turbo model release

[jongwook]

We’re releasing a new Whisper model named large-v3-turbo, or turbo for short. It is an optimized version of Whisper large-v3 and has only 4 decoder layers—just like the tiny model—down from the 32 in the large series.

This work is inspired by Distil-Whisper^[1], where the authors observed that using a smaller decoder can greatly improve transcription speed while causing minimal degradation in accuracy. Unlike Distil-Whisper, which used distillation to train a smaller model, Whisper turbo was fine-tuned for two more epochs over the same amount of multilingual transcription data used for training large-v3, i.e. excluding translation data, on which we don’t expect turbo to perform well.

Across languages, the turbo model performs similarly to large-v2, though it shows larger degradation on some languages like Thai and Cantonese. Whisper turbo performs better on FLEURS, which consists of cleaner recordings than Common Voice. The figure below shows the turbo model’s performance on the subset of languages in the Common Voice 15 and FLEURS datasets where large-v3 scored a 20% error rate or lower.

Combined with a recent patch (#2359) to use F.scaled_dot_product_attention when available, the ASR speed of turbo is faster than what tiny used to be:

This puts turbo at the "best of both worlds" when comparing speed and accuracy:

---

You can update the Python package to version 20240930 or later to use the new model:

pip install -U openai-whisper

With this upgrade, the whisper CLI command now defaults to the turbo model:

whisper audio.wav  # will use `--model turbo` by default

---

References

[1] Gandhi, S., von Platen, P., & Rush, A. M. (2023). Distil-whisper: Robust knowledge distillation via large-scale pseudo labelling. arXiv preprint arXiv:2311.00430.

[mvoodarla]

Thanks for the amazing model. We now support the new whisper-large-v3-turbo on Sieve!

Use it via sieve/speech_transcriber: https://www.sievedata.com/functions/sieve/speech_transcriber
Use sieve/whisper directly: https://www.sievedata.com/functions/sieve/whisper

Just set speed_boost to True. API guide is under "Usage Guide" tab.

[FurkanGozukara]

it is amazing but i really want to to see lower WER rather than speed up :( I hope it arrives asap

[AmgadHasan]

Thanks you @jongwook for releasing this.

For finetuning, what learning rate would you recommend? We've been using 1e-5 with large-v2 and large-v3 and it's been working well. Should we change it for turbo?

[FurkanGozukara]

@jongwook what do we use how do we train? example code repo?

[jongwook]

HF has an excellent article on this: https://huggingface.co/blog/fine-tune-whisper and also see #64

[FurkanGozukara]

@jongwook thank you so much. will this notebook work for newest turbo model? https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb

[asr-lord]

@jongwook thank you so much. will this notebook work for newest turbo model? https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb

Yes, you should change openai/whisper-small to openai/whisper-large-v3-turbo in the notebook.

[gslin1224]

Hi everyone,

I have a question about Whisper's translate function and fine-tuning.

If I fine-tune the Whisper large model using a dataset that includes mixed Chinese and English audio, along with bilingual transcripts (Chinese and English), will this fine-tuning process affect the model's translate functionality? Specifically, after fine-tuning, will I still be able to use Whisper's built-in translation feature to translate Chinese audio into English?

Thanks in advance for any insights!

[flishwang]

Does the turbo model still support translate task? I tried it but it seems outputing the raw languages instead of English. Does any one meet this issue?

[Jiltseb]

It's not finetuned for translation task, so it won't perform well as @jongwook already written.

[Vaibhavs10]

Hi all, I'm VB from the Open Source audio team at Hugging Face. Thanks a ton @jongwook and team for Whisper-large-v3-turbo - It's amazing to see the results.

For reference, the transformers compatible weights are here: https://huggingface.co/openai/whisper-large-v3-turbo

You can reference them via a simple snippet:

import torch
from transformers import pipeline

model_id = "openai/whisper-large-v3-turbo"

pipe = pipeline(
    "automatic-speech-recognition",
    model=model_id, 
    torch_dtype=torch.float16,
    device="cuda" # replace with `mps` for Mac
)

result = pipe("<file_name.mp3>") # pass `return_timestamps=True` for timestamps
print(result["text"])

You can also fine-tune it for your own use-case as well: https://huggingface.co/blog/fine-tune-whisper

Looking forward to the next updates to the series 🤗

[FurkanGozukara]

@Vaibhavs10 any easy tool guide tutorial to fine tune? i would like to fine tune on my video which as fully accurate subtitles - written by me : https://www.youtube.com/SECourses

[AmgadHasan]

@Vaibhavs10
@jongwook
What is the native precision of the model? The HF version is fp16. Shouldn't it be bf16?

[Herz3h]

Amazing work !

Thank you. Is there any ongoing work regarding the hallucination of whisper ? This for us seems to be the biggest downside of using it. Sometimes it also gets stuck in a loop and keeps repeating stuff (happens more often in v3, therefore we use v2 which seems better in French language at least)

[gongouveia]

Hello @Herz3h
Check about enabling VAD, supressing silent instances should improve your hallucinations.
You can activate whisper's VAD or use a more robust pre processing with it (e.g. Silero).

