longer

app.py

@@ -5,10 +5,73 @@ import subprocess
 from simple_diarizer.diarizer import Diarizer
 import streamlit as st
 
+def speech_to_text(uploaded):
+    model = whisper.load_model('tiny')
+    result = model.transcribe(uploaded,verbose=True)
+    return f'You said: {result["text"]}'
+
+def segment(nu_speakers):
+
+    diar = Diarizer(embed_model='xvec',cluster_method='sc')
+    segments = diar.diarize('mono.wav', num_speakers=nu_speakers)
+
+    sdf = pd.DataFrame(segments)
+
+    # reorganize so the first speaker is always speaker 1
+    speaker_s = sdf['label'].drop_duplicates().reset_index()['label']
+    speaker_d = dict((v,k+1) for k,v in speaker_s.items())
+
+    sdf['speaker'] = sdf['label'].replace(speaker_d)
+    return sdf
+
+def audio_to_df(uploaded):
+    monotize(uploaded)
+    model = whisper.load_model('tiny')
+    result = model.transcribe('mono.wav',verbose=True,
+                          without_timestamps=False)
+    tdf = pd.DataFrame(result['segments'])
+    return tdf
+
 def monotize(uploaded):
     cmd = f"ffmpeg -y -i {uploaded} -acodec pcm_s16le -ar 16000 -ac 1 mono.wav"
     subprocess.Popen(cmd, shell=True).wait()
 
+def add_preface(row):
+    text = row['text'].replace('\n','')
+    speaker = row['speaker']
+    return f'Speaker {speaker}: {text}'
+
+def transcribe(uploaded, nu_speakers):
+
+    monotize(uploaded)
+    tdf = audio_to_df(uploaded)
+    sdf = segment(nu_speakers)
+
+    ns_list = sdf[['start','speaker']].to_dict(orient='records')
+
+    # Find the nearest transcript line to the start of each speaker
+    for row in ns_list:
+        input = row['start']
+        id = tdf.iloc[(tdf['start']-input).abs().argsort()[:1]]['id'].values[0]
+        tdf.loc[tdf['id'] ==id, 'speaker'] = row['speaker']
+
+    tdf['speaker'].fillna(method = 'ffill', inplace = True)
+    tdf['speaker'].fillna(method = 'bfill', inplace = True)
+
+    tdf['n1'] = tdf['speaker'] != tdf['speaker'].shift(1)
+    tdf['speach'] = tdf['n1'].cumsum()
+    binned_df = tdf.groupby(['speach', 'speaker'])['text'].apply('\n'.join).reset_index()
+
+    binned_df['speaker'] = binned_df['speaker'].astype(int)
+    binned_df['output'] = binned_df.apply(add_preface, axis=1)
+
+    lines = []
+    for row in binned_df['output'].values:
+        st.write(row)
+        lines.append(row)
+
+    return '\n'.join(lines)
+
 
 form = st.form(key='my_form')
 uploaded = form.file_uploader("Choose a file")