Temporary remove samples.

.gitattributes

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
@@ -29,3 +30,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+=======
+*.wav filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+>>>>>>> 02dca0a (Temporary remove samples.)

Interface.py

@@ -0,0 +1,75 @@
+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',
+    )