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refact project

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This commit is contained in:
chuxij
2025-04-25 15:45:24 +00:00
parent f91ce4867f
commit db32300e75
20 changed files with 2031 additions and 4027 deletions
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ui/auth.py
-3
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def same_auth(username, password):
return username == "timedomain_text2music_team" and password == "TimeDomain_ACEFlow_DEMO"
ui/components.py
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import gradio as gr
from pathlib import Path
TAG_PLACEHOLDER = "jazz, deep, bass-driven, conscious, acid jazz, electronic, 90 bpm"
LYRIC_PLACEHOLDER = """[verse]
夜风轻轻吹过脸颊，
城市霓虹闪烁如画。
脚步追随节奏的牵挂，
思绪飘荡，梦的火花。
[chorus]
夜色中，心如电光闪耀，
节奏深，带我飞向云霄。
音符跳跃，击退烦忧来到，
在这梦里，我们自由起舞不老。
[verse]
低音如潮冷冷拉扯，
心跳同步，扣人心弦如歌。
键盘奏出泪隐藏的选择，
鼓点敲醒藏于夜里的角色。
[chorus]
夜色中，心如电光闪耀，
节奏深，带我飞向云霄。
音符跳跃，击退烦忧来到，
在这梦里，我们自由起舞不老。
[bridge]
每一滴汗珠画出轨迹，
肢体语言无声诉心意。
触碰世界不可言语的秘语，
在旋律中找回真实的自己。
[chorus]
夜色中，心如电光闪耀，
节奏深，带我飞向云霄。
音符跳跃，击退烦忧来到，
在这梦里，我们自由起舞不老。
"""
def create_output_ui():
# For many consumer-grade GPU devices, only one batch can be run
output_audio1 = gr.Audio(type="filepath", label="Generated Audio 1")
# output_audio2 = gr.Audio(type="filepath", label="Generated Audio 2")
with gr.Accordion("Input Parameters", open=False):
input_params_json = gr.JSON(label="Input Parameters")
# outputs = [output_audio1, output_audio2]
outputs = [output_audio1]
return outputs, input_params_json
def dump_func(*args):
print(args)
return []
def create_text2music_ui(
gr,
text2music_process_func,
sample_bnt=None,
sample_data_func=None,
):
with gr.Row():
with gr.Column():
audio_duration = gr.Slider(-1, 300.0, step=0.1, value=60, label="Audio Duration", interactive=True, info="Duration of the audio in seconds. -1 means random duration (30 ~ 300).")
prompt = gr.Textbox(lines=2, label="Tags", max_lines=4, placeholder=TAG_PLACEHOLDER)
lyrics = gr.Textbox(lines=9, label="Lyrics", max_lines=13, placeholder=LYRIC_PLACEHOLDER)
with gr.Accordion("Basic Settings", open=False):
infer_step = gr.Slider(minimum=1, maximum=1000, step=1, value=60, label="Infer Steps", interactive=True)
guidance_scale = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=15.0, label="Guidance Scale", interactive=True)
scheduler_type = gr.Radio(["euler", "heun"], value="euler", label="Scheduler Type", elem_id="scheduler_type", )
manual_seeds = gr.Textbox(label="manual seeds (default None)", placeholder="1,2,3,4", value=None)
with gr.Accordion("Advanced Settings", open=False):
cfg_type = gr.Radio(["cfg", "apg", "cfg_star"], value="apg", label="CFG Type", elem_id="cfg_type", )
use_erg_tag = gr.Checkbox(label="use ERG for tag", value=True, )
use_erg_lyric = gr.Checkbox(label="use ERG for lyric", value=True, )
use_erg_diffusion = gr.Checkbox(label="use ERG for diffusion", value=True, )
omega_scale = gr.Slider(minimum=-100.0, maximum=100.0, step=0.1, value=10.0, label="Granularity Scale", interactive=True, )
guidance_interval = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Guidance Interval", interactive=True, )
guidance_interval_decay = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.0, label="Guidance Interval Decay", interactive=True, )
min_guidance_scale = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=3.0, label="Min Guidance Scale", interactive=True, )
oss_steps = gr.Textbox(label="OSS Steps", placeholder="16, 29, 52, 96, 129, 158, 172, 183, 189, 200", value=None)
text2music_bnt = gr.Button(variant="primary")
with gr.Column():
outputs, input_params_json = create_output_ui()
