Create app.py

app.py

@@ -0,0 +1,59 @@
+import torch
+import whisper
+import pytube
+import librosa
+import streamlit as st
+import numpy as np
+from fpdf import FPDF
+
+
+
+def predict(url=None, model = "medium", translation="No",tran_lang="en"):
+
+    model_m = whisper.load_model(model)
+
+    file_path = 'https://cf-courses-data.s3.ujs.cloud-object-storage.appdomain.cloud/IBM-GPXX0EPMEN/20220627_140242.mp4'
+
+    audio_35 = whisper.load_audio(file_path)
+
+    audio = whisper.pad_or_trim(audio_35)
+
+    mel = whisper.log_mel_spectrogram(audio).to(model_m.device)
+
+    _,probs = model_m.detect_language(mel)
+
+    lang_dict = sorted(probs)
+    video_url = url
+    v_data = pytube.YouTube(video_url)
+    speech = v_data.streams.get_audio_only()
+    test_audio_file = speech.download()
+
+    transcription = model_m.transcribe(test_audio_file,fp16=False)["text"]
+
+    if translation == "Yes":
+        trans = model_m.transcribe(file_path,language=tran_lang,fp16=False)["text"]
+        return lang_dict, transcription, trans
+    else:
+        return lang_dict, transcription
+
+url = st.text_input(value="Please enter the YouTube url: ", label="YouTube URL")
+tran_req = st.selectbox(label="Do you want to translate the transcript?",options=("Yes","No"))
+
+if tran_req=="Yes":
+    lang = st.selectbox(label="Please select the required language: ", options=("en","fr","jp","bn","de","ga"))
+else:
+    lang = "en"
+
+if st.button("Generate"):
+    lang_d,transcription,trans = predict(url,translation=tran_req,tran_lang=lang)
+
+    st.write(lang_d)
+    pdf = FPDF()
+    pdf.add_page()
+
+    pdf.set_font("Arial", size = 15)
+    
+    for words in transcription:
+        pdf.cell(200, 10, txt = x, ln = 1, align = 'L')
+    output = pdf.output("transcript.pdf")
+    st.download(label="Click here to download the transcript", data=output, mime='pdf',file_name="transcript.pdf")