app.py

import whisper
import pandas as pd
import whisper
import subprocess
from simple_diarizer.diarizer import Diarizer
import streamlit as st
import base64




def create_download_link(val, filename, label):
    b64 = base64.b64encode(val)
    return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="{filename}">{label}</a>'


def segment(nu_speakers):

    diar = Diarizer(embed_model='ecapa',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(model_size)
    result = model.transcribe('mono.wav',verbose=True,
                          without_timestamps=False,
                           task = task)
    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):
    with st.spinner(text="Converting file..."):
        monotize('temp_audio')

    audio_file = open('mono.wav', 'rb')
    audio_bytes = audio_file.read()
    st.audio('mono.wav', format='audio/wav')

    with st.spinner(text=f"Transcribing using {model_size} model..."):
        tdf = audio_to_df(uploaded)
    with st.spinner(text="Segmenting..."):
        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)
    tdf['speaker'] = tdf['speaker'].astype(int)

    tdf_cols = ['speaker','start','end','text']
    #st.dataframe(tdf[tdf_cols])
    return {'text':lines, 'df': tdf[tdf_cols]}


descript = ("This web app creates transcripts using OpenAI's [Whisper](https://github.com/openai/whisper) to transcribe "
            "audio files combined with [Chau](https://github.com/cvqluu)'s [Simple Diarizer](https://github.com/cvqluu/simple_diarizer) "
            "to partition the text by speaker.\n"
            "* You can upload an audio or video file of up to 200MBs.\n"
            "* Creating the transcript takes some time. "
            "The process takes approximately 20% of the length of the audio file using the base Whisper model.\n "
            "* The transcription process handles a variety of languages, and can also translate the audio to English. The tiny model is not good at translating. \n"
            "* Speaker segmentation seems to work best with the base model. The small model produces better transcripts, but something seems off with the timecodes, degrading the speaker attribution. \n"
            "* After uploading the file, be sure to select the number of speakers." )

st.title("Automated Transcription")
st.markdown(descript)

form = st.form(key='my_form')
uploaded = form.file_uploader("Choose a file")
nu_speakers = form.slider('Number of speakers in recording:', min_value=1, max_value=8, value=2, step=1)
models = form.selectbox(
    'Which Whisper model?',
    ('Tiny (fast)', 'Base (good)', 'Small (great but slow)'), index=1)
translate = form.checkbox('Translate to English?')
submit = form.form_submit_button("Transcribe!")


if submit:
    if models == 'Tiny (fast)':
        model_size = 'tiny'
    elif models == 'Base (good)':
        model_size ='base'
    elif models == 'Small (great but slow)':
        model_size = 'small'

    if translate == True:
        task = 'translate'
    else:
        task = 'transcribe'

    bytes_data = uploaded.getvalue()
    with open('temp_audio', 'wb') as outfile:
        outfile.write(bytes_data)
    transcript = transcribe('temp_audio', nu_speakers)

    csv = transcript['df'].to_csv( float_format='%.2f', index=False).encode('utf-8')
    text = '\n'.join(transcript['text']).encode('utf-8')
    download_url = create_download_link(text, 'transcript.txt', 'Download transcript as plain text.')
    st.markdown(download_url, unsafe_allow_html=True)

    download_url = create_download_link(csv, 'transcript.csv', 'Download transcript as CSV (with time codes)')
    st.markdown(download_url, unsafe_allow_html=True)