# sample_bnt.click(
# sample_data_func,
# inputs=[dataset_example_idx, dataset_source, source],
# outputs=[dataset_example_idx, prompt, lyrics, target_audio, audio_duration, input_params_json],
# )
text2music_bnt.click(
fn=text2music_process_func,
inputs=[
audio_duration,
prompt,
lyrics,
infer_step,
guidance_scale,
scheduler_type,
cfg_type,
omega_scale,
manual_seeds,
guidance_interval,
guidance_interval_decay,
min_guidance_scale,
use_erg_tag,
use_erg_lyric,
use_erg_diffusion,
oss_steps,
], outputs=outputs + [input_params_json]
)
def create_main_demo_ui(
text2music_process_func=dump_func,
sample_data_func=dump_func,
):
with gr.Blocks(
title="FusicModel 1.0 DEMO",
) as demo:
gr.Markdown(
"""
<h1 style="text-align: center;">FusicModel 1.0 DEMO</h1>
"""
)
sample_bnt = None
with gr.Tab("text2music"):
create_text2music_ui(
gr=gr,
text2music_process_func=text2music_process_func,
sample_bnt=sample_bnt,
sample_data_func=sample_data_func,
)
return demo
if __name__ == "__main__":
demo = create_main_demo_ui()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
)
ui/text2music_large_lyric_components_v3.py
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@@ -1,323 +0,0 @@
import gradio as gr
from pathlib import Path
import json
from collections import OrderedDict, Counter
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from language_segmentation import LangSegment
MAX_GENERATE_LEN = 60
SUPPORT_LANGUAGES = [
"af", "sq", "am", "ar", "an", "hy", "az", "ba", "eu", "be", "bn", "bs", "bg", "my", "ca", "zh", "cs", "da", "nl", "en", "eo", "et", "fi", "fr", "gd", "ka", "de", "el", "gn", "gu", "hi", "hu", "io", "id", "ia", "it", "ja", "kk", "km", "ko", "ku", "la", "lt", "lb", "mk", "mt", "nb", "no", "or", "fa", "pl", "pt", "ro", "ru", "sa", "sr", "sd", "sk", "sl", "es", "sw", "sv", "tl", "ta", "tt", "th", "tr", "tk", "uk", "vi", "cy", "is", "ga", "gl", "se", "yue"
]
langseg = LangSegment()
langseg.setfilters([
'af', 'am', 'an', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'dz', 'el',
'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fo', 'fr', 'ga', 'gl', 'gu', 'he', 'hi', 'hr', 'ht', 'hu', 'hy',
'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lb', 'lo', 'lt', 'lv', 'mg',
'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'nb', 'ne', 'nl', 'nn', 'no', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'qu',
'ro', 'ru', 'rw', 'se', 'si', 'sk', 'sl', 'sq', 'sr', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'ug', 'uk',
'ur', 'vi', 'vo', 'wa', 'xh', 'zh', 'zu'
])
keyscale_idx_mapping = OrderedDict({
"C major": 1,
"C# major": 2,
"D major": 3,
"Eb major": 4,
"E major": 5,
"F major": 6,
"F# major": 7,
"G major": 8,
"Ab major": 9,
"A major": 10,
"Bb major": 11,
"B major": 12,
"A minor": 13,
"Bb minor": 14,
"B minor": 15,
"C minor": 16,
"C# minor": 17,
"D minor": 18,
"Eb minor": 19,
"E minor": 20,
"F minor": 21,
"F# minor": 22,
"G minor": 23,
"Ab minor": 24
})
def get_checkpoint_paths(checkpoint_path):
# 获取指定目录中的所有checkpoint文件路径
directory = Path(checkpoint_path).parent
checkpoints = [str(p) for p in directory.glob("*.ckpt")]
print(checkpoints)
return checkpoints
def create_list_checkpoint_path_ui(checkpoint_path):
with gr.Column():
gr.Markdown("Checkpoint Selection")
with gr.Group():
with gr.Row(equal_height=True):
with gr.Column(scale=9):
selected_checkpoint = gr.Dropdown(
choices=get_checkpoint_paths(checkpoint_path),
label="Select Model",
interactive=True,
value=checkpoint_path,
)
with gr.Column(scale=1):
refresh_button = gr.Button("Refresh Checkpoints", elem_id="refresh_button", variant="primary")
refresh_button.click(
fn=lambda: gr.update(choices=get_checkpoint_paths(checkpoint_path)),
inputs=None,
outputs=[selected_checkpoint]
)
return selected_checkpoint
