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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import subprocess | |
| import re | |
| import logging | |
| import os | |
| import numpy as np | |
| import matplotlib | |
| import scipy.io | |
| import scipy.io.wavfile | |
| matplotlib.use('Agg') | |
| logging.basicConfig(level=logging.INFO) | |
| logging.getLogger() | |
| def get_chunk_times(in_filename, silence_threshold, silence_duration=1): | |
| silence_duration_re = re.compile('silence_duration: (\d+.\d+)') | |
| silence_end_re = re.compile('silence_end: (\d+.\d+)\s') | |
| command = f"ffmpeg -i {in_filename} -af silencedetect=n=-{silence_threshold}dB:d={silence_duration} -f null - " | |
| out = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) | |
| stdout, stderr = out.communicate() | |
| lines = stdout.splitlines() | |
| ts = 0 | |
| chunks = [] | |
| for line in lines: | |
| match = silence_duration_re.search(str(line)) | |
| if(match): | |
| chunk_time = float(match.group(1)) | |
| ts = ts + chunk_time | |
| end = silence_end_re.search(str(line)) | |
| if(end): | |
| t_end = float(end.group(1)) | |
| t_start = t_end - chunk_time | |
| chunks.append([t_start, t_end, chunks]) | |
| logging.info(f"TS audio {os.path.basename(in_filename)} = {ts}") | |
| return ts, chunks | |
| def get_plot(a): | |
| x = [1, 2, 3] | |
| y = np.array([[1, 2], [3, 4], [5, 6]]) | |
| plt.plot(x, y) | |
| return plt.gcf() | |
| def get_audio_plot(filename, chunks): | |
| fig, ax = plt.subplots() | |
| fig.set_size_inches(18.5, 10.5) | |
| sampleRate, audioBuffer = scipy.io.wavfile.read(filename) | |
| duration = len(audioBuffer)/sampleRate | |
| time = np.arange(0,duration,1/sampleRate) #time vector | |
| ax.plot(time,audioBuffer) | |
| y1 = min(audioBuffer) | |
| y2 = max(audioBuffer) | |
| for c in chunks: | |
| ax.fill_between(c[0:2], y1, y2, | |
| color='gray', alpha=0.5) | |
| plt.xlabel('Time [s]') | |
| plt.ylabel('Amplitude') | |
| plt.title(os.path.basename(filename)) | |
| #plt.show() | |
| return plt.gcf() | |
| def get_audio_info(audio): | |
| ts, chunks = get_chunk_times(audio.name, 30, 1) | |
| p = get_audio_plot(audio.name, chunks) | |
| return str(ts), p | |
| otext = gr.outputs.Textbox(type="auto", label="Silence time") | |
| oplot = gr.outputs.Image(type="plot", label=None) | |
| iaudio = gr.inputs.Audio(source="upload", type="file", label=None) | |
| #iface = gr.Interface(audio, iaudio, [otext, oplot]) | |
| iface = gr.Interface( | |
| get_audio_info, | |
| iaudio, | |
| [otext, oplot], | |
| description="Enter .WAV audio to view silence areas", | |
| ) | |
| iface.test_launch() | |
| iface.launch() | |
| # import matplotlib.pyplot as plt | |
| # import numpy as np | |
| # import pandas as pd | |
| # import gradio as gr | |
| # import matplotlib | |
| # matplotlib.use('Agg') | |
| # iaudio = gr.inputs.Audio(source="upload", type="file", label=None) | |
| # def sales_projections(employee_data): | |
| # sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy() | |
| # regression_values = np.apply_along_axis( | |
| # lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data | |
| # ) | |
| # projected_months = np.repeat( | |
| # np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0 | |
| # ) | |
| # projected_values = np.array( | |
| # [ | |
| # month * month * regression[0] + month * regression[1] + regression[2] | |
| # for month, regression in zip(projected_months, regression_values) | |
| # ] | |
| # ) | |
| # # x = [1, 2, 3] | |
| # # y = np.array([[1, 2], [3, 4], [5, 6]]) | |
| # # plt.plot(x, y) | |
| # #plt.plot(projected_values.T) | |
| # #plt.legend(employee_data["Name"]) | |
| # return employee_data, get_plot(1), regression_values | |
| # iface = gr.Interface( | |
| # get_plot, | |
| # iaudio, | |
| # ["plot"], | |
| # description="Enter sales figures for employees to predict sales trajectory over year.", | |
| # ) | |
| # iface.launch() |