import numpy as np import streamlit as st import librosa import soundfile as sf import librosa.display from config import CONFIG import torch from dataset import MaskGenerator import onnxruntime, onnx import matplotlib.pyplot as plt from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from pystoi import stoi from pesq import pesq import pandas as pd import torchaudio from torchmetrics.audio import ShortTimeObjectiveIntelligibility as STOI from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality as PESQ from PLCMOS.plc_mos import PLCMOSEstimator from speechmos import dnsmos from speechmos import plcmos import speech_recognition as sr from jiwer import wer @st.cache def load_model(): path = 'lightning_logs/version_0/checkpoints/frn.onnx' onnx_model = onnx.load(path) options = onnxruntime.SessionOptions() options.intra_op_num_threads = 2 options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL session = onnxruntime.InferenceSession(path, options) input_names = [x.name for x in session.get_inputs()] output_names = [x.name for x in session.get_outputs()] return session, onnx_model, input_names, output_names def inference(re_im, session, onnx_model, input_names, output_names): inputs = {input_names[i]: np.zeros([d.dim_value for d in _input.type.tensor_type.shape.dim], dtype=np.float32) for i, _input in enumerate(onnx_model.graph.input) } output_audio = [] for t in range(re_im.shape[0]): inputs[input_names[0]] = re_im[t] out, prev_mag, predictor_state, mlp_state = session.run(output_names, inputs) inputs[input_names[1]] = prev_mag inputs[input_names[2]] = predictor_state inputs[input_names[3]] = mlp_state output_audio.append(out) output_audio = torch.tensor(np.concatenate(output_audio, 0)) output_audio = output_audio.permute(1, 0, 2).contiguous() output_audio = torch.view_as_complex(output_audio) output_audio = torch.istft(output_audio, window, stride, window=hann) return output_audio.numpy() def visualize(hr, lr, recon, sr): sr = sr window_size = 1024 window = np.hanning(window_size) stft_hr = librosa.core.spectrum.stft(hr, n_fft=window_size, hop_length=512, window=window) stft_hr = 2 * np.abs(stft_hr) / np.sum(window) stft_lr = librosa.core.spectrum.stft(lr, n_fft=window_size, hop_length=512, window=window) stft_lr = 2 * np.abs(stft_lr) / np.sum(window) stft_recon = librosa.core.spectrum.stft(recon, n_fft=window_size, hop_length=512, window=window) stft_recon = 2 * np.abs(stft_recon) / np.sum(window) fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharey=True, sharex=True, figsize=(16, 12)) ax1.title.set_text('Оригинальный сигнал') ax2.title.set_text('Сигнал с потерями') ax3.title.set_text('Улучшенный сигнал') canvas = FigureCanvas(fig) p = librosa.display.specshow(librosa.amplitude_to_db(stft_hr), ax=ax1, y_axis='log', x_axis='time', sr=sr) p = librosa.display.specshow(librosa.amplitude_to_db(stft_lr), ax=ax2, y_axis='log', x_axis='time', sr=sr) p = librosa.display.specshow(librosa.amplitude_to_db(stft_recon), ax=ax3, y_axis='log', x_axis='time', sr=sr) ax1.set_xlabel('Время, с') ax1.set_ylabel('Частота, Гц') ax2.set_xlabel('Время, с') ax2.set_ylabel('Частота, Гц') ax3.set_xlabel('Время, с') ax3.set_ylabel('Частота, Гц') return fig packet_size = CONFIG.DATA.EVAL.packet_size window = CONFIG.DATA.window_size stride = CONFIG.DATA.stride title = 'Сокрытие потерь пакетов' st.set_page_config(page_title=title, page_icon=":sound:") st.title(title) st.subheader('1. Загрузка аудио') uploaded_file = st.file_uploader("Загрузите