Changed app_file name.

Interface.py

@@ -1,75 +0,0 @@
-import glob
-import random
-import os
-import soundfile as sf
-import streamlit as st
-from pydub import AudioSegment
-
-from modules.diarization.nemo_diarization import diarization
-
-st.title('Call Transcription demo')
-st.subheader('This simple demo shows the possibilities of the ASR and NLP in the task of '
-             'automatic speech recognition and diarization. It works with mp3, ogg and wav files. You can randomly '
-             'pickup a set of images from the built-in database or try uploading your own files.')
-
-
-if st.button('Try random samples from the database'):
-    folder = "data/datasets/crema_d_diarization_chunks"
-    os.makedirs(folder, exist_ok=True)
-    list_all_audio = glob.glob("data/datasets/crema_d_diarization_chunks/*.wav")
-    chosen_files = sorted(random.sample(list_all_audio, 1))
-    file_name = os.path.basename(chosen_files[0]).split(".")[0]
-    audio_file = open(chosen_files[0], 'rb')
-    audio_bytes = audio_file.read()
-    st.audio(audio_bytes)
-    f = sf.SoundFile(chosen_files[0])
-    st.write("Starting transcription. Estimated processing time: %0.1f seconds" % (f.frames / (f.samplerate * 5)))
-    result = diarization(chosen_files[0])
-    with open("info/transcripts/pred_rttms/" + file_name + ".txt") as f:
-        transcript = f.read()
-    st.write("Transcription completed.")
-    st.write("Number of speakers: %s" % result[file_name]["speaker_count"])
-    st.write("Sentences: %s" % len(result[file_name]["sentences"]))
-    st.write("Words: %s" % len(result[file_name]["words"]))
-    st.download_button(
-        label="Download audio transcript",
-        data=transcript,
-        file_name='transcript.txt',
-        mime='text/csv',
-    )
-
-uploaded_file = st.file_uploader("Choose your recording with a speech",
-                                 accept_multiple_files=False, type=["mp3", "wav", "ogg"])
-if uploaded_file is not None:
-    folder = "data/user_data/"
-    os.makedirs(folder, exist_ok=True)
-    for f in glob.glob(folder + '*'):
-        os.remove(f)
-    save_path = folder + uploaded_file.name
-    if ".mp3" in uploaded_file:
-        sound = AudioSegment.from_mp3(uploaded_file)
-    elif ".ogg" in uploaded_file:
-        sound = AudioSegment.from_ogg(uploaded_file)
-    else:
-        sound = AudioSegment.from_wav(uploaded_file)
-    sound.export(save_path, format="wav", parameters=["-ac", "1"])
-    file_name = os.path.basename(save_path).split(".")[0]
-    audio_file = open(save_path, 'rb')
-    audio_bytes = audio_file.read()
-    st.audio(audio_bytes)
-    f = sf.SoundFile(save_path)
-    st.write("Starting transcription. Estimated processing time: %0.0f minutes and %02.0f seconds"
-             % ((f.frames / (f.samplerate * 3) // 60), (f.frames / (f.samplerate * 3) % 60)))
-    result = diarization(save_path)
-    with open("info/transcripts/pred_rttms/" + file_name + ".txt") as f:
-        transcript = f.read()
-    st.write("Transcription completed.")
-    st.write("Number of speakers: %s" % result[file_name]["speaker_count"])
-    st.write("Sentences: %s" % len(result[file_name]["sentences"]))
-    st.write("Words: %s" % len(result[file_name]["words"]))
-    st.download_button(
-        label="Download audio transcript",
-        data=transcript,
-        file_name='transcript.txt',
-        mime='text/csv',
-    )