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Running on Zero
Running on Zero
File size: 4,553 Bytes
96558cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | from __future__ import annotations
import json
import tempfile
import uuid
from pathlib import Path
import gradio as gr
import soundfile as sf
try:
import spaces
except ImportError:
class spaces:
class GPU:
def __init__(self, func=None, duration=60):
self.func = func
def __call__(self, *args, **kwargs):
if self.func is not None:
return self.func(*args, **kwargs)
return args[0]
from pyharp import ModelCard, build_endpoint
from muq_mulan_runtime import rank_descriptions
MIN_AUDIO_SECONDS = 10
MAX_AUDIO_SECONDS = 60
MAX_DESCRIPTIONS = 8
MAX_DESCRIPTION_LENGTH = 300
OUTPUT_ROOT = Path(tempfile.gettempdir()) / "muq_mulan_outputs"
model_card = ModelCard(
name="MuQ-MuLan",
description=(
"Rank English music descriptions by their similarity to an "
"uploaded music clip."
),
author="Tencent AI Lab",
tags=[
"music-information-retrieval",
"music-text-retrieval",
"music-tagging",
"audio-analysis",
],
)
def _validate_audio(path: str | None) -> str:
if not path:
raise gr.Error("Please upload a music clip.")
try:
duration = sf.info(path).duration
except Exception as exc:
raise gr.Error(f"Could not read the audio file: {exc}") from exc
if duration < MIN_AUDIO_SECONDS:
raise gr.Error(
f"Audio must be at least {MIN_AUDIO_SECONDS} seconds long. "
f"Received {duration:.1f} seconds."
)
if duration > MAX_AUDIO_SECONDS:
raise gr.Error(
f"Audio must be no longer than {MAX_AUDIO_SECONDS} seconds. "
f"Received {duration:.1f} seconds."
)
return path
def _parse_descriptions(value: str | None) -> list[str]:
descriptions = [
line.strip()
for line in (value or "").splitlines()
if line.strip()
]
if not descriptions:
raise gr.Error("Enter at least one music description.")
if len(descriptions) > MAX_DESCRIPTIONS:
raise gr.Error(
f"Enter no more than {MAX_DESCRIPTIONS} descriptions."
)
if any(len(description) > MAX_DESCRIPTION_LENGTH for description in descriptions):
raise gr.Error(
"Each description must be no more than "
f"{MAX_DESCRIPTION_LENGTH} characters."
)
return descriptions
@spaces.GPU(duration=240)
def process_fn(
input_audio: str | None,
candidate_descriptions: str | None,
) -> str:
input_audio = _validate_audio(input_audio)
descriptions = _parse_descriptions(candidate_descriptions)
try:
results = rank_descriptions(input_audio, descriptions)
except Exception as exc:
raise gr.Error(f"MuQ-MuLan inference failed: {exc}") from exc
output_dir = OUTPUT_ROOT / uuid.uuid4().hex
output_dir.mkdir(parents=True, exist_ok=True)
output_path = output_dir / "muq_mulan_similarity.json"
output_path.write_text(
json.dumps(
{
"model": "OpenMuQ/MuQ-MuLan-large",
"score_type": "cosine_similarity",
"score_range": [-1.0, 1.0],
"results": results,
},
ensure_ascii=False,
indent=2,
)
+ "\n",
encoding="utf-8",
)
return str(output_path)
with gr.Blocks(title="MuQ-MuLan Music-Text Similarity") as demo:
input_components = [
gr.Audio(
type="filepath",
label="Music Audio",
)
.harp_required(True)
.set_info("Music clip between 10 and 60 seconds long."),
gr.Textbox(
lines=5,
label="Candidate Descriptions",
placeholder=(
"upbeat electronic dance music\n"
"slow acoustic ballad\n"
"bright piano melody"
),
)
.harp_required(True)
.set_info("Enter one English description per line."),
]
output_components = [
gr.File(
type="filepath",
file_types=[".json"],
label="Similarity Ranking",
).set_info("Descriptions ranked by cosine similarity."),
]
build_endpoint(
model_card=model_card,
input_components=input_components,
output_components=output_components,
process_fn=process_fn,
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=1).launch(
show_error=True,
pwa=True,
)
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