Upload app.py

app.py

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+import streamlit as st
+import streamlit.components.v1 as stc
+import noisereduce as nr
+import librosa
+import soundfile as sf
+import numpy as np
+import plotly.graph_objects as go
+import pickle
+
+from pyannote.audio.utils.signal import Binarize
+import torch
+
+@st.cache
+def speech_activity_detection_model():
+    # sad = torch.hub.load('pyannote-audio', 'sad_ami', source='local', device='cpu', batch_size=128)
+    with open('speech_activity_detection_model.pkl', 'rb') as f:
+        sad = pickle.load(f)
+    return sad
+
+@st.cache
+def trim_noise_part_from_speech(sad, fname, speech_wav, sr):
+    file_obj = {"uri": "filename", "audio": fname}
+    sad_scores = sad(file_obj)
+    binarize = Binarize(offset=0.52, onset=0.52, log_scale=True, min_duration_off=0.1, min_duration_on=0.1)
+    speech = binarize.apply(sad_scores, dimension=1)
+    
+    noise_wav = np.zeros((speech_wav.shape[0], 0))
+    append_axis = 1 if speech_wav.ndim == 2 else 0
+    noise_ranges = []
+    noise_start = 0
+    for segmentation in speech.segmentation():
+        noise_end, next_noise_start = int(segmentation.start*sr), int(segmentation.end*sr)
+        noise_wav = np.append(noise_wav, speech_wav[:, noise_start:noise_end], axis=append_axis)
+        noise_ranges.append((noise_start/sr, noise_end/sr))
+        noise_start = next_noise_start
+    return noise_wav.T, noise_ranges
+
+@st.cache
+def trim_audio(data, rate, start_sec=None, end_sec=None):
+    start, end = int(start_sec * rate), int(end_sec * rate)
+    if data.ndim == 1: # mono
+        return data[start:end]
+    elif data.ndim == 2: # stereo
+        return data[:, start:end]
+    
+title = 'Audio noise reduction'
+st.set_page_config(page_title=title, page_icon=":sound:")
+st.title(title)
+
+uploaded_file = st.file_uploader("Upload your audio file (.wav)") 
+
+is_file_uploaded = uploaded_file is not None
+if not is_file_uploaded:
+    uploaded_file = 'sample.wav'    
+
+wav, sr = librosa.load(uploaded_file, sr=None)
+wav_seconds = int(len(wav)/sr)
+
+st.subheader('Original audio')
+st.audio(uploaded_file)
+
+st.subheader('Noise part')
+noise_part_detection_method = st.radio('Noise source detection', ['Manually', 'Automatically (using speech activity detections)'])
+if noise_part_detection_method == "Manually": # ノイズ区間は1箇所
+    default_ranges = (0.0, float(wav_seconds)) if is_file_uploaded else (73.0, float(wav_seconds))
+    noise_part_ranges = [st.slider("Select a part of the noise (sec)", 0.0, float(wav_seconds), default_ranges, step=0.1)]
+    noise_wav = trim_audio(wav, sr, noise_part_ranges[0][0], noise_part_ranges[0][1])
+    
+elif noise_part_detection_method == "Automatically (using speech activity detections)": # ノイズ区間が複数
+    with st.spinner('Please wait for Detecting the speech activities'):
+        sad = speech_activity_detection_model()
+        noise_wav, noise_part_ranges = trim_noise_part_from_speech(sad, uploaded_file, wav, sr)
+   
+fig = go.Figure()
+x_wav = np.arange(len(wav)) / sr
+fig.add_trace(go.Scatter(y=wav[::1000]))
+for noise_part_range in noise_part_ranges:
+    fig.add_vrect(x0=int(noise_part_range[0]*sr/1000), x1=int(noise_part_range[1]*sr/1000), fillcolor="Red", opacity=0.2)
+fig.update_layout(width=700, margin=dict(l=0, r=0, t=0, b=0, pad=0))
+fig.update_yaxes(visible=False, ticklabelposition='inside', tickwidth=0)
+st.plotly_chart(fig, use_container_with=True)
+
+st.text('Noise audio')
+sf.write('noise_clip.wav', noise_wav, sr)
+noise_wav, sr = librosa.load('noise_clip.wav', sr=None)
+st.audio('noise_clip.wav')
+
+if st.button('Denoise the audio!'):
+    with st.spinner('Please wait for completion'):
+        nr_wav = nr.reduce_noise(audio_clip=wav, noise_clip=noise_wav, prop_decrease=1.0)
+
+        st.subheader('Denoised audio')
+        sf.write('nr_clip.wav', nr_wav, sr)
+    st.success('Done!')
+    st.text('Denoised audio')
+    st.audio('nr_clip.wav')