def create_keyscale_bpm_time_signature_input_ui(options=["auto", "manual"]):
gr.Markdown("### Time and Keyscale Control")
with gr.Group():
results = [
["keyscale", 0],
["bpm", 0],
["timesignature", 0],
["is_music_start", 0],
["is_music_end", 0],
]
keyscale_bpm_time_signature_input = gr.List(visible=False, elem_id="keyscale_bpm_time_signature_input", value=results)
audio_duration = gr.Slider(10, 600, step=1, value=MAX_GENERATE_LEN, label="Audio Duration", interactive=True)
with gr.Row():
is_music_start_input = gr.Radio(["auto", "start", "not_start"], value="auto", label="Is Music Start", elem_id="is_music_start_input")
is_music_end_input = gr.Radio(["auto", "end", "not_end"], value="auto", label="Is Music End", elem_id="is_music_end_input")
def when_is_music_start_input_change(
is_music_start_input,
):
nonlocal results
if is_music_start_input == "auto":
is_music_start = 0
elif is_music_start_input == "start":
is_music_start = 1
else:
is_music_start = 2
results[3][1] = is_music_start
return gr.update(elem_id="keyscale_bpm_time_signature_input", value=results)
is_music_start_input.change(
when_is_music_start_input_change,
inputs=[is_music_start_input],
outputs=[keyscale_bpm_time_signature_input]
)
def when_is_music_end_input_change(
is_music_end_input,
):
nonlocal results
if is_music_end_input == "auto":
is_music_end = 0
elif is_music_end_input == "end":
is_music_end = 1
else:
is_music_end = 2
results[4][1] = is_music_end
return gr.update(elem_id="keyscale_bpm_time_signature_input", value=results)
is_music_end_input.change(
when_is_music_end_input_change,
inputs=[is_music_end_input],
outputs=[keyscale_bpm_time_signature_input]
)
with gr.Row():
keyscale_control = gr.Radio(options, value="auto", label="Keyscale", elem_id="keyscale_control")
bpm_control = gr.Radio(options, value="auto", label="BPM", elem_id="bpm_control")
time_signature_control = gr.Radio(options, value="auto", label="Time Signature", elem_id="time_signature_control")
keyscale_input = gr.Dropdown(list(keyscale_idx_mapping.keys()), label="Keyscale", info="the keyscale of the music", visible=False, elem_id="keyscale_input")
def when_keyscale_change(
keyscale_input,
keyscale_control,
):
nonlocal results
keyscale = keyscale_input
if keyscale_control == "auto":
keyscale = 0
results[0][1] = keyscale
return [gr.update(elem_id="keyscale_bpm_time_signature_input", value=results), gr.update(elem_id="keyscale_input", visible=(keyscale_control == "manual"))]
keyscale_input.change(
when_keyscale_change,
inputs=[keyscale_input, keyscale_control],
outputs=[keyscale_bpm_time_signature_input, keyscale_input]
)
keyscale_control.change(
fn=when_keyscale_change,
inputs=[keyscale_input, keyscale_control],
outputs=[keyscale_bpm_time_signature_input, keyscale_input]
)
bpm_input = gr.Slider(30, 200, step=1, value=120, label="BPM", info="the beats per minute of the music", visible=False, interactive=True, elem_id="bpm_input")
def when_bmp_change(
bpm_input,
bpm_control,
):
nonlocal results
bpm = bpm_input
if bpm_control == "auto":
bpm = 0
results[1][1] = bpm
updates = [gr.update(elem_id="keyscale_bpm_time_signature_input", value=results), gr.update(elem_id="bpm_input", visible=(bpm_control == "manual"))]
return updates
bpm_control.change(
fn=when_bmp_change,
inputs=[bpm_input, bpm_control],
outputs=[keyscale_bpm_time_signature_input, bpm_input]
)
bpm_input.change(
when_bmp_change,
inputs=[bpm_input, bpm_control],
outputs=[keyscale_bpm_time_signature_input, bpm_input]
)
time_signature_input = gr.Slider(1, 12, step=1, value=4, label="Time Signature", info="the time signature of the music", visible=False, interactive=True, elem_id="time_signature_input")
def when_time_signature_change(
time_signature_input,
time_signature_control,
