Upload 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
+
+    print("Input Text: " + 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)