аудио формата (.wav) 48 КГц") is_file_uploaded = uploaded_file is not None if not is_file_uploaded: uploaded_file = 'sample.wav' target, sr = librosa.load(uploaded_file) target = target[:packet_size * (len(target) // packet_size)] st.text('Ваше аудио') st.audio(uploaded_file) st.subheader('2. Выберите желаемый процент потерь') slider = [st.slider("Ожидаемый процент потерь для генератора потерь цепи Маркова", 0, 100, step=1)] loss_percent = float(slider[0])/100 mask_gen = MaskGenerator(is_train=False, probs=[(1 - loss_percent, loss_percent)]) lossy_input = target.copy().reshape(-1, packet_size) mask = mask_gen.gen_mask(len(lossy_input), seed=0)[:, np.newaxis] lossy_input *= mask lossy_input = lossy_input.reshape(-1) hann = torch.sqrt(torch.hann_window(window)) lossy_input_tensor = torch.tensor(lossy_input) re_im = torch.stft(lossy_input_tensor, window, stride, window=hann, return_complex=False).permute(1, 0, 2).unsqueeze( 1).numpy().astype(np.float32) session, onnx_model, input_names, output_names = load_model() if st.button('Сгенерировать потери'): with st.spinner('Ожидайте...'): output = inference(re_im, session, onnx_model, input_names, output_names) st.subheader('3. Визуализация') fig = visualize(target, lossy_input, output, sr) st.pyplot(fig) st.success('Сделано!') sf.write('target.wav', target, sr) sf.write('lossy.wav', lossy_input, sr) sf.write('enhanced.wav', output, sr) st.text('Оригинальное аудио') st.audio('target.wav') st.text('Аудио с потерями') st.audio('lossy.wav') st.text('Улучшенное аудио') st.audio('enhanced.wav') #data_clean, samplerate = torchaudio.load('target.wav') #data_lossy, samplerate = torchaudio.load('lossy.wav') #data_enhanced, samplerate = torchaudio.load('enhanced.wav') #min_len = min(data_clean.shape[1], data_lossy.shape[1], data_enhanced.shape[1]) #data_clean = data_clean[:, :min_len] #data_lossy = data_lossy[:, :min_len] #data_enhanced = data_enhanced[:, :min_len] #stoi = STOI(samplerate) #stoi_orig = round(float(stoi(data_clean, data_clean)),3) #stoi_lossy = round(float(stoi(data_clean, data_lossy)),5) #stoi_enhanced = round(float(stoi(data_clean, data_enhanced)),5) #stoi_mass=[stoi_orig, stoi_lossy, stoi_enhanced] #pesq = PESQ(8000, 'nb') #data_clean = data_clean.cpu().numpy() #data_lossy = data_lossy.cpu().numpy() #data_enhanced = data_enhanced.cpu().numpy() #if samplerate != 8000: #data_lossy = librosa.resample(data_lossy, orig_sr=48000, target_sr=8000) #data_clean = librosa.resample(data_clean, orig_sr=48000, target_sr=8000) #data_enhanced = librosa.resample(data_enhanced, orig_sr=48000, target_sr=8000) #pesq_orig = float(pesq(torch.tensor(data_clean), torch.tensor(data_clean))) #pesq_lossy = float(pesq(torch.tensor(data_lossy), torch.tensor(data_clean))) #pesq_enhanced = float(pesq(torch.tensor(data_enhanced), torch.tensor(data_clean))) #psq_mas=[pesq_orig, pesq_lossy, pesq_enhanced] #_____________________________________________ data_clean, samplerate = sf.read('target.wav') data_lossy, samplerate = sf.read('lossy.wav') data_enhanced, samplerate = sf.read('enhanced.wav') min_len = min(data_clean.shape[0], data_lossy.shape[0], data_enhanced.shape[0]) data_clean = data_clean[:min_len] data_lossy = data_lossy[:min_len] data_enhanced = data_enhanced[:min_len] stoi_orig = round(stoi(data_clean, data_clean, samplerate, extended=False),5) stoi_lossy = round(stoi(data_clean, data_lossy , samplerate, extended=False),5) stoi_enhanced = round(stoi(data_clean, data_enhanced, samplerate, extended=False),5) stoi_mass=[stoi_orig, stoi_lossy, stoi_enhanced] #def get_power(x, nfft): # S = librosa.stft(x, n_fft=nfft) # S = np.log(np.abs(S) ** 2 + 1e-8) # return S #def LSD(x_hr, x_pr): # S1 = get_power(x_hr, nfft=2048) # S2 = get_power(x_pr, nfft=2048) # lsd = np.mean(np.sqrt(np.mean((S1 - S2) ** 2, axis=-1)), axis=0) # return lsd #lsd_orig = LSD(data_clean,data_clean) #lsd_lossy = LSD(data_lossy,data_clean) #lsd_enhanced = LSD(data_enhanced,data_clean) #lsd_mass=[lsd_orig, lsd_lossy, lsd_enhanced] if samplerate != 8000: data_lossy = librosa.resample(data_lossy, orig_sr=48000, target_sr=8000) data_clean = librosa.resample(data_clean, orig_sr=48000, target_sr=8000) data_enhanced = librosa.resample(data_enhanced, orig_sr=48000, target_sr=8000) pesq_orig = pesq(fs = 8000, ref = data_clean, deg = data_clean, mode='nb') pesq_lossy = pesq(fs = 8000, ref = data_clean, deg = data_lossy, mode='nb') pesq_enhanced = pesq(fs = 8000, ref = data_clean, deg = data_enhanced, mode='nb') psq_mas=[pesq_orig, pesq_lossy, pesq_enhanced] data_clean, fs = sf.read('target.wav') data_lossy, fs = sf.read('lossy.wav') data_enhanced, fs = sf.read('enhanced.wav') if fs!= 16000: data_lossy = librosa.resample(data_lossy, orig_sr=48000, target_sr=16000) data_clean = librosa.resample(data_clean, orig_sr=48000, target_sr=16000) data_enhanced = librosa.resample(data_enhanced, orig_sr=48000, target_sr=16000) PLC_example=PLCMOSEstimator() PLC_org = PLC_example.run(audio_degraded=data_clean, audio_clean=data_clean)[0] PLC_lossy = PLC_example.run(audio_degraded=data_lossy, audio_clean=data_clean)[0] PLC_enhanced = PLC_example.run(audio_degraded=data_enhanced, audio_clean=data_clean)[0] PLC_massv1 = [PLC_org, PLC_lossy, PLC_enhanced] df_1 = pd.DataFrame(columns=['Audio', 'PESQ', 'STOI', 'PLCMOSv1']) df_1['Audio'] = ['Clean', 'Lossy', 'Enhanced'] df_1['PESQ'] = psq_mas df_1['STOI'] = stoi_mass #df['LSD'] = lsd_mass df_1['PLCMOSv1'] = PLC_massv1 #new_columns = pd.MultiIndex.from_tuples([('', 'Audio'), ('Эталонные метрики', 'PESQ'), ('Эталонные метрики', 'STOI'), ('Эталонные метрики', 'PLCMOSv1')]) # Присваиваем новый мультииндекс столбцам #df_1.columns = new_columns PLC_massv2 = [plcmos.run("target.wav", sr=16000)['plcmos'], plcmos.run("lossy.wav", sr=16000)['plcmos'], plcmos.run("enhanced.wav", sr=16000)['plcmos']] #DNS = [dnsmos.run("target.wav", sr=16000)['ovrl_mos'], dnsmos.run("lossy.wav", sr=16000)['ovrl_mos'], dnsmos.run("enhanced.wav", sr=16000)['ovrl_mos']] df_1['PLCMOSv2'] = PLC_massv2 #df_1['DNSMOS'] = DNS #df_2 = pd.DataFrame(columns=['DNSMOS', 'PLCMOSv2']) #df_2['DNSMOS'] = DNS #df_2['PLCMOSv2'] = PLC_massv2 #new_columns = pd.MultiIndex.from_tuples([('Неэталонные метрики', 'DNSMOS'), ('Неэталонные метрики', 'PLCMOSv2')]) # Присваиваем новый мультииндекс столбцам #df_2.columns = new_columns #df_merged = df_1.merge(df_2, left_index=True, right_index=True) r = sr.Recognizer () harvard = sr.AudioFile('target.wav') with harvard as source: audio = r.record(source) orig = r.recognize_google(audio, language = "ru-RU") harvard = sr.AudioFile('lossy.wav') with harvard as source: audio = r.record(source) lossy = r.recognize_google(audio, language = "ru-RU") harvard = sr.AudioFile('enhanced.wav') with harvard as source: audio = r.record(source) enhanced = r.recognize_google(audio, language = "ru-RU") error1 = wer(orig, orig) error2 = wer(orig, lossy) error2 = wer(orig, enhanced) WER_mass=[error1, error2, error3] df_1['WER'] = WER_mass st.dataframe(df_1)