305 lines
10 KiB
Python
305 lines
10 KiB
Python
import argparse
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from concurrent.futures import ProcessPoolExecutor
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import os
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import subprocess as sp
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from tempfile import NamedTemporaryFile
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import time
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import warnings
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import torch
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import gradio as gr
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from audiocraft.data.audio_utils import convert_audio
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from audiocraft.data.audio import audio_write
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from audiocraft.models import MusicGen
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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MODEL = None # Last used model
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IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
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MAX_BATCH_SIZE = 12
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BATCHED_DURATION = 15
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INTERRUPTING = False
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# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
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_old_call = sp.call
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def _call_nostderr(*args, **kwargs):
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# Avoid ffmpeg vomitting on the logs.
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kwargs['stderr'] = sp.DEVNULL
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kwargs['stdout'] = sp.DEVNULL
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_old_call(*args, **kwargs)
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sp.call = _call_nostderr
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# Preallocating the pool of processes.
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pool = ProcessPoolExecutor(4)
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pool.__enter__()
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def interrupt():
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global INTERRUPTING
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INTERRUPTING = True
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def make_waveform(*args, **kwargs):
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# Further remove some warnings.
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be = time.time()
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with warnings.catch_warnings():
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warnings.simplefilter('ignore')
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out = gr.make_waveform(*args, **kwargs)
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print("Make a video took", time.time() - be)
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return out
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def load_model(version='melody'):
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global MODEL
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print("Loading model", version)
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if MODEL is None or MODEL.name != version:
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MODEL = MusicGen.get_pretrained(version)
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def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
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MODEL.set_generation_params(duration=duration, **gen_kwargs)
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print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
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be = time.time()
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processed_melodies = []
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target_sr = 32000
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target_ac = 1
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for melody in melodies:
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if melody is None:
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processed_melodies.append(None)
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else:
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sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
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if melody.dim() == 1:
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melody = melody[None]
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melody = melody[..., :int(sr * duration)]
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melody = convert_audio(melody, sr, target_sr, target_ac)
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processed_melodies.append(melody)
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if any(m is not None for m in processed_melodies):
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outputs = MODEL.generate_with_chroma(
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descriptions=texts,
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melody_wavs=processed_melodies,
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melody_sample_rate=target_sr,
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progress=progress,
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)
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else:
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outputs = MODEL.generate(texts, progress=progress)
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outputs = outputs.detach().cpu().float()
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out_files = []
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for output in outputs:
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with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
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audio_write(
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file.name, output, MODEL.sample_rate, strategy="loudness",
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loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
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out_files.append(pool.submit(make_waveform, file.name))
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res = [out_file.result() for out_file in out_files]
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print("batch finished", len(texts), time.time() - be)
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return res
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def predict_batched(texts, melodies):
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max_text_length = 512
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texts = [text[:max_text_length] for text in texts]
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load_model('melody')
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res = _do_predictions(texts, melodies, BATCHED_DURATION)
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return [res]
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def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
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global INTERRUPTING
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INTERRUPTING = False
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if temperature < 0:
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raise gr.Error("Temperature must be >= 0.")
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if topk < 0:
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raise gr.Error("Topk must be non-negative.")
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if topp < 0:
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raise gr.Error("Topp must be non-negative.")
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topk = int(topk)
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load_model(model)
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def _progress(generated, to_generate):
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progress((generated, to_generate))
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if INTERRUPTING:
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raise gr.Error("Interrupted.")
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MODEL.set_custom_progress_callback(_progress)
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outs = _do_predictions(
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[text], [melody], duration, progress=True,
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top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
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return outs[0]
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def ui_full(launch_kwargs):
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as interface:
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gr.Markdown(
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"""
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<div align='center' ><font size='60'>音乐生成</font></div>
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"""
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)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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text = gr.Text(label="输入文本", interactive=True)
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melody = gr.Audio(source="upload", type="numpy", label="旋律(可选)", interactive=True)
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with gr.Row():
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submit = gr.Button("Submit")
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# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
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_ = gr.Button("中断").click(fn=interrupt, queue=False)
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"], label="模型", value="melody", interactive=True)
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with gr.Row():
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duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
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with gr.Row():
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topk = gr.Number(label="Top-k", value=250, interactive=True)
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topp = gr.Number(label="Top-p", value=0, interactive=True)
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temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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with gr.Column():
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output = gr.Video(label="生成的音乐")
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submit.click(predict_full, inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef], outputs=[output])
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gr.Examples(
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fn=predict_full,
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examples=[
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[
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"An 80s driving pop song with heavy drums and synth pads in the background",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"A cheerful country song with acoustic guitars",
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"./assets/bolero_ravel.mp3",
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"melody"
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],
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[
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"90s rock song with electric guitar and heavy drums",
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None,
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"medium"
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],
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[
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
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"./assets/bach.mp3",
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"melody"
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],
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[
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"lofi slow bpm electro chill with organic samples",
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None,
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"medium",
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],
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],
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inputs=[text, melody, model],
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outputs=[output],
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label="例子"
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)
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interface.queue().launch(**launch_kwargs)
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def ui_batched(launch_kwargs):
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown(
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"""
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<div align='center' ><font size='60'>音乐生成</font></div>
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"""
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)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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text = gr.Text(label="Describe your music", lines=2, interactive=True)
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melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
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with gr.Row():
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submit = gr.Button("Generate")
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict_batched, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
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gr.Examples(
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fn=predict_batched,
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examples=[
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[
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"An 80s driving pop song with heavy drums and synth pads in the background",
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"./assets/bach.mp3",
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],
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[
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"A cheerful country song with acoustic guitars",
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"./assets/bolero_ravel.mp3",
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],
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[
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"90s rock song with electric guitar and heavy drums",
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None,
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],
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[
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"a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
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"./assets/bach.mp3",
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],
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[
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"lofi slow bpm electro chill with organic samples",
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None,
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],
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],
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inputs=[text, melody],
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outputs=[output],
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label="例子"
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)
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demo.queue(max_size=8 * 4).launch(**launch_kwargs)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--listen',
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type=str,
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default='0.0.0.0' if 'SPACE_ID' in os.environ else '0.0.0.0',
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help='IP to listen on for connections to Gradio',
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)
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parser.add_argument(
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'--username', type=str, default='', help='Username for authentication'
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)
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parser.add_argument(
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'--password', type=str, default='', help='Password for authentication'
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)
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parser.add_argument(
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'--server_port',
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type=int,
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default=0,
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help='Port to run the server listener on',
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)
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parser.add_argument(
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'--inbrowser', action='store_true', help='Open in browser'
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)
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parser.add_argument(
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'--share', action='store_true', help='Share the gradio UI'
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)
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args = parser.parse_args()
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launch_kwargs = {}
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launch_kwargs['server_name'] = args.listen
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if args.username and args.password:
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launch_kwargs['auth'] = (args.username, args.password)
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if args.server_port:
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launch_kwargs['server_port'] = args.server_port
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if args.inbrowser:
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launch_kwargs['inbrowser'] = args.inbrowser
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if args.share:
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launch_kwargs['share'] = args.share
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# Show the interface
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if IS_BATCHED:
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ui_batched(launch_kwargs)
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else:
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ui_full(launch_kwargs)
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