Spaces:
Running
on
Zero
Running
on
Zero
Zero GPU
Browse files- app.py +86 -103
- diffrhythm/infer/infer.py +4 -5
- diffrhythm/infer/infer_utils.py +1 -2
app.py
CHANGED
@@ -9,6 +9,7 @@ from einops import rearrange
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import argparse
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import json
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import os
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from tqdm import tqdm
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import random
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import numpy as np
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@@ -22,13 +23,15 @@ from diffrhythm.infer.infer_utils import (
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)
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from diffrhythm.infer.infer import inference
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-
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cfm, tokenizer, muq, vae = prepare_model(device)
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cfm = torch.compile(cfm)
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-
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sway_sampling_coef = -1 if
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lrc_prompt, start_time = get_lrc_token(lrc, tokenizer, device)
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style_prompt = get_style_prompt(muq, ref_audio_path)
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negative_style_prompt = get_negative_style_prompt(device)
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@@ -48,7 +51,7 @@ def infer_music(lrc, ref_audio_path, steps, sway_sampling_coef_bool, max_frames=
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def R1_infer1(theme, tags_gen, language):
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try:
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client = OpenAI(api_key="
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llm_prompt = """
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请围绕"{theme}"主题生成一首符合"{tags}"风格的完整歌词。生成的{language}语言的歌词。
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@@ -82,7 +85,7 @@ def R1_infer1(theme, tags_gen, language):
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def R1_infer2(tags_lyrics, lyrics_input):
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client = OpenAI(api_key="
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llm_prompt = """
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{lyrics_input}这是一首歌的歌词,每一行是一句歌词,{tags_lyrics}是我希望这首歌的风格,我现在想要给这首歌的每一句歌词打时间戳得到LRC,我希望时间戳分配应根据歌曲的标签、歌词的情感、节奏来合理推测,而非机械地按照歌词长度分配。第一句歌词的时间戳应考虑前奏长度,避免歌词从 `[00:00.00]` 直接开始。严格按照 LRC 格式输出歌词,每行格式为 `[mm:ss.xx]歌词内容`。最后的结果只输出LRC,不需要其他的解释。
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@@ -110,10 +113,32 @@ css = """
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white-space: pre-wrap; /* 保留换行 */
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line-height: 1.5; /* 行高优化 */
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.Markdown("
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with gr.Tabs() as tabs:
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@@ -144,31 +169,26 @@ with gr.Blocks(css=css) as demo:
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placeholder="Input the full lyrics",
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lines=12,
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max_lines=50,
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elem_classes="lyrics-scroll-box"
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)
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audio_prompt = gr.Audio(label="Audio Prompt", type="filepath")
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with gr.Column():
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steps = gr.Slider(
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minimum=10,
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-
maximum=
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value=32,
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step=1,
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label="Diffusion Steps",
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interactive=True,
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elem_id="step_slider"
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)
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sway_sampling_coef_bool = gr.Radio(
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choices=[("False", False), ("True", True)],
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label="Use sway_sampling_coef",
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value=False,
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interactive=True,
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elem_classes="horizontal-radio"
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)
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lyrics_btn = gr.Button("Submit", variant="primary")
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audio_output = gr.Audio(label="Audio Result", type="filepath", elem_id="audio_output")
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-
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gr.Examples(
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examples=[
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["./gift_of_the_world.wav"],
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@@ -177,59 +197,21 @@ with gr.Blocks(css=css) as demo:
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],
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inputs=[audio_prompt],
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label="Audio Examples",
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examples_per_page=3
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)
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gr.Examples(
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examples=[
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["""[00:
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[00:13.20]Your shadow dances on the dashboard shrine
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[00:16.85]Neon ghosts in gasoline rain
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[00:20.40]I hear your laughter down the midnight train
