fix: incorrect whisper model env references

README.md

@@ -3,7 +3,7 @@
 Features:
 - GPU and CPU support.
 - Easily deployable using Docker.
-- Configurable through environment variables (see [config.py](./faster_whisper_server/config.py)).
+- **Configurable through environment variables (see [config.py](./faster_whisper_server/config.py))**.
 - OpenAI API compatible.
 
 Please create an issue if you find a bug, have a question, or a feature suggestion.

examples/live-audio/script.sh

@@ -7,12 +7,12 @@ set -e
 # ffmpeg -y -hide_banner -loglevel quiet -i audio.mp3 -ac 1 -ar 16000 -f s16le -acodec pcm_s16le audio.pcm
 # rm -f audio.mp3
 
-export WHISPER_MODEL=Systran/faster-distil-whisper-large-v3 # or Systran/faster-whisper-tiny.en if you are running on a CPU for a faster inference.
+export WHISPER__MODEL=Systran/faster-distil-whisper-large-v3 # or Systran/faster-whisper-tiny.en if you are running on a CPU for a faster inference.
 
 # Ensure you have `faster-whisper-server` running. If this is your first time running it expect to wait up-to a minute for the model to be downloaded and loaded into memory. You can run `curl localhost:8000/health` to check if the server is ready or watch the logs with `docker logs -f <container_id>`.
-docker run --detach --gpus=all --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER_MODEL=$WHISPER_MODEL fedirz/faster-whisper-server:latest-cuda
+docker run --detach --gpus=all --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER__MODEL=$WHISPER__MODEL fedirz/faster-whisper-server:latest-cuda
 # or you can run it on a CPU
-# docker run --detach --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER_MODEL=$WHISPER_MODEL fedirz/faster-whisper-server:latest-cpu
+# docker run --detach --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER__MODEL=$WHISPER__MODEL fedirz/faster-whisper-server:latest-cpu
 
 # `pv` is used to limit the rate at which the audio is streamed to the server. Audio is being streamed at a rate of 32kb/s(16000 sample rate * 16-bit sample / 8 bits per byte = 32000 bytes per second). This emulutes live audio input from a microphone: `ffmpeg -loglevel quiet -f alsa -i default -ac 1 -ar 16000 -f s16le`
 # shellcheck disable=SC2002

examples/youtube/script.sh

@@ -3,12 +3,12 @@
 set -e
 
 # NOTE: do not use any distil-* model other than the large ones as they don't work on long audio files for some reason.
-export WHISPER_MODEL=Systran/faster-distil-whisper-large-v3 # or Systran/faster-whisper-tiny.en if you are running on a CPU for a faster inference.
+export WHISPER__MODEL=Systran/faster-distil-whisper-large-v3 # or Systran/faster-whisper-tiny.en if you are running on a CPU for a faster inference.
 
 # Ensure you have `faster-whisper-server` running. If this is your first time running it expect to wait up-to a minute for the model to be downloaded and loaded into memory. You can run `curl localhost:8000/health` to check if the server is ready or watch the logs with `docker logs -f <container_id>`.
-docker run --detach --gpus=all --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER_MODEL=$WHISPER_MODEL fedirz/faster-whisper-server:latest-cuda
+docker run --detach --gpus=all --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER__MODEL=$WHISPER__MODEL fedirz/faster-whisper-server:latest-cuda
 # or you can run it on a CPU
-# docker run --detach --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER_MODEL=$WHISPER_MODEL fedirz/faster-whisper-server:latest-cpu
+# docker run --detach --publish 8000:8000 --volume ~/.cache/huggingface:/root/.cache/huggingface --env WHISPER__MODEL=$WHISPER__MODEL fedirz/faster-whisper-server:latest-cpu
 
 # Download the audio from a YouTube video. In this example I'm downloading "The Evolution of the Operating System" by Asionometry YouTube channel. I highly checking this channel out, the guy produces very high content. If you don't have `youtube-dl`, you'll have to install it. https://github.com/ytdl-org/youtube-dl
 youtube-dl --extract-audio --audio-format mp3 -o the-evolution-of-the-operating-system.mp3 'https://www.youtube.com/watch?v=1lG7lFLXBIs'

faster_whisper_server/config.py

@@ -162,8 +162,8 @@ class Config(BaseSettings):
 
     Pydantic will automatically handle mapping uppercased environment variables to the corresponding fields.
     To populate nested, the environment should be prefixed with the nested field name and an underscore. For example,
-    the environment variable `LOG_LEVEL` will be mapped to `log_level`, `WHISPER_MODEL` to `whisper.model`, etc.
-    """
+    the environment variable `LOG_LEVEL` will be mapped to `log_level`, `WHISPER__MODEL`(note the double underscore) to `whisper.model`, etc.
+    """  # noqa: E501
 
     model_config = SettingsConfigDict(env_nested_delimiter="__")