :(" | |
| return res | |
| sentiment_analysis_interface = gr.Interface( | |
| fn=inference_sentiment_analysis, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter a Sentence"), | |
| outputs="text", | |
| ) | |
| """## Peom Generation""" | |
| def inference_poem_gen(start): | |
| tokenized = tokenizer_poem_gn(start, return_tensors="np") | |
| outputs = model_poem_gn.generate(**tokenized, max_new_tokens=20) | |
| res = tokenizer_poem_gn.decode(outputs[0]) | |
| return res.replace("<LINE>", "\n") | |
| poem_gen_interface = gr.Interface( | |
| fn=inference_poem_gen, | |
| inputs=gr.Textbox(lines=2, placeholder="Start Here..."), | |
| outputs="text", | |
| ) | |
| """## Combine All""" | |
| demo = gr.TabbedInterface([music_gen_interface, poem_gen_interface, sentiment_analysis_interface], | |
| ["Music Generation", "Poem Generation", "Sentiment Analysis"]) | |
| demo.launch(debug=True, share=True) | |