Upload app.py

app.py

@@ -0,0 +1,140 @@
+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()