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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 | |
| 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 | |
| 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 | |
| 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') | |