Simon Willison’s Weblog

PDF to Podcast (via) At first glance this project by Stephan Fitzpatrick is a cute demo of a terrible sounding idea... but then I tried it out and the results are weirdly effective. You can listen to a fake podcast version of the transformers paper, or upload your own PDF (with your own OpenAI API key) to make your own.

It's open source (Apache 2) so I had a poke around in the code. It gets a lot done with a single 180 line Python script.

When I'm exploring code like this I always jump straight to the prompt - it's quite long, and starts like this:

Your task is to take the input text provided and turn it into an engaging, informative podcast dialogue. The input text may be messy or unstructured, as it could come from a variety of sources like PDFs or web pages. Don't worry about the formatting issues or any irrelevant information; your goal is to extract the key points and interesting facts that could be discussed in a podcast. [...]

So I grabbed a copy of it and pasted in my blog entry about WWDC, which produced this result when I ran it through Gemini Flash using llm-gemini:

cat prompt.txt | llm -m gemini-1.5-flash-latest

Then I piped the result through my ospeak CLI tool for running text-to-speech with the OpenAI TTS models (after truncating to 690 tokens with ttok because it turned out to be slightly too long for the API to handle):

llm logs --response | ttok -t 690 | ospeak -s -o wwdc-auto-podcast.mp3

And here's the result (3.9MB 3m14s MP3).

It's not as good as the PDF-to-Podcast version because Stephan has some really clever code that uses different TTS voices for each of the characters in the transcript, but it's still a surprisingly fun way of repurposing text from my blog. I enjoyed listening to it while I was cooking dinner.

# 13th June 2024, 1:03 am / pdf, podcasts, projects, text-to-speech, ai, openai, generative-ai, llms, gemini

Ultravox (via) Ultravox is "a multimodal Speech LLM built around a pretrained Whisper and Llama 3 backbone". It's effectively an openly licensed version of half of the GPT-4o model OpenAI demoed (but did not fully release) a few weeks ago: Ultravox is multimodal for audio input, but still relies on a separate text-to-speech engine for audio output.

You can try it out directly in your browser through this page on AI.TOWN - hit the "Call" button to start an in-browser voice conversation with the model.

I found the demo extremely impressive - really low latency and it was fun and engaging to talk to. Try saying "pretend to be a wise and sarcastic old fox" to kick it into a different personality.

The GitHub repo includes code for both training and inference, and the full model is available from Hugging Face - about 30GB of .safetensors files.

Ultravox says it's licensed under MIT, but I would expect it to also have to inherit aspects of the Llama 3 license since it uses that as a base model.

# 10th June 2024, 5:34 am / text-to-speech, ai, generative-ai, llama, homebrew-llms, llms

Expanding on how Voice Engine works and our safety research. Voice Engine is OpenAI's text-to-speech (TTS) model. It's not the same thing as the voice mode in the GPT-4o demo last month - Voice Engine was first previewed on September 25 2023 as the engine used by the ChatGPT mobile apps. I also used the API version to build my ospeak CLI tool.

One detail in this new explanation of Voice Engine stood out to me:

In November of 2023, we released a simple TTS API also powered by Voice Engine. We chose another limited release where we worked with professional voice actors to create 15-second audio samples to power each of the six preset voices in the API.

This really surprised me. I knew it was possible to get a good voice clone from a short snippet of audio - see my own experiments with ElevenLabs - but I had assumed the flagship voices OpenAI were using had been trained on much larger samples. Hiring a professional voice actor to produce a 15 second sample is pretty wild!

This becomes a bit more intuitive when you learn how the TTS model works:

The model is not fine-tuned for any specific speaker, there is no model customization involved. Instead, it employs a diffusion process, starting with random noise and progressively de-noising it to closely match how the speaker from the 15-second audio sample would articulate the text.

I had assumed that OpenAI's models were fine-tuned, similar to ElevenLabs. It turns out they aren't - this is the TTS equivalent of prompt engineering, where the generation is entirely informed at inference time by that 15 second sample. Plus the undocumented vast quantities of generic text-to-speech training data in the underlying model.

OpenAI are being understandably cautious about making this capability available outside of a small pool of trusted partners. One of their goals is to encourage the following:

Phasing out voice based authentication as a security measure for accessing bank accounts and other sensitive information

# 8th June 2024, 5:48 pm / ethics, text-to-speech, ai, openai, generative-ai

LLaMA voice chat, with Whisper and Siri TTS. llama.cpp author Georgi Gerganov has stitched together the LLaMA language model, the Whisper voice to text model (with his whisper.cpp library) and the macOS “say” command to create an entirely offline AI agent that he can talk to with his voice and that can speak replies straight back to him.

# 27th March 2023, 9:06 pm / macosx, text-to-speech, ai, generative-ai, whisper, llama, homebrew-llms, llms