):
nonlocal results
time_signature = time_signature_input
if time_signature_control == "auto":
time_signature = 0
results[2][1] = time_signature
return [gr.update(elem_id="keyscale_bpm_time_signature_input", value=results), gr.update(elem_id="time_signature_input", visible=(time_signature_control == "manual"))]
time_signature_input.change(
when_time_signature_change,
inputs=[time_signature_input, time_signature_control],
outputs=[keyscale_bpm_time_signature_input, time_signature_input]
)
time_signature_control.change(
fn=when_time_signature_change,
inputs=[time_signature_input, time_signature_control],
outputs=[keyscale_bpm_time_signature_input, time_signature_input]
)
return [audio_duration, keyscale_bpm_time_signature_input]
def detect_language(lyrics: str) -> list:
lyrics = lyrics.strip()
if not lyrics:
return gr.update(value="en")
langs = langseg.getTexts(lyrics)
lang_counter = Counter()
for lang in langs:
lang_counter[lang["lang"]] += len(lang["text"])
lang = lang_counter.most_common(1)[0][0]
return lang
def create_output_ui():
target_audio = gr.Audio(type="filepath", label="Target Audio")
output_audio1 = gr.Audio(type="filepath", label="Generated Audio 1")
output_audio2 = gr.Audio(type="filepath", label="Generated Audio 2")
input_params_json = gr.JSON(label="Input Parameters")
outputs = [output_audio1, output_audio2]
return outputs, target_audio, input_params_json
def dump_func(*args):
print(args)
return []
def create_main_demo_ui(
checkpoint_path="checkpoints/aceflow3_0311/1d_epoch=16-step=140k.ckpt",
text2music_process_func=dump_func,
sample_data_func=dump_func,
):
with gr.Blocks(
title="AceFlow 3.0 DEMO (3.5B)",
) as demo:
gr.Markdown(
"""
<h1 style="text-align: center;">AceFlow 3.0 DEMO</h1>
"""
)
selected_checkpoint = create_list_checkpoint_path_ui(checkpoint_path)
gr.Markdown("Dataset Filter")
with gr.Group():
with gr.Row(equal_height=True):
language = gr.Dropdown(["en", "zh"], label="Language", value="en", elem_id="language")
dataset_example_idx = gr.Number(
value=-1,
label="Dataset Example Index",
interactive=True
)
sample_bnt = gr.Button(value="Sample Data", elem_id="sample_bnt", variant="primary")
with gr.Row():
with gr.Column():
audio_duration = gr.Slider(10, 600, step=1, value=MAX_GENERATE_LEN, label="Audio Duration", interactive=True)
prompt = gr.Textbox(lines=2, label="Tags", max_lines=4)
lyrics = gr.Textbox(lines=9, label="Lyrics", max_lines=9)
scheduler_type = gr.Radio(["euler", "heun"], value="euler", label="Scheduler Type", elem_id="scheduler_type")
cfg_type = gr.Radio(["cfg", "apg"], value="apg", label="CFG Type", elem_id="cfg_type")
infer_step = gr.Slider(minimum=1, maximum=1000, step=1, value=60, label="Infer Steps", interactive=True)
guidance_scale = gr.Slider(minimum=0.0, maximum=200.0, step=0.1, value=15.0, label="Guidance Scale", interactive=True)
omega_scale = gr.Slider(minimum=-100.0, maximum=100.0, step=0.1, value=10.0, label="Granularity Scale", interactive=True)
manual_seeds = gr.Textbox(label="manual seeds (default None)", placeholder="1,2,3,4", value=None)
text2music_bnt = gr.Button(variant="primary")
with gr.Column():
outputs, target_audio, input_params_json = create_output_ui()
sample_bnt.click(
sample_data_func,
inputs=[dataset_example_idx, audio_duration],
outputs=[target_audio, prompt, lyrics, input_params_json],
)
text2music_bnt.click(
fn=text2music_process_func,
inputs=[
audio_duration,
prompt,
lyrics,
input_params_json,
selected_checkpoint,
scheduler_type,
cfg_type,
infer_step,
guidance_scale,
omega_scale,
manual_seeds,
], outputs=outputs + [input_params_json]
)
return demo
if __name__ == "__main__":
demo = create_main_demo_ui()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
)