Spaces:
Running
Running
File size: 2,229 Bytes
8769669 | 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 | from __future__ import annotations
import gradio as gr
from pyharp import *
try: # torch>=2.6 flipped torch.load(weights_only) to True; legacy ckpts need False
import torch as _torch
if getattr(_torch.load, "__harp_compat__", False) is False:
_torch_load_orig = _torch.load
def _torch_load_compat(*args, **kwargs):
kwargs.setdefault("weights_only", False)
return _torch_load_orig(*args, **kwargs)
_torch_load_compat.__harp_compat__ = True
_torch.load = _torch_load_compat
except Exception: # torch not installed / unexpected API -- nothing to patch
pass
import tempfile
import matchering as mg
model_card = ModelCard(
name="Matchering",
description="Match the RMS, Frequency Response, Peak Amplitude, and Stereo Width of a target track to a reference track for instant mastering.",
author="sergree",
tags=["audio", "mastering", "dsp", "matching"],
)
def process_fn(target_audio, ref_audio, bit_depth):
out_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
out_file.close()
out_path = out_file.name
if bit_depth == "pcm24":
result_config = mg.pcm24(out_path)
else:
result_config = mg.pcm16(out_path)
mg.process(
target=target_audio,
reference=ref_audio,
results=[result_config]
)
return out_path
with gr.Blocks() as demo:
input_components = [
gr.Audio(type="filepath", label="Target Audio").harp_required(True).set_info("The track you want to master (your mix)."),
gr.Audio(type="filepath", label="Reference Audio").harp_required(True).set_info("The reference track you want your target to sound like."),
gr.Dropdown(choices=["pcm16", "pcm24"], value="pcm16", label="Output Bit Depth", info="Choose 16-bit PCM (CD quality) or 24-bit PCM (Studio quality) for the output file."),
]
output_components = [
gr.Audio(type="filepath", label="Mastered Audio"),
]
build_endpoint(
model_card=model_card,
input_components=input_components,
output_components=output_components,
process_fn=process_fn,
)
demo.queue().launch(share=True, show_error=False, pwa=True)
|