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[00:24.15]Static whispers through frayed wires
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[00:27.65]Guitar strings hum our cathedral choirs
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[00:31.30]Flicker screens show reruns of June
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[00:34.90]I'm drowning in this mercury lagoon
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[00:38.55]Electric veins pulse through concrete skies
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[00:42.10]Your name echoes in the hollow where my heartbeat lies
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[00:45.75]We're satellites trapped in parallel light
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[00:49.25]Burning through the atmosphere of endless night
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[01:00.00]Dusty vinyl spins reverse
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[01:03.45]Our polaroid timeline bleeds through the verse
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[01:07.10]Telescope aimed at dead stars
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[01:10.65]Still tracing constellations through prison bars
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[01:14.30]Electric veins pulse through concrete skies
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[01:17.85]Your name echoes in the hollow where my heartbeat lies
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[01:21.50]We're satellites trapped in parallel light
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[01:25.05]Burning through the atmosphere of endless night
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[02:10.00]Clockwork gears grind moonbeams to rust
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[02:13.50]Our fingerprint smudged by interstellar dust
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[02:17.15]Velvet thunder rolls through my veins
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[02:20.70]Chasing phantom trains through solar plane
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[02:24.35]Electric veins pulse through concrete skies
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[02:27.90]Your name echoes in the hollow where my heartbeat lies"""],
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["""[00:05.00]Stardust whispers in your eyes
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[00:09.30]Moonlight paints our silhouettes
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[00:13.75]Tides bring secrets from the deep
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[00:18.20]Where forever's breath is kept
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[00:22.90]We dance through constellations' maze
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[00:27.15]Footprints melt in cosmic waves
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[00:31.65]Horizons hum our silent vow
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[00:36.10]Time unravels here and now
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[00:40.85]Eternal embers in the night oh oh oh
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[00:45.25]Healing scars with liquid light
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[00:49.70]Galaxies write our refrain
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[00:54.15]Love reborn in endless rain
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[01:15.30]Paper boats of memories
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[01:19.75]Float through veins of ancient trees
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[01:24.20]Your laughter spins aurora threads
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[01:28.65]Weaving dawn through featherbed"""]
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],
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inputs=[lrc],
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label="Lrc Examples",
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examples_per_page=2
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)
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-
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# page 2
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with gr.Tab("LLM Generate LRC", id=1):
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with gr.Row():
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gr.Markdown("### Method 1: Generate from Theme")
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theme = gr.Textbox(label="theme", placeholder="Enter song theme, e.g. Love and Heartbreak")
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tags_gen = gr.Textbox(label="tags", placeholder="Example: male pop confidence healing")
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language = gr.Dropdown(["zh", "en"], label="language", value="en")
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gen_from_theme_btn = gr.Button("Generate LRC (From Theme)", variant="primary")
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with gr.Group(visible=True):
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gr.Markdown("### Method 2: Add Timestamps to Lyrics")
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lyrics_input = gr.Textbox(
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label="Raw Lyrics (without timestamps)",
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placeholder="Enter plain lyrics (without timestamps), e.g.:\nYesterday\nAll my troubles...",
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lines=
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max_lines=50,
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elem_classes="lyrics-scroll-box"
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)
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gen_from_lyrics_btn = gr.Button("Generate LRC (From Lyrics)", variant="primary")
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with gr.Column():
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lrc_output = gr.Textbox(
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label="Generated LRC Lyrics",
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placeholder="Timed lyrics will appear here",
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lines=
