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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", 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() |