Create app.py

app.py

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+
+import streamlit as st
+import openai
+import os
+from pydub import AudioSegment
+from dotenv import load_dotenv
+from tempfile import NamedTemporaryFile
+import math
+from docx import Document
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Set your OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def get_chunk_length_ms(file_path, target_size_mb):
+    """
+    Calculate the length of each chunk in milliseconds to create chunks of approximately target_size_mb.
+
+    Args:
+    file_path (str): Path to the audio file.
+    target_size_mb (int): Target size of each chunk in megabytes.
+
+    Returns:
+    int: Chunk length in milliseconds.
+    """
+    audio = AudioSegment.from_file(file_path)
+    file_size_bytes = os.path.getsize(file_path)
+    duration_ms = len(audio)
+
+    # Calculate the approximate duration per byte
+    duration_per_byte = duration_ms / file_size_bytes
+
+    # Calculate the chunk length in milliseconds for the target size
+    chunk_length_ms = target_size_mb * 1024 * 1024 * duration_per_byte
+    return math.floor(chunk_length_ms)
+
+def split_audio(audio_file_path, chunk_length_ms):
+    """
+    Split an audio file into chunks of specified length.
+
+    Args:
+    audio_file_path (str): Path to the audio file.
+    chunk_length_ms (int): Length of each chunk in milliseconds.
+
+    Returns:
+    list: List of AudioSegment chunks.
+    """
+    audio = AudioSegment.from_file(audio_file_path)
+
+    chunks = [audio[i:i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
+
+    return chunks
+
+def transcribe(audio_file):
+    """
+    Transcribe an audio file using OpenAI Whisper model.
+
+    Args:
+    audio_file (str): Path to the audio file.
+
+    Returns:
+    str: Transcribed text.
+    """
+    with open(audio_file, "rb") as audio:
+        response = openai.audio.transcriptions.create(
+            model="whisper-1",
+            file=audio,
+            response_format="text",
+            language="en"  # Ensures transcription is in English
+        )
+    return response
+
+def process_audio_chunks(audio_chunks):
+    """
+    Process and transcribe each audio chunk.
+
+    Args:
+    audio_chunks (list): List of AudioSegment chunks.
+
+    Returns:
+    str: Combined transcription from all chunks.
+    """
+    transcriptions = []
+    min_length_ms = 100  # Minimum length required by OpenAI API (0.1 seconds)
+    
+    for i, chunk in enumerate(audio_chunks):
+        if len(chunk) < min_length_ms:
+            st.warning(f"Chunk {i} is too short to be processed.")
+            continue
+        
+        with NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
+            chunk.export(temp_audio_file.name, format="wav")
+            temp_audio_file_path = temp_audio_file.name
+
+        transcription = transcribe(temp_audio_file_path)
+        if transcription:
+            transcriptions.append(transcription)
+            st.write(f"Transcription for chunk {i}: {transcription}")
+
+        os.remove(temp_audio_file_path)
+    return " ".join(transcriptions)
+
+def save_transcription_to_docx(transcription, audio_file_path):
+    """
+    Save the transcription as a .docx file.
+
+    Args:
+    transcription (str): Transcribed text.
+    audio_file_path (str): Path to the original audio file for naming purposes.
+
+    Returns:
+    str: Path to the saved .docx file.
+    """
+    # Extract the base name of the audio file (without extension)
+    base_name = os.path.splitext(os.path.basename(audio_file_path))[0]
+    
+    # Create a new file name by appending "_full_transcription" with .docx extension
+    output_file_name = f"{base_name}_full_transcription.docx"
+    
+    # Create a new Document object
+    doc = Document()
+    
+    # Add the transcription text to the document
+    doc.add_paragraph(transcription)
+    
+    # Save the document in .docx format
+    doc.save(output_file_name)
+    
+    return output_file_name
+
+st.title("Audio Transcription with OpenAI's Whisper")
+
+# uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "ogg", "m4a"])
+uploaded_file = st.file_uploader("Upload an audio or video file", type=["wav", "mp3", "ogg", "m4a", "mp4", "mov"])
+
+
+if 'transcription' not in st.session_state:
+    st.session_state.transcription = None
+
+if uploaded_file is not None and st.session_state.transcription is None:
+    st.audio(uploaded_file)
+    
+    # Save uploaded file temporarily
+    file_extension = uploaded_file.name.split(".")[-1]
+    original_file_name = uploaded_file.name.rsplit('.', 1)[0]  # Get the original file name without extension
+    temp_audio_file = f"temp_audio_file.{file_extension}"
+    with open(temp_audio_file, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Split and process audio
+    with st.spinner('Transcribing...'):
+        chunk_length_ms = get_chunk_length_ms(temp_audio_file, target_size_mb=1)
+        audio_chunks = split_audio(temp_audio_file, chunk_length_ms)
+        transcription = process_audio_chunks(audio_chunks)
+        if transcription:
+            st.session_state.transcription = transcription
+            st.success('Transcription complete!')
+
+            # Save transcription to a Word (.docx) file
+            output_docx_file = save_transcription_to_docx(transcription, uploaded_file.name)
+            st.session_state.output_docx_file = output_docx_file
+            
+    # Clean up temporary file
+    if os.path.exists(temp_audio_file):
+        os.remove(temp_audio_file)
+
+if st.session_state.transcription:
+    st.text_area("Transcription", st.session_state.transcription, key="transcription_area_final")
+    
+    # Download the transcription as a .docx file
+    with open(st.session_state.output_docx_file, "rb") as docx_file:
+        st.download_button(
+            label="Download Transcription (.docx)",
+            data=docx_file,
+            file_name=st.session_state.output_docx_file,
+            mime='application/vnd.openxmlformats-officedocument.wordprocessingml.document'
+        )
+