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import whisper | |
import pandas as pd | |
import whisper | |
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") | |
nu_speakers = form.slider('Number of speakers in audio file:', min_value=1, max_value=6, value=2, step=1) | |
submit = form.form_submit_button("Transcribe!") | |
if submit: | |
bytes_data = uploaded.getvalue() | |
with open('temp_audio', 'wb') as outfile: | |
outfile.write(bytes_data) | |
st.write('Converting audio file.') | |
monotize('temp_audio') | |
text = transcribe('temp_audio', nu_speakers) | |