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Running on Zero
Running on Zero
File size: 3,723 Bytes
61383c3 | 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 | 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 merit_runtime import compare_audio
from pyharp import ModelCard, build_endpoint
MIN_AUDIO_SECONDS = 5
MAX_AUDIO_SECONDS = 10
OUTPUT_ROOT = Path(tempfile.gettempdir()) / "merit_outputs"
model_card = ModelCard(
name="MERIT",
description=(
"Compare two music clips using independent melody, rhythm, "
"and timbre similarity representations."
),
author="AMAAI Lab",
tags=[
"music-information-retrieval",
"music-similarity",
"melody",
"rhythm",
"timbre",
],
)
def _validate_audio(path: str | None, label: str) -> str:
if not path:
raise gr.Error(f"Please upload {label}.")
try:
duration = sf.info(path).duration
except Exception as exc:
raise gr.Error(f"Could not read {label}: {exc}") from exc
if duration <= 0:
raise gr.Error(f"{label} is empty.")
if duration < MIN_AUDIO_SECONDS:
raise gr.Error(
f"{label} must be at least {MIN_AUDIO_SECONDS} seconds long. "
f"Received {duration:.1f} seconds."
)
if duration > MAX_AUDIO_SECONDS:
raise gr.Error(
f"{label} must be no longer than {MAX_AUDIO_SECONDS} seconds. "
f"Received {duration:.1f} seconds."
)
return path
@spaces.GPU(duration=120)
def process_fn(
reference_audio: str | None,
comparison_audio: str | None,
) -> str:
reference_audio = _validate_audio(reference_audio, "Reference Audio")
comparison_audio = _validate_audio(comparison_audio, "Comparison Audio")
try:
scores = compare_audio(reference_audio, comparison_audio)
except Exception as exc:
raise gr.Error(f"MERIT inference failed: {exc}") from exc
output_dir = OUTPUT_ROOT / uuid.uuid4().hex
output_dir.mkdir(parents=True, exist_ok=True)
output_path = output_dir / "merit_similarity.json"
output_path.write_text(
json.dumps(
{
"model": "MERIT",
"score_type": "cosine_similarity",
"score_range": [-1.0, 1.0],
"scores": scores,
},
indent=2,
)
+ "\n",
encoding="utf-8",
)
return str(output_path)
with gr.Blocks(title="MERIT Music Similarity") as demo:
input_components = [
gr.Audio(
type="filepath",
label="Reference Audio",
)
.harp_required(True)
.set_info("First music clip, 5 to 10 seconds long."),
gr.Audio(
type="filepath",
label="Comparison Audio",
)
.harp_required(True)
.set_info("Second music clip, 5 to 10 seconds long."),
]
output_components = [
gr.File(
type="filepath",
file_types=[".json"],
label="Similarity Results",
).set_info("Melody, rhythm, and timbre cosine similarity scores."),
]
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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