Creating an AI-Driven Podcast Transcription Service with Ollama

What is Ollama?

Why Use AI-Driven Transcription?

• Speed: Traditional transcription methods can take hours or even days. AI-driven systems can transcribe recordings within minutes.
• Cost-Effective: Costs associated with manual transcription services can add up rapidly. Using AI can significantly reduce expenses.
• Accessibility: By providing transcripts, you make content more accessible to people who are deaf or hard of hearing.
• SEO Benefits: Transcriptions can enhance the searchability of your content, attracting more listeners to your podcast.

Setting Up Your Environment

1. Install Ollama: Begin by downloading Ollama from its GitHub page.
2. Install Whisper: Use the Whisper model, which is essential for transcribing audio. You can find it here.
3. Python Environment: Create a Python virtual environment to manage all dependencies easily.
4. Other Dependencies: You will also need libraries like
   1 numpy
   ,
   1 sounddevice
   , and
   1 nltk
   . Run the following commands to install them:
   1 2 bash pip install numpy sounddevice nltk
5. Install Bark: For producing synthesized speech output, you’ll need Bark as well, available here.

Creating the Transcription Service

Step 1: Initialize Ollama and Whisper

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import whisper

# Load the Whisper model
def load_whisper_model():
    model = whisper.load_model("base.en")  # You can choose other sizes for better performance
    return model

whisper_model = load_whisper_model()

Step 2: Transcribing Audio

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def transcribe_audio(file_path):
    result = whisper_model.transcribe(file_path, fp16=False)
    return result["text"]

Step 3: User Interaction

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import os

def user_upload():
    audio_file = input("Please enter the path to the audio file you want to transcribe:")
    if os.path.isfile(audio_file):
        print("Transcribing...")
        transcription = transcribe_audio(audio_file)
        print(f"Transcription: {transcription}")
    else:
        print("File not found. Please try again.")

Step 4: Adding Text-to-Speech

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from bark import TextToSpeechService

tts_service = TextToSpeechService()

def synthesize_and_play(text):
    sample_rate, audio_array = tts_service.synthesize(text)
    play_audio(sample_rate, audio_array)

def play_audio(sample_rate, audio_array):
    sd.play(audio_array, sample_rate)
    sd.wait()

Testing and Improvements

• Check different audio qualities: Test with audio recorded in different environments to see how accurately the service performs.
• Evaluate speaker variation: Transcribe recordings with multiple voices to assess if the model accurately captures each speaker.
• Iterate upon feedback: Gather user feedback to understand how well the tool performs and make adjustments.

Future Enhancements

• Speaker Diarization: Incorporating speaker identification to differentiate between various speakers in a podcast.
• Cloud Integration: If desired, you could add cloud storage capabilities to save and manage transcriptions without local dependencies.
• Search Feature: Create a searchable database that allows users to easily find certain transcriptions or keywords within them.

Promote Your Service with Arsturn

• With Arsturn, you can effortlessly craft conversational AI chatbots tailored specifically to your needs, empowering your audience interaction before they even log in. Join thousands who are utilizing the power of Conversational AI to build meaningful connections across various digital channels.
• Their user-friendly platform ensures you can create intelligent chatbots without needing coding skills!
• Plus, you can utilize your existing data to make these bots unique to your brand while saving on development costs.
• Jump into the future of digital engagement with Arsturn and revolutionize your audience experience!

Conclusion