[jhj0517]

You can use Silero VAD to detect voice parts, or UVR to remove other noise (such as music) from the audio.

In Whisper-WebUI such pre-processing pipeline is implemented for lower WER.
Of course, since it uses submodels in the pipeline, it slows down the whole process when you use it.

[gongouveia]

@Herz3h @jhj0517 Beware that using noise removal (such as Demucs in UVR) although enhances hearing quality, it adds impurities and artifacts to the frequency domain, significantly decreasing translation quality (higher WER/CER).
Whisper is more than enough capable of translating with some background noise, no need of noise removal in most cases.

[jhj0517]

I used MDX models for noise removal and in my test it gave me better results in most cases.

I haven't tested with Demucs, so I don't know if it could negatively affect the result.

But yes, there's still a possibility that submodels can also cause hallucinations, so you might want to compare results with submodels / without submodels.

I prefer to use them when the audio has such noise, because in my experience it gives me better results in most cases.

[xmoiduts]

I recommend cutting the beginning of the media file and making the audio appear at the start of the media, this suppresses hallucinations related to the topic but should not exist, and, in word-precise mode, shorten the 'words' so that they are no longer multi-sentences per word.

[FlorDonnaSanders]

Any chance for availability of this model over the offical OpenAI API anytime soon?

[simpthy]

[ryanheise]

@simpthy
According to the v3 release, some languages without spaces to separate words, such as Japanese and Mandarin, were measured in CER rather than WER. I think Korean was also measured in CER (Korean does have spaces, although it doesn't use spaces to separate everything, and it's comparable in syntax to Japanese.)

[jongwook]

Yes to what Ryan said; the languages in italic used CER. I've switched to use CER for Korean after realizing that WER is often too harsh because the use of spaces is often arbitrary in many Korean words. CER also fits better with the agglutinative nature of the language.

I'm not sure what exactly contributed to the better accuracy in the Taiwan subset and haven't done any deeper analysis; the reason could be anything from - the subset had more sentences that are easier to recognize, the recording quality was better in general, or the model actually better understands the way people speak Mandarin in Taiwan.

[ryanheise]

This may be helpful: https://huggingface.co/learn/audio-course/en/chapter5/evaluation#which-metric-should-i-use

In general, the WER is used far more than the CER for assessing speech systems. This is because the WER requires systems to have greater understanding of the context of the predictions. In our example, “sit” is in the wrong tense. A system that understands the relationship between the verb and tense of the sentence would have predicted the correct verb tense of “sat”. We want to encourage this level of understanding from our speech systems. So although the WER is less forgiving than the CER, it’s also more conducive to the kinds of intelligible systems we want to develop. Therefore, we typically use the WER and would encourage you to as well! However, there are circumstances where it is not possible to use the WER. Certain languages, such as Mandarin and Japanese, have no notion of ‘words’, and so the WER is meaningless. Here, we revert to using the CER.

[ushnah]

where can i find WER for urdu language (turbo model)?

[jongwook]

Turbo scored 26.5% (CV15) and 21.9% (FLEURS) on Urdu

[MedAymenF]

@jongwook What about Kabyle (kab)?

[zx3777]

Thanks, the biggest drawback of Whisper right now is that the timeline is inaccurate. Subtitles generated by the large series appear much earlier than they should. Tested in korean.

[ioudove]

Could you please find a way to solve the old issues of hallucinations and incorrect timestamps in Whisper?

[LaurinmyReha]

maybe give this one a shot. Should be a bit more accurate and deliver better timestamps, atleast for english:

https:/nyrahealth/CrisperWhisper

[oswaldoludwig]

To speed up decoding without hurting performance too much, we should have a fat, shallow decoder, to take advantage of parallel processing. Fewer layers in the decoder, as they did, but more attention heads per layer, this could be computed in parallel, but the layers cannot (i.e. we can only compute a layer after we have its input, which is the output of the previous one). By keeping the encoder deep, you ensure that the input is thoroughly processed and rich in features. Then, using a fat, shallow decoder can speed up the auto-regressive decoding process, which calls the decoder block many times.

[Jeong-Haneul]

I’ve noticed an issue with the no_speech_prob variable when using the Turbo model. While it works correctly with the large model, it doesn’t seem to function as expected in the Turbo model. Please look into this.

Thank you!
'no_speech_prob': 1.903758928278876e-09
in turbo model.

[FurkanGozukara]

@jongwook any idea to fix this bad broken Turbo model?

on my newest video whisper turbo performed really really bad - it was so broken that unfixable manually - i had to use large-v3 and fix it

here turbo model transcription :
turbo model.txt

And here fully manually fixed subtitles video (you can download and test) : https://youtu.be/URnOHbmuKWs

here manually fixed subtitles :
manually fixed.txt

this is why we need a better WER model not a faster one - it is unusable without huge effort

compare both and you won't believe difference it is just huge sadly

here example 2 case

below one turbo

below one manually fixed

[ryanheise]

Here is my final solution: whisper audio.mp3 --initial_prompt "Include all punctuation, new paragraph, including (brackets), new paragraph, in the transcription" --word_timestamps True.