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elem_classes="lrc-output",
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show_copy_button=True
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)
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-
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# Examples section
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gr.Examples(
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examples=[
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[
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"Love and Heartbreak",
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"female vocal emotional piano pop",
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"en"
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],
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[
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"Heroic Epic",
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"male choir orchestral powerful",
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"zh"
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]
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],
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inputs=[theme, tags_gen, language],
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label="Examples: Generate from Theme"
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)
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gr.Examples(
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examples=[
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[
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"acoustic folk happy",
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"""I'm sitting here in the boring room
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It's just another rainy Sunday afternoon"""
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],
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[
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"electronic dance energetic",
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"""We're living in a material world
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And I am a material girl"""
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]
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],
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inputs=[tags_lyrics, lyrics_input],
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label="Examples: Generate from Lyrics"
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)
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# Bind functions
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gen_from_theme_btn.click(
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lyrics_btn.click(
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fn=infer_music,
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inputs=[lrc, audio_prompt, steps
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outputs=audio_output
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)
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demo.queue().launch(show_api=False, show_error=True)
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import argparse
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import json
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import os
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import spaces
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from tqdm import tqdm
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import random
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import numpy as np
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)
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from diffrhythm.infer.infer import inference
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+
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device='cuda'
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cfm, tokenizer, muq, vae = prepare_model(device)
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cfm = torch.compile(cfm)
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@spaces.GPU
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def infer_music(lrc, ref_audio_path, steps, max_frames=2048, device='cuda'):
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sway_sampling_coef = -1 if steps < 32 else None
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lrc_prompt, start_time = get_lrc_token(lrc, tokenizer, device)
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style_prompt = get_style_prompt(muq, ref_audio_path)
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negative_style_prompt = get_negative_style_prompt(device)
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def R1_infer1(theme, tags_gen, language):
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try:
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client = OpenAI(api_key="3581722f-9abc-49cf-9792-fa962cad9c4f", base_url = "https://ark.cn-beijing.volces.com/api/v3")
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llm_prompt = """
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请围绕"{theme}"主题生成一首符合"{tags}"风格的完整歌词。生成的{language}语言的歌词。
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def R1_infer2(tags_lyrics, lyrics_input):
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client = OpenAI(api_key="3581722f-9abc-49cf-9792-fa962cad9c4f", base_url = "https://ark.cn-beijing.volces.com/api/v3")
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llm_prompt = """
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{lyrics_input}这是一首歌的歌词,每一行是一句歌词,{tags_lyrics}是我希望这首歌的风格,我现在想要给这首歌的每一句歌词打时间戳得到LRC,我希望时间戳分配应根据歌曲的标签、歌词的情感、节奏来合理推测,而非机械地按照歌词长度分配。第一句歌词的时间戳应考虑前奏长度,避免歌词从 `[00:00.00]` 直接开始。严格按照 LRC 格式输出歌词,每行格式为 `[mm:ss.xx]歌词内容`。最后的结果只输出LRC,不需要其他的解释。
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white-space: pre-wrap; /* 保留换行 */
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line-height: 1.5; /* 行高优化 */
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}
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+
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.gr-examples {
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background: transparent !important;
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border: 1px solid #e0e0e0 !important;
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border-radius: 8px;
