Using OpenAI's Whisper API for speech-to-text

This week OpenAI released their Whisper speech-to-text hosted API. I’ve previously experimented with hosting Whisper locally but it was too slow on my CPU. (See previous discussion)

To test out the API I wrote a script to send the speech to OpenAI:

#!/usr/bin/env bash

# First argument is OpenAi API key

# WAV data is avaiable via STDIN
cat > $wav_file

curl \
  -X POST \
  -H "Authorization: Bearer $1" \
  -H "Content-Type: multipart/form-data" \
  -F file=@$wav_file \
  -F model=whisper-1 \
  -F response_format=text \
  | sed -e "s/[[:punct:]]//g" | sed -e "s/\(.*\)/\L\1/"

# sed removes punctuation and makes lower case

# Transcription on STDOUT

Create a file called in en/ profile. When you create the file don’t forget to chmod +x to make the file executable.

Then in Rhasspy settings, under Speech to Text choose Local Command. In Program paste /profiles/en/ and in Arguments paste in your OpenAI API key.

Save and restart Rhasspy.

So far the speech recognition has been good, and latency is acceptable. Hopefully the speech recognition is much better than DeepSpeech / Vosk / Kaldi especially in my noisy kitchen.

I would prefer that my entire setup ran locally, but using the API is a quick way to test if I want to invest more time in making a local version of Whisper faster.

How did your experiment go?

Given the Whisper is something you can run locally (OpenAI has published it back in Oct-Dec last year) it would be much more interesting to see someone will invest his time to extend the Rhasspy docker image to have it as one of the SST engines right out of the box.

It’s also worth to mention, there’s a project by a talented guy Georgi Gerganov called “Whisper.cpp” (GitHub - ggerganov/whisper.cpp: Port of OpenAI's Whisper model in C/C++). To my limited understanding, it’s using the same trained model (basically pretty same files with weights) from OpenAI’ Whisper, but the actual implementation is on C++ instead of Python, which should be performing better on a underpowered PCs or SBCs

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tl;dr I will continue using Whisper because it’s much better in echoy/noisy environments.

Overall, I am very happy with the performance. Equally as good as Google Assistant / Google Nest devices, and far better than the built in ASR (Kaldi, DeepSpeech, Vosk.) My kitchen has a lot of echo, and when it’s busy (microwave, extractor fan) the speech-to-text would fail unless I was < 0.5 m from the mic. Now I can be 2-3 m away and it works fine.

The API performance is fine, too. Not noticeably slower than running the large ASR models locally.

As alx84 said, there are local Whisper models that run on CPU. I tested Whisper.cpp, but the large model was still too slow for realtime. There is also faster-whisper, but I have not tested that, although I believe Rhasspy 3 will support it.

I found that the smaller Whisper models incorrectly transcribed short utterances (“activate relax”), maybe because there was not enough context, or the training data was from podcasts and videos, not voice assistants? However with the API (running the largest model?) I have not had any issues.


Thanks. Giving it a go.

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Do you happen know of any way to send the text transcribed by Whisper back to Home Assistant?

I would like to be able to say “ChatGPT” followed by a question and have the question routed to HA.

It seems to be possible to do it by rewriting your shell script.

Would you mind sharing what mic or mic array you’re using that gives you that 2-3m range?

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The mic/speakers I am using are the Jabra 410 and Jabra 510. I got them 2nd hand.

The primary difference between the two is that the 510 has Bluetooth and the 410 does not. I do not use the Bluetooth, but I guess you could have the speaker in a room and e.g. the Pi somewhere else within Bluetooth range. That way the speaker could be on a coffee table and still look neat. Not sure Bluetooth is stable enough long term, and linux sound config is frustrating enough without adding Bluetooth into the mix.

The nice thing about a conference speaker is even with music playing it still detected voice fine. When playing back the recorded voice wav file I can barely hear the music even though it was 50% volume. The hardware does a good job of removing audio played over the speaker, as a good conference speaker should.

Downside is the speaker is not great for music – it’s designed for voice frequencies.

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I’m not sure how familiar you are with Rhasspy, forgive me if I am exlaining things you already know.

Home Assistant has an addon for Rhasspy. See Rhasspy - Home Assistant This will allow Home Assistant to “handle intents” for controlling your home.

If you want a custom intent handler, you can use Node-RED. Have a look at Rhasspy “Intent Handling”, you can use Home Assistant, http (a web hook), or local command (a shell script, pyton program, …)

For Rhasspy to activate when you say the “wake word” like “chatGPT” you’ll need to program your own wake word. Rhasspy has built in wake words like “hey mycroft”, “porcupine”, etc. To use a csutom wake word you’ll need to train one. You can do this online using picovoice.

In Rhasspy settings - wake word: I’ve had the most success with Mycroft Precise and hey-mycroft-2.

I’d suggest following the Rhasspy getting started tutorials and get a solid working foundation using mostly the defaults, before adding complexity with custom wake words, whisper, chatgpt.

Thanks for the detailed reply. Very helpful!

Thanks I’ve used whisper.cpp during work on this: GitHub - hbarnard/mema it’s an experimental setup/project for older people to record memories and photos without a lot of keyboard activity. There’s a partial write up here: Working with the MeMa 3.0 project : Horizon

Anyway, for ‘short stories’ I found I could do real-time transcription on the laptop install but not on Pi4 with Ubuntu. Since this is pretty specialised, I now have a cron on the Pi4 that scoops up current audio recordings and transcribes them as a background task. I have one Orange Pi5 that I haven’t put into service yet, I’ll report on how that goes a little later in the year.


Sorry friends, forgot to say there’s also a project called April-ASR (GitHub - abb128/april-asr: Speech-to-text library in C). It’s used in “Live Captions” application (GitHub - abb128/LiveCaptions: Linux Desktop application that provides live captioning) from the same developer and it is giving shockingly good results for the real-time speech recognition: the recognition quality is high while keeping an ultra low performance impact. It’s worth to give it a shot as well.