Delete app.py

app.py

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-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 = """
-<div>
-
-<a style="display:inline-block" href='https://github.com/sander-wood/text-to-music'><img src='https://img.shields.io/github/stars/sander-wood/text-to-music?style=social' /></a>
-<a style="display:inline-block" href="https://arxiv.org/pdf/2211.11216.pdf"><img src="https://img.shields.io/badge/arXiv-2211.11216-b31b1b.svg"></a>
-<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/sander-wood/text-to-music?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-md-dark.svg" alt="Duplicate Space"></a>
-</div>
-
-## 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:[email protected]).
-"""
-
-
-examples = [
-    ["This is a traditional Irish dance music.\nNote Length-1/8\nMeter-6/8\nKey-D", 3, 1024, 0.9, 1.0, 0],
-    ["This is a jazz-swing lead sheet with chord and vocal.", 3, 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
-
-    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)