Transcribe Audio2Text + Resumo

Audio should be in mp3 format. If you have a different format, you can convert it using ffmpeg.

ffmpeg -i audio.mp4 -vn -acodec libmp3lame -q:a 2 audio.mp3

ffmpeg -i audio.m4a -c:a libmp3lame -q:a 2 audio.mp3

Install dependencies

If you don't have uv installed, you can install it curling the following command Installing uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

After that in order to run the application, you need to install the required dependencies. You can do this using uv (a virtual environment manager).

uv sync

P.S.: Take a look at the pyproject.toml file, in particular the [[tool.uv.index]] section, because in my machine I had to add the pytorch-cu118 index to be able to install the torch package. If you have a new NVIDIA GPU, you might need to change the index URL to match your CUDA version (or remove it if you don't need it).

Install ffmpeg if you don't have it installed yet. This is required to convert audio files to mp3 format.

sudo apt install ffmpeg

Create a .env file

Create a .env file in the root directory of the project.

This file should contain the Hugging Face model names and other configurations. Here is an example of what your .env file might look like:

The bellow values are the default ones used in the project, but you can change them to use different models or configurations as needed.

HUGGINGFACE_LLM_MODEL=unsloth/Phi-4-mini-reasoning-unsloth-bnb-4bit
HUGGINGFACE_WHISPER_MODEL=openai/whisper-large-v3-turbo

Run as a command line

You can run the script to transcribe audio files directly from the command line. Make sure you have an audio file ready in the audio directory.

uv run python -m main --sample audio/audio.mp3 --output_file audio/transcribed.txt

Run the server

Another way to use the application is to run it as a server. This allows you to send audio files via HTTP requests and receive transcriptions in response.

uv run uvicorn app:app --host 0.0.0.0 --port 8000 --reload