Missing timestamp offset using Whisper with pipeline and sequential decoding

[dintifla]

System Info

• transformers version: 4.45.2
• Platform: macOS-15.0.1-arm64-arm-64bit
• Python version: 3.12.1
• Huggingface_hub version: 0.23.3
• Safetensors version: 0.4.3
• Accelerate version: 0.34.2
• Accelerate config: not found
• PyTorch version (GPU?): 2.4.1 (False)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?: no

Who can help?

@Rocketknight1 @gante @ylacombe

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

1. pip install transformers==4.45.2

2. Setup a Whisper pipeline using chunk_length_s=0 (which is sequential long-form decoding according to the model card (at least for large-v3)) and return_timestamps=True

3. Transcribe an audio longer than 30s

   from transformers import pipeline
   import torch
   
   audio_file = '<an-audio-file-longer-than-30-s>'
   chunked = False
   
   pipe = pipeline(
       'automatic-speech-recognition',
       model='openai/whisper-small',
       chunk_length_s=30 if chunked else 0,
       return_timestamps=True,
       torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
       device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu',
   )
   
   result = pipe(audio_file)
   transcript = '\n'.join(
       f"({chunk['timestamp'][0]}, {chunk['timestamp'][1]})\t{chunk['text']}" for chunk in result['chunks']
   )
   print(transcript)

4. See that the timestamps start at 0.0s after 30s

   (0.0, 4.44)      Er hatte schon mal eine Schnauze voll von allem und jedem.
   (4.44, 6.28)     Und er hat den Schluss getroffen.
   (6.28, 7.8)      Es hilft nichts mehr.
   (7.8, 9.28)      Ich wandere aus.
   (9.28, 11.4)     Das kann ein Grund sein,
   (11.4, 14.48)    wieso er eine Heimat für immer der Rückenträger will.
   (14.48, 16.72)   Oder es ist etwas ganz anderes.
   (16.72, 19.24)   Der wohl bekannt ist Grund...
   (19.24, 20.36)  ... die Liebe.
   (20.36, 22.44)   So ist es bei Hans Muster.
   (22.44, 24.72)   Die Liebe hat ihn nach Deutschland gezogen.
   (24.72, 27.0)    Und dort ist er seit vier Jahren.
   (27.0, 29.4)     Aber welter der für immer dort bleibt.
   (0.0, 1.0)       Gute Frage.
   (1.0, 4.0)       Ich stelle mir einen Gart am Viertel vor im PO bei den Leuten.
   (4.0, 7.0)       Und bis dort her, mein Name ist Peter Müller.
   (7.0, 11.0)      Und ich bin Wassermelone Heines vom Harry Styles.
   

Expected behavior

The timestamps should be correct, also if the audio is longer than 30s (as if the chunked-algorithm is used):

(0.0, 4.44)      Er hatte schon mal eine Schnauze voll von allem und jedem.
(4.44, 6.28)     Und er hat den Schluss getroffen.
(6.28, 7.8)      Es hilft nichts mehr.
(7.8, 9.28)      Ich wandere aus.
(9.28, 11.4)     Das kann ein Grund sein,
(11.4, 14.48)    wieso er eine Heimat für immer der Rückenträger will.
(14.48, 16.72)   Oder es ist etwas ganz anderes.
(16.72, 19.24)   Der wohl bekannt ist Grund...
(19.24, 20.36)  ... die Liebe.
(20.36, 22.44)   So ist es bei Hans Muster.
(22.44, 24.72)   Die Liebe hat ihn nach Deutschland gezogen.
(24.72, 26.0)    Und dort ist er seit vier Jahren.
(26.0, 29.0)     Aber welter der für immer dort bleibt, gute Frage.
(29.0, 32.0)     Wir stellen es dir an, am Viertel vor, im PO bei den Leuten.
(32.0, 35.0)     Und bis dort her, mein Name ist Peter Müller.
(35.0, 39.0)     Und jetzt ein Wassermelon Heines vom Harry Styles.

The output is from above script using chunked=True

[gante]

cc @eustlb, since you're working on other Whisper fixes :)

[dineshveguru]

Any fix ?

[Rocketknight1]

@ylacombe can you see a quick fix here? If it's more of a pipeline issue and you don't have any ideas, let me know and I can take it

[ylacombe]

Hey @dintifla and @dineshveguru , thanks for your message.

cc @eustlb, seems linked to #34537 but it's not exactly the same issue, any ideas why it happen ?

[dintifla]

Hey @ylacombe

As far as I analyzed it is because stride is missing in the output here:

transformers/src/transformers/models/whisper/tokenization_whisper.py

Line 1064 in 1f6b423

if "stride" in output:

hence, time_offset is always 0.0

the calculation in your linked PR works differently.

[ylacombe]

Indeed @dintifla, you're right on the cause, I'll dig into this tomorrow

[ylacombe]

Turns out it's not because stride is missing. stride is only present if we use chunk_length_s>0. In other words, it's only used with the general long-form transcription algorithm from the ASR pipeline.

The error you observed actually happens because time_offset is always zero here if chunk_length_s=0.

To fix this, we have to be careful about how we set the time offset. @eustlb, you've worked a lot on this in #34537. Could you a TL;DR on the different cases we can observe? Notably, it seems that we're facing the case:

<0> t1 t2 <T1> <T1> t3 t4 t5 ... tn <TN> <0> tn+1 tn+1 <T1_bis> <T1_bis>...

where tX denote text tokens and <TX> denote timestamp tokens. Are there other possible cases (other than <TN> <0>)? Does <TN> <0> fall into the 1st or the 2nd cases described in #34537 ?

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.

[dintifla]

This issue still persists (v4.47.1). Please re-open it.
Can I somehow assist @ylacombe ?

[eustlb]

Fixed in #35750 that will be merged ASAP! Thanks a lot for raising this issue, and thanks a lot for your patience 🤗

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the contributing guidelines are likely to be ignored.