import gradio as gr import torch import random from unidecode import unidecode from samplings import top_p_sampling, temperature_sampling from transformers import AutoTokenizer, AutoModelForSeq2SeqLM device = torch.device("cuda" if torch.cuda.is_available() else "cpu") description = """
Duplicate Space
## ℹ️ How to use this demo? 1. Enter a query in the text box. 2. You can set the parameters (i.e., number of tunes, maximum length, top-p, temperature, and random seed) for the generation. (optional) 3. Click "Submit" and wait for the result. 4. The generated ABC notation can be converted to MIDI or PDF using [EasyABC](https://sourceforge.net/projects/easyabc/), you can also use this [online renderer](https://ldzhangyx.github.io/abc/) to render the ABC notation. ## ❕Notice - The text box is case-sensitive. - The demo is based on BART-base and fine-tuned on the Textune dataset (282,870 text-music pairs). - The demo only supports English text as the input. - The demo is still in the early stage, and the generated music is not perfect. If you have any suggestions, please feel free to contact me via [email](mailto:shangda@mail.ccom.edu.cn). """ examples = [ ["This is a traditional Irish dance music.\nNote Length-1/8\nMeter-6/8\nKey-D", 1, 1024, 0.9, 1.0, 0], ["This is a jazz-swing lead sheet with chord and vocal.", 1, 1024, 0.9, 1.0, 0] ] def generate_abc(text, num_tunes, max_length, top_p, temperature, seed): try: seed = int(seed) except: seed = None print("Input Text:\n" + text) text = unidecode(text) tokenizer = AutoTokenizer.from_pretrained('sander-wood/text-to-music') model = AutoModelForSeq2SeqLM.from_pretrained('sander-wood/text-to-music') model = model.to(device) input_ids = tokenizer(text, return_tensors='pt', truncation=True, max_length=max_length)['input_ids'].to(device) decoder_start_token_id = model.config.decoder_start_token_id eos_token_id = model.config.eos_token_id random.seed(seed) tunes = "" for n_idx in range(num_tunes): print("\nX:"+str(n_idx+1)+"\n", end="") tunes += "X:"+str(n_idx+1)+"\n" decoder_input_ids = torch.tensor([[decoder_start_token_id]]) for t_idx in range(max_length): if seed!=None: n_seed = random.randint(0, 1000000) random.seed(n_seed) else: n_seed = None outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids.to(device)) probs = outputs.logits[0][-1] probs = torch.nn.Softmax(dim=-1)(probs).cpu().detach().numpy() sampled_id = temperature_sampling(probs=top_p_sampling(probs, top_p=top_p, seed=n_seed, return_probs=True), seed=n_seed, temperature=temperature) decoder_input_ids = torch.cat((decoder_input_ids, torch.tensor([[sampled_id]])), 1) if sampled_id!=eos_token_id: sampled_token = tokenizer.decode([sampled_id]) print(sampled_token, end="") tunes += sampled_token else: tunes += '\n' break return tunes input_text = gr.inputs.Textbox(lines=5, label="Input Text", placeholder="Describe the music you want to generate ...") input_num_tunes = gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Number of Tunes") input_max_length = gr.inputs.Slider(minimum=10, maximum=1000, step=10, default=500, label="Max Length") input_top_p = gr.inputs.Slider(minimum=0.0, maximum=1.0, step=0.05, default=0.9, label="Top P") input_temperature = gr.inputs.Slider(minimum=0.0, maximum=2.0, step=0.1, default=1.0, label="Temperature") input_seed = gr.inputs.Textbox(lines=1, label="Seed (int)", default="None") output_abc = gr.outputs.Textbox(label="Generated Tunes") gr.Interface(fn=generate_abc, inputs=[input_text, input_num_tunes, input_max_length, input_top_p, input_temperature, input_seed], outputs=output_abc, title="Textune: Generating Tune from Text", description=description, examples=examples).launch(debug=True)