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import shlex
import subprocess
import spaces
import torch
import gradio as gr
# install packages for mamba
def install_mamba():
#subprocess.run(shlex.split("pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118"))
subprocess.run(shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v2.2.2/mamba_ssm-2.2.2+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
#subprocess.run(shlex.split("pip install numpy==1.26.4"))
def clone_github():
subprocess.run([
"git", "clone",
f"https://RoyChao19477:{os.environ['GITHUB_TOKEN']}@github.com/RoyChao19477/for_HF_AVSEMamba.git"
])
install_mamba()
clone_github()
ABOUT = """
# SEMamba: Speech Enhancement
A Mamba-based model that denoises real-world audio.
Upload or record a noisy clip and click **Enhance** to hear + see its spectrogram.
"""
import torch
import yaml
import librosa
import librosa.display
import matplotlib
import numpy as np
import soundfile as sf
import matplotlib.pyplot as plt
from models.stfts import mag_phase_stft, mag_phase_istft
from models.generator import SEMamba
from models.pcs400 import cal_pcs
from ultralytics import YOLO
import supervision as sv
def dummy_fn(video):
return video
model = YOLO("yolov8n.pt") # or a face-specific checkpoint
results = model.predict("input_frame.jpg")[0]
# Filter only face (class 0 in COCO = 'person'; need a face-detection specific model if you want more precision)
faces = [b for b in results.boxes if b.cls == 0]
gr.Interface(fn=dummy_fn, inputs=gr.Video(source="webcam"), outputs="video").launch()kkkkkj
ckpt = "ckpts/SEMamba_advanced.pth"
cfg_f = "recipes/SEMamba_advanced.yaml"
# load config
with open(cfg_f, 'r') as f:
cfg = yaml.safe_load(f)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cuda"
model = SEMamba(cfg).to(device)
#sdict = torch.load(ckpt, map_location=device)
#model.load_state_dict(sdict["generator"])
#model.eval()
@spaces.GPU
def enhance(filepath, model_name):
# Load model based on selection
ckpt_path = {
"VCTK-Demand": "ckpts/SEMamba_advanced.pth",
"VCTK+DNS": "ckpts/vd.pth"
}[model_name]
print("Loading:", ckpt_path)
model.load_state_dict(torch.load(ckpt_path, map_location=device)["generator"])
model.eval()
with torch.no_grad():
# load & resample
wav, orig_sr = librosa.load(filepath, sr=None)
noisy_wav = wav.copy()
if orig_sr != 16000:
wav = librosa.resample(wav, orig_sr=orig_sr, target_sr=16000)
x = torch.from_numpy(wav).float().to(device)
norm = torch.sqrt(len(x)/torch.sum(x**2))
#x = (x * norm).unsqueeze(0)
x = (x * norm)
# split into 4s segments (64000 samples)
segment_len = 4 * 16000
chunks = x.split(segment_len)
enhanced_chunks = []
for chunk in chunks:
if len(chunk) < segment_len:
#pad = torch.zeros(segment_len - len(chunk), device=chunk.device)
pad = (torch.randn(segment_len - len(chunk), device=chunk.device) * 1e-4)
chunk = torch.cat([chunk, pad])
chunk = chunk.unsqueeze(0)
amp, pha, _ = mag_phase_stft(chunk, 400, 100, 400, 0.3)
amp2, pha2, _ = model(amp, pha)
out = mag_phase_istft(amp2, pha2, 400, 100, 400, 0.3)
out = (out / norm).squeeze(0)
enhanced_chunks.append(out)
out = torch.cat(enhanced_chunks)[:len(x)].cpu().numpy() # trim padding
# back to original rate
if orig_sr != 16000:
out = librosa.resample(out, orig_sr=16000, target_sr=orig_sr)
# Normalize
peak = np.max(np.abs(out))
if peak > 0.05:
out = out / peak * 0.85
# write file
sf.write("enhanced.wav", out, orig_sr)
# spectrograms
fig, axs = plt.subplots(1, 2, figsize=(16, 4))
# noisy
D_noisy = librosa.stft(noisy_wav, n_fft=512, hop_length=256)
S_noisy = librosa.amplitude_to_db(np.abs(D_noisy), ref=np.max)
librosa.display.specshow(S_noisy, sr=orig_sr, hop_length=256, x_axis="time", y_axis="hz", ax=axs[0], vmax=0)
axs[0].set_title("Noisy Spectrogram")
# enhanced
D_clean = librosa.stft(out, n_fft=512, hop_length=256)
S_clean = librosa.amplitude_to_db(np.abs(D_clean), ref=np.max)
librosa.display.specshow(S_clean, sr=orig_sr, hop_length=256, x_axis="time", y_axis="hz", ax=axs[1], vmax=0)
#librosa.display.specshow(S_clean, sr=16000, hop_length=512, x_axis="time", y_axis="hz", ax=axs[1], vmax=0)
axs[1].set_title("Enhanced Spectrogram")
plt.tight_layout()
return "enhanced.wav", fig
#with gr.Blocks() as demo:
# gr.Markdown(ABOUT)
# input_audio = gr.Audio(label="Input Audio", type="filepath", interactive=True)
# enhance_btn = gr.Button("Enhance")
# output_audio = gr.Audio(label="Enhanced Audio", type="filepath")
# plot_output = gr.Plot(label="Spectrograms")
#
# enhance_btn.click(fn=enhance, inputs=input_audio, outputs=[output_audio, plot_output])
#
#demo.queue().launch()
with gr.Blocks() as demo:
gr.Markdown(ABOUT)
input_audio = gr.Audio(label="Input Audio", type="filepath", interactive=True)
model_choice = gr.Radio(
label="Choose Model (The use of VCTK+DNS is recommended)",
choices=["VCTK-Demand", "VCTK+DNS"],
value="VCTK-Demand"
)
enhance_btn = gr.Button("Enhance")
output_audio = gr.Audio(label="Enhanced Audio", type="filepath")
plot_output = gr.Plot(label="Spectrograms")
enhance_btn.click(
fn=enhance,
inputs=[input_audio, model_choice],
outputs=[output_audio, plot_output]
)
gr.Markdown("**Note**: The current models are trained on 16kHz audio. Therefore, any input audio not sampled at 16kHz will be automatically resampled before enhancement.")
demo.queue().launch()
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