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margin: 1rem 0 !important;
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padding: 1rem !important;
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.Markdown("<h1 style='text-align: center'>DiffRhythm(谛韵)</h1>")
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gr.HTML("""
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<div style="display:flex; justify-content: center; column-gap:4px;">
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<a href="https://github.com/ASLP-lab/DiffRhythm">
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<img src='https://img.shields.io/badge/Arxiv-Paper-blue'>
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</a>
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<a href="https://github.com/ASLP-lab/DiffRhythm">
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<img src='https://img.shields.io/badge/GitHub-Repo-green'>
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</a>
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<a href="https://aslp-lab.github.io/DiffRhythm.github.io/">
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<img src='https://img.shields.io/badge/Project-Page-brown'>
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</a>
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</div>
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""")
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with gr.Tabs() as tabs:
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placeholder="Input the full lyrics",
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lines=12,
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max_lines=50,
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elem_classes="lyrics-scroll-box",
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value="""[00:05.00]Stardust whispers in your eyes\n[00:09.30]Moonlight paints our silhouettes\n[00:13.75]Tides bring secrets from the deep\n[00:18.20]Where forever's breath is kept\n[00:22.90]We dance through constellations' maze\n[00:27.15]Footprints melt in cosmic waves\n[00:31.65]Horizons hum our silent vow\n[00:36.10]Time unravels here and now\n[00:40.85]Eternal embers in the night oh oh oh\n[00:45.25]Healing scars with liquid light\n[00:49.70]Galaxies write our refrain\n[00:54.15]Love reborn in endless rain\n[01:00.00]Interlude\n[01:15.30]Paper boats of memories\n[01:19.75]Float through veins of ancient trees\n[01:24.20]Your laughter spins aurora threads\n[01:28.65]Weaving dawn through featherbed"""
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)
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audio_prompt = gr.Audio(label="Audio Prompt", type="filepath", value="./gift_of_the_world.wav")
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with gr.Column():
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steps = gr.Slider(
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minimum=10,
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maximum=100,
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value=32,
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step=1,
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label="Diffusion Steps",
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interactive=True,
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elem_id="step_slider"
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)
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lyrics_btn = gr.Button("Submit", variant="primary")
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audio_output = gr.Audio(label="Audio Result", type="filepath", elem_id="audio_output")
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+
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gr.Examples(
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examples=[
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["./gift_of_the_world.wav"],
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],
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inputs=[audio_prompt],
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label="Audio Examples",
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examples_per_page=3,
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elem_id="audio-examples-container"
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)
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gr.Examples(
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examples=[
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["""[00:05.00]Stardust whispers in your eyes\n[00:09.30]Moonlight paints our silhouettes\n[00:13.75]Tides bring secrets from the deep\n[00:18.20]Where forever's breath is kept\n[00:22.90]We dance through constellations' maze\n[00:27.15]Footprints melt in cosmic waves\n[00:31.65]Horizons hum our silent vow\n[00:36.10]Time unravels here and now\n[00:40.85]Eternal embers in the night oh oh oh\n[00:45.25]Healing scars with liquid light\n[00:49.70]Galaxies write our refrain\n[00:54.15]Love reborn in endless rain\n[01:00.00]Interlude\n[01:15.30]Paper boats of memories\n[01:19.75]Float through veins of ancient trees\n[01:24.20]Your laughter spins aurora threads\n[01:28.65]Weaving dawn through featherbed"""],
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+
["""[00:10.00]Moonlight spills through broken blinds\n[00:13.20]Your shadow dances on the dashboard shrine\n[00:16.85]Neon ghosts in gasoline rain\n[00:20.40]I hear your laughter down the midnight train\n[00:24.15]Static whispers through frayed wires\n[00:27.65]Guitar strings hum our cathedral choirs\n[00:31.30]Flicker screens show reruns of June\n[00:34.90]I'm drowning in this mercury lagoon\n[00:38.55]Electric veins pulse through concrete skies\n[00:42.10]Your name echoes in the hollow where my heartbeat lies\n[00:45.75]We're satellites trapped in parallel light\n[00:49.25]Burning through the atmosphere of endless night\n[01:00.00]Dusty vinyl spins reverse\n[01:03.45]Our polaroid timeline bleeds through the verse\n[01:07.10]Telescope aimed at dead stars\n[01:10.65]Still tracing constellations through prison bars\n[01:14.30]Electric veins pulse through concrete skies\n[01:17.85]Your name echoes in the hollow where my heartbeat lies\n[01:21.50]We're satellites trapped in parallel light\n[01:25.05]Burning through the atmosphere of endless night\n[02:10.00]Clockwork gears grind moonbeams to rust\n[02:13.50]Our fingerprint smudged by interstellar dust\n[02:17.15]Velvet thunder rolls through my veins\n[02:20.70]Chasing phantom trains through solar plane\n[02:24.35]Electric veins pulse through concrete skies\n[02:27.90]Your name echoes in the hollow where my heartbeat lies"""]
