Week 05 Progress Report by Mebin J Thattil

~ Interesting demos this week!
These blogs can be found in the SugarLabs website. The content is the same in both places.~

Project: Speak Activity
Mentors: Chihurumnaya Ibiam, Kshitij Shah
Assisting Mentors: Walter Bender, Devin Ulibarri
Reporting Period: 2025-06-29 - 2025-07-06

Goals for This Week

• Goal 1: Test out different Kokoro voices
• Goal 2: Integrate the SLM into Speak

This Week’s Progress

1. Hey Kokoro, you sound different today

This week, I tested out different voices of Kokoro in two different ways:

1. I tested them inside Speak, within Sugar, and it worked. It still uses the hacky way of creating a temporary WAV file and then playing it via GStreamer, but it works. Streaming will be introduced soon.

   Under-the-hood changes:

   • Kokoro currently uses the following stack:

   Text → Kokoro → handle phonemes via G2P engine → Misaki (primary G2P) → fallback → Espeak-ng

   • Speak already uses Espeak.
   • So I swapped Misaki's fallback with Espeak (instead of Espeak-ng) to reduce dependencies.
   • I’ve yet to encounter a case that triggers the fallback, as Misaki is already quite good.

2. I deployed a web app that lets you generate and mix audio. You can try it out here.

   • The primary reason this was built as a web app is so that we can get kids to test this out and having things as a web app makes it easier. It's cruical for us to get real world feedback before proceeding with the implementation.
   • This web app allows you to try out a plethora of different voices and also mix and match different voices to create basically infinite combinations. It's truly amazing the kind of voices you can create with this.

   

   • It's a container app, meaning both the frontend (Streamlit) and backend (Kokoro - FastAPI) run as separate Docker containers hosted on Azure.
   • The Kokoro - FastAPI exposes an OpenAI-compatible API to generate audio.
   • This allows us to stream audio output to a client using OpenAI’s Python libraries, like so:

from openai import OpenAI

client = OpenAI(
    base_url="http://my_kokoro_backend:8880/v1", api_key="not-needed"
)

with client.audio.speech.with_streaming_response.create(
    model="kokoro",
    voice="af_sky+af_bella",  # single or multiple voicepack combo
    input="Hello world!"
) as response:
    response.stream_to_file("output.mp3")

• Another potential application of this setup (deploying as separate containers) is to bring Kokoro into pre-existing Speak with minimal dependency changes.
• This would work on low end machines with a stable internet connection, as audio is generated server-side and streamed to the client.
• While this wasn’t in the original plan, the current architecture makes it a possibility worth exploring.
• The original plan was to have an offline only version of Kokoro that Speak uses for it's voice.

Understanding and playing with Kokoro:

• How voice names are read

  Kokoro has a catalog of voices like af_bella or af_sky.
  The first letter of the voice name indicates the language:
  - a for American English
  - b for British English
  - j for Japanese
  - m for Mandarin Chinese
  - s for Spanish
  - f for French
  - h for Hindi
  - i for Italian
  - p for Brazilian Portuguese

  The second letter indicates gender:
  - m for male
  - f for female

  So af_bella would be American English, female.

• Different language options:

  These do two things:
  1. Speak the text with an accent.
  2. Handle the language-specific text more effectively.

  Example: Using hf_alpha with both Hindi and English:
  Input:

  नमस्ते, आप कैसे हैं? I can also speak in English as well!

  Output audio:
  

  I speak Hindi, and I can confirm it sounds correct.
  This is a great example of how Kokoro can help kids learning new languages by combining accent and language aware pronunciation.

• Voice mixing:

  • You can mix any of the available Kokoro voices.
  • Mixing is done by assigning weights (between 0 and 1) to each voice.
  • For example, mixing two voices with 0.5 weight each gives a 50-50 blend.
  • Mixing three voices with weights 0.6, 0.3, and 0.1 gives a 60-30-10 blend.
  • This allows for basically infinite voice combinations.

  This could be super useful for building personas in Speak, as each persona could have a unique voice!

  

Links:

• Streamlit web app
• Kokoro - Fast API
• Streamlit Source Code

2. New brains for Speak

• I used the previously quantized Tiny Llama 1B GGUF model with llama-cpp-python inside Sugar, using it as the backend for Speak’s chatbot.

• Using llama-cpp gave great performance boosts, but there's a catch:

  • Llama-cpp needs to be built for each OS and architecture.
  • This complicates distribution and packaging.
  • We can’t shift the build process to the client since most of them are using constrained hardware.

• So I tried a different approach: fine-tune a smaller model that doesn't need such optimizations.

• I chose Llama 135M (yes, M, not B 😄) and fine-tuned it on my educational conversation dataset.
• I cleaned and extended the dataset (cleanup scripts are in the repo).
• The model was fine-tuned on AWS Sagemaker. You can find the files here.
• The unquantized size is ~500MB, so post-quantization it should shrink further.

But...

• I didn’t proceed with quantization because the raw performance post finetuning wasn’t up to expectations.
• So next step: gather better data, retrain, and re-evaluate.
• Will also discuss next directions with mentors after the next fine-tune round.

Next Week’s Roadmap

• Work on mechanics for voice switching and personas inside sugar.
• Improve dataset quality.
• Fine-tune the model again and evaluate performance.

Acknowledgments

Thank you to my mentors, the Sugar Labs community, and fellow GSoC contributors for their ongoing support.