Spaces:
Running
on
Zero
Running
on
Zero
test
Browse files- app.py +5 -5
- requirements.txt +1 -2
app.py
CHANGED
@@ -51,7 +51,7 @@ def infer_music(lrc, ref_audio_path, steps, max_frames=2048, device='cuda'):
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def R1_infer1(theme, tags_gen, language):
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try:
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client = OpenAI(api_key=os.getenv('
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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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@@ -66,7 +66,7 @@ def R1_infer1(theme, tags_gen, language):
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"""
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response = client.chat.completions.create(
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model=
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messages=[
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{"role": "system", "content": "You are a professional musician who has been invited to make music-related comments."},
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{"role": "user", "content": llm_prompt.format(theme=theme, tags=tags_gen, language=language)},
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@@ -85,14 +85,14 @@ 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=os.getenv('
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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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"""
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response = client.chat.completions.create(
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model=
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messages=[
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{"role": "system", "content": "You are a professional musician who has been invited to make music-related comments."},
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{"role": "user", "content": llm_prompt.format(lyrics_input=lyrics_input, tags_lyrics=tags_lyrics)},
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@@ -128,7 +128,7 @@ 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://
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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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def R1_infer1(theme, tags_gen, language):
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try:
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+
client = OpenAI(api_key=os.getenv('HS_DP_API'), 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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"""
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response = client.chat.completions.create(
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model="ep-20250304144033-nr9wl",
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messages=[
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{"role": "system", "content": "You are a professional musician who has been invited to make music-related comments."},
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{"role": "user", "content": llm_prompt.format(theme=theme, tags=tags_gen, language=language)},
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def R1_infer2(tags_lyrics, lyrics_input):
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client = OpenAI(api_key=os.getenv('HS_DP_API'), 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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"""
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response = client.chat.completions.create(
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model="ep-20250304144033-nr9wl",
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messages=[
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{"role": "system", "content": "You are a professional musician who has been invited to make music-related comments."},
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{"role": "user", "content": llm_prompt.format(lyrics_input=lyrics_input, tags_lyrics=tags_lyrics)},
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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://arxiv.org/abs/2503.01183">
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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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requirements.txt
CHANGED
@@ -30,5 +30,4 @@ einops==0.8.1
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lazy_loader==0.4
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scipy==1.15.2
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ftfy==6.3.1
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torchdiffeq==0.2.5
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https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.2cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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lazy_loader==0.4
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scipy==1.15.2
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ftfy==6.3.1
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torchdiffeq==0.2.5
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