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],
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+
inputs=[lrc],
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label="Lrc Examples",
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+
examples_per_page=2,
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+
elem_id="lrc-examples-container",
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)
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+
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# page 2
|
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with gr.Tab("LLM Generate LRC", id=1):
|
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with gr.Row():
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gr.Markdown("### Method 1: Generate from Theme")
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theme = gr.Textbox(label="theme", placeholder="Enter song theme, e.g. Love and Heartbreak")
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tags_gen = gr.Textbox(label="tags", placeholder="Example: male pop confidence healing")
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+
# language = gr.Dropdown(["zh", "en"], label="language", value="en")
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+
language = gr.Radio(["zh", "en"], label="Language", value="en")
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gen_from_theme_btn = gr.Button("Generate LRC (From Theme)", variant="primary")
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+
|
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+
gr.Examples(
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+
examples=[
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+
[
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+
"Love and Heartbreak",
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+
"vocal emotional piano pop",
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+
"en"
|
236 |
+
],
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+
[
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238 |
+
"Heroic Epic",
|
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+
"choir orchestral powerful",
|
240 |
+
"zh"
|
241 |
+
]
|
242 |
+
],
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+
inputs=[theme, tags_gen, language],
|
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+
label="Examples: Generate from Theme"
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+
)
|
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|
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with gr.Group(visible=True):
|
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gr.Markdown("### Method 2: Add Timestamps to Lyrics")
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|
250 |
lyrics_input = gr.Textbox(
|
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label="Raw Lyrics (without timestamps)",
|
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placeholder="Enter plain lyrics (without timestamps), e.g.:\nYesterday\nAll my troubles...",
|
253 |
+
lines=10,
|
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max_lines=50,
|
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elem_classes="lyrics-scroll-box"
|
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)
|
257 |
+
|
258 |
gen_from_lyrics_btn = gr.Button("Generate LRC (From Lyrics)", variant="primary")
|
259 |
|
260 |
+
gr.Examples(
|
261 |
+
examples=[
|
262 |
+
[
|
263 |
+
"acoustic folk happy",
|
264 |
+
"""I'm sitting here in the boring room\nIt's just another rainy Sunday afternoon"""
|
265 |
+
],
|
266 |
+
[
|
267 |
+
"electronic dance energetic",
|
268 |
+
"""We're living in a material world\nAnd I am a material girl"""
|
269 |
+
]
|
270 |
+
],
|
271 |
+
inputs=[tags_lyrics, lyrics_input],
|
272 |
+
label="Examples: Generate from Lyrics"
|
273 |
+
)
|
274 |
+
|
275 |
+
|
276 |
with gr.Column():
|
277 |
lrc_output = gr.Textbox(
|
278 |
label="Generated LRC Lyrics",
|
279 |
placeholder="Timed lyrics will appear here",
|
280 |
+
lines=57,
|
281 |
elem_classes="lrc-output",
|
282 |
show_copy_button=True
|
283 |
)
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|
284 |
|
285 |
# Bind functions
|
286 |
gen_from_theme_btn.click(
|
|
|
303 |
|
304 |
lyrics_btn.click(
|
305 |
fn=infer_music,
|
306 |
+
inputs=[lrc, audio_prompt, steps],
|
307 |
outputs=audio_output
|
308 |
)
|
309 |
+
|
310 |
+
|
311 |
demo.queue().launch(show_api=False, show_error=True)
|
312 |
|
313 |
|
diffrhythm/infer/infer.py
CHANGED
@@ -90,14 +90,13 @@ def inference(cfm_model, vae_model, cond, text, duration, style_prompt, negative
|
|
90 |
generated = generated.to(torch.float32)
|
91 |
latent = generated.transpose(1, 2) # [b d t]
|
92 |
|
93 |
-
output = decode_audio(latent, vae_model)
|
94 |
|
95 |
# Rearrange audio batch to a single sequence
|
96 |
output = rearrange(output, "b d n -> d (b n)")
|
97 |
-
|
98 |
-
|
99 |
-
|
100 |
-
return output
|
101 |
|
102 |
if __name__ == "__main__":
|
103 |
parser = argparse.ArgumentParser()
|
|
|
90 |
generated = generated.to(torch.float32)
|
91 |
latent = generated.transpose(1, 2) # [b d t]
|
92 |
|
93 |
+
output = decode_audio(latent, vae_model, chunked=False)
|
94 |
|
95 |
# Rearrange audio batch to a single sequence
|
96 |
output = rearrange(output, "b d n -> d (b n)")
|
97 |
+
output_tensor = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).cpu()
|
98 |
+
output_np = output_tensor.numpy().T.astype(np.float32)
|
99 |
+
return (44100, output_np)
|
|
|
100 |
|
101 |
if __name__ == "__main__":
|
102 |
parser = argparse.ArgumentParser()
|
diffrhythm/infer/infer_utils.py
CHANGED
@@ -34,8 +34,7 @@ def prepare_model(device):
|
|
34 |
|
35 |
# prepare vae
|
36 |
vae_ckpt_path = hf_hub_download(repo_id="ASLP-lab/DiffRhythm-vae", filename="vae_model.pt")
|
37 |
-
|
38 |
-
vae = torch.jit.load(vae_ckpt_path, map_location=device)
|
39 |
|
40 |
return cfm, tokenizer, muq, vae
|
41 |
|
|
|
34 |
|
35 |
# prepare vae
|
36 |
vae_ckpt_path = hf_hub_download(repo_id="ASLP-lab/DiffRhythm-vae", filename="vae_model.pt")
|
37 |
+
vae = torch.jit.load(vae_ckpt_path).to(device)
|
|
|
38 |
|
39 |
return cfm, tokenizer, muq, vae
|
40 |
|