anhnv125 commited on
Commit
042c5b7
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2 Parent(s): 28d7565 16e5af6

Merge branch 'main' of https://huggingface.co./spaces/anhnv125/FRN

Browse files
Files changed (3) hide show
  1. README.md +4 -2
  2. app.py +11 -7
  3. requirements.txt +1 -1
README.md CHANGED
@@ -3,8 +3,10 @@ title: FRN
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  emoji: πŸ“‰
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  colorFrom: gray
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  colorTo: red
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- sdk: static
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- pinned: false
 
 
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  ---
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  # FRN - Full-band Recurrent Network Official Implementation
 
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  emoji: πŸ“‰
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  colorFrom: gray
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  colorTo: red
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+ sdk: streamlit
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+ pinned: true
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+ app_file: app.py
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+ sdk_version: 1.10.0
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  ---
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  # FRN - Full-band Recurrent Network Official Implementation
app.py CHANGED
@@ -1,5 +1,6 @@
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  import streamlit as st
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  import librosa
 
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  import librosa.display
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  from config import CONFIG
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  import torch
@@ -9,7 +10,7 @@ import matplotlib.pyplot as plt
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  import numpy as np
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  from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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- @st.cache_resource
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  def load_model():
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  path = 'lightning_logs/version_0/checkpoints/frn.onnx'
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  onnx_model = onnx.load(path)
@@ -87,9 +88,9 @@ target = target[:packet_size * (len(target) // packet_size)]
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  st.subheader('Original audio')
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  st.audio(uploaded_file)
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- st.subheader('Choose loss packet percentage')
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- loss_percent = st.radio('Loss percentage', ['10%', '20%', '30%', '40%'])
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- loss_percent = float(loss_percent[:-1])/100
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  mask_gen = MaskGenerator(is_train=False, probs=[(1 - loss_percent, loss_percent)])
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  lossy_input = target.copy().reshape(-1, packet_size)
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  mask = mask_gen.gen_mask(len(lossy_input), seed=0)[:, np.newaxis]
@@ -109,9 +110,12 @@ if st.button('Conceal lossy audio!'):
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  fig = visualize(target, lossy_input, output)
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  st.pyplot(fig)
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  st.success('Done!')
 
 
 
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  st.text('Original audio')
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- st.audio(target, sample_rate=sr)
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  st.text('Lossy audio')
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- st.audio(lossy_input, sample_rate=sr)
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  st.text('Enhanced audio')
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- st.audio(output, sample_rate=sr)
 
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  import streamlit as st
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  import librosa
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+ import soundfile as sf
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  import librosa.display
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  from config import CONFIG
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  import torch
 
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  import numpy as np
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  from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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+ @st.cache
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  def load_model():
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  path = 'lightning_logs/version_0/checkpoints/frn.onnx'
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  onnx_model = onnx.load(path)
 
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  st.subheader('Original audio')
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  st.audio(uploaded_file)
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+ st.subheader('Choose expected packet loss rate')
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+ slider = [st.slider("Expected loss rate for Markov Chain loss generator", 0, 100, step=1)]
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+ loss_percent = float(slider[0])/100
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  mask_gen = MaskGenerator(is_train=False, probs=[(1 - loss_percent, loss_percent)])
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  lossy_input = target.copy().reshape(-1, packet_size)
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  mask = mask_gen.gen_mask(len(lossy_input), seed=0)[:, np.newaxis]
 
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  fig = visualize(target, lossy_input, output)
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  st.pyplot(fig)
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  st.success('Done!')
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+ sf.write('target.wav', target, sr)
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+ sf.write('lossy.wav', lossy_input, sr)
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+ sf.write('enhanced.wav', output, sr)
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  st.text('Original audio')
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+ st.audio('target.wav')
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  st.text('Lossy audio')
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+ st.audio('lossy.wav')
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  st.text('Enhanced audio')
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+ st.audio('enhanced.wav')
requirements.txt CHANGED
@@ -12,6 +12,6 @@ soundfile==0.11.0
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  torch==1.13.1
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  torchmetrics==0.11.0
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  tqdm==4.64.0
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- stoi==0.3.3
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  pesq==0.0.4
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  onnx==1.13.0
 
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  torch==1.13.1
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  torchmetrics==0.11.0
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  tqdm==4.64.0
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+ pystoi==0.3.3
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  pesq==0.0.4
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  onnx==1.13.0