It's worth noting that the reason this may help is not because you are instructing Whisper to include all punctuation (it is not like a ChatGPT prompt), but simply because your prompt contains examples of punctuation. All that happens when you supply the initial prompt is that you are telling whisper the transcript of what came just before the current point in the audio. So here, Whisper will think that if we rewound the audio 30 seconds, the previous part of audio would have been transcribed literally as "Include all punctuation, new paragraph, including (brackets), new paragraph, in the transcription". If that prior transcript contains punctuation, it makes it more likely that Whisper will include punctuation in the transcript of the next chunk of audio that follows (Perhaps you can design a better prompt with that understanding). This also works for vocabulary: if you incorporate certain vocabulary into the prompt, Whisper will be more likely to use the spelling you have provided in the next part of the transcript.

[FurkanGozukara]

thank you for replies @ryanheise and @AeneasZhu . i was already using this :

--initial_prompt ""Welcome to our Youtube channel.""

but it still loses punctuating in many cases which i hate

I haven't tried --word_timestamps True with turbo gonna try

[ryanheise]

Your prompt doesn't contain much punctuation (only one full stop, and no commas), so you might consider making your prompt longer so that it has enough punctuation, to give it a feel for what you want. It is still possible for the initial prompt to eventually become a distant/forgotten memory in long audio files, so if you're experiencing that behaviour, you might want to test this PR: #2343

[FurkanGozukara]

@ryanheise do you have good prompt?

[ryanheise]

There isn't a general purpose prompt I can recommend, so I think you'll just need to experiment to find what works well with your specific audio domain (e.g. Try short vs long prompts, multiple sentences vs one sentence, vary the amount of punctuation, and so on), but also I suggest testing #2343 in case you find that the transcription starts out well and then degrades later in the audio.

[gslin1224]

Hey guys, if I wanna use

Adding --task translate will translate the speech into English:
whisper japanese.wav --language Japanese --task translate

in the Python Usage,what library should I call?

thanks a lot!

I try It and found that solution to use python usage,

below is the code for Python usage

import whisper

model = whisper.load_model("large", download_root="/home/Lin/whisper")

result = model.transcribe("/home/common_voice_zh-CN_21347786.mp3", task="translate")

print(result["text"])

But current turbo seem doesn't support that translate Function

[Sekiraw]

It's not finetuned for translation task, so it won't perform well as @jongwook already written.

[gslin1224]

It's not finetuned for translation task, so it won't perform well as @jongwook already written.

So now only turbo not support the translate Function?

[asr-lord]

As they said, turbo not perform well in translation: "Whisper turbo was fine-tuned for two more epochs over the same amount of multilingual transcription data used for training large-v3, i.e. excluding translation data, on which we don’t expect turbo to perform well."

[gslin1224]

As they said, turbo not perform well in translation: "Whisper turbo was fine-tuned for two more epochs over the same amount of multilingual transcription data used for training large-v3, i.e. excluding translation data, on which we don’t expect turbo to perform well."

ok, thanks a lot

[x86Gr]

I've been using the turbo model and I'm getting moderately better quality, in proper names and acronyms compared to V3. With a moderately noisy audio file i get things like this:

Large V3: We're coming back from pruxelles were we held a CDA to fix the mess of the other day. we did it but we're late like jankees. you know what I mean
Turbo: We're coming back from Bruxelles were we held a CDA to fix the mess of the other day. We did it but we're late like...like jankees! You know what I mean?

Turbo compared to V3 behaves more like a Large V2 with clean audio.
Settings are no prompt / condition on previous text False / beam size 5 / best of 5 / patience 1.0
(we're still testing it so the sampled audios are not many)

[dgoryeo]

When the model is downloaded at the first useage the size of the zip file is roughly 1.5GB, while I expected large-v3-turbo to be 6GB. Is that purely due to the compression, or might have something gone wrong in my environment?

[gordon0414]

just as @jongwook mentioned in his first paragraph,

It is an optimized version of https:/openai/whisper/discussions/1762 and has only 4 decoder layers—just like the tiny model—down from the 32 in the large series.

the decoder is 1/8 wise, and when we think of the fact that the decoder part would be originally bigger in size because of the cross-attention KQV, we can understand the decrease of the size

[andyhoeung]

Personally, when transcribing Japanese audio, I still see some random unrelated English words being inserted inbetween Japanese characters. Seems this issue still persists since v3, and v2 doesn't have this problem. Shame, because it's really fast and outside of that issue, it's really good overall.

[misutoneko]

That's funny, I think I got it the other way around at some point (japanese characters for english audio).
Have you tried with --hallucination_silence_threshold? In my testing that got rid of most(?) of the hallucinations.
I've only used english audio samples though.

[andyhoeung]

I'll give that a try and report back.

[andyhoeung]

I started with a value of 2 up until 8, didn't seem to fix much.

[misutoneko]

Thank you for testing. My samples are very very short (under 30s mostly) so that may also have something to do with it.
I used smaller values such as 0.3 or so...but yeah, gotta keep experimenting I guess :D