Same sentences over and over

[ghost]

Hello everyone!
If I use the medium model for example, for 2 or 3 minutes I'll have the same sentence over and over (when It's not the case in the conversation at all), but if I use the small model, I'll have the right sentences where the medium model "failed", but I'll have the same problem in another moment.
The repeated sentences also have the same duration, as if it was the same tokens/data over and over for a few minutes.
I thought it was a RAM problem at first, but the same thing happened with the small and base model.

And also sometimes the start and end times are wrong (a big delay) for a few minutes, then everything returns to normal. But It may be not related and normal.

Japanese language. Tested on different videos, doesn't happen every time but still quite frequently.
Using CUDA. No other parameters.

[NielsMayer]

Oh you mean like (see below): just when i thought the AI had become sentient, it turned out to be a bug :-) ) ...

This is the original source: https://www.bbc.co.uk/programmes/m001c3y3 ...

seems like saxophone squeaks and squonks trigger it...

zenrise:~/BBC/GillesPeterson$ whisper "Gilles Peterson, 24_09_2022 - m001c3y2.m4a" --model medium
100%|█████████████████████████████████████| 1.42G/1.42G [02:55<00:00, 8.69MiB/s]
/usr/local/lib/python3.8/dist-packages/whisper/transcribe.py:70: UserWarning: FP16 is not supported on CPU; using FP32 instead
warnings.warn("FP16 is not supported on CPU; using FP32 instead")
Detecting language using up to the first 30 seconds. Use `--language` to specify the language
Detected language: english
[00:00.000 --> 00:04.000]  Right now, it's Giles Peterson.
[00:04.000 --> 00:05.000]  What time is it?
[00:05.000 --> 00:06.000]  3 o'clock.
[00:06.000 --> 00:07.000]  I can't hear you!
[00:07.000 --> 00:22.000]  The only thing that I'm saying here is that we are making a joyful noise to people that
[00:22.000 --> 00:24.000]  don't suppose to be dying yet.
[00:24.000 --> 00:31.000]  Music is the spirit.
[00:31.000 --> 00:35.000]  Music is life.
[00:35.000 --> 00:45.000]  Life is music.
[00:45.000 --> 00:55.000]  This is Giles Peterson.
[01:27.000 --> 01:40.000]  Music is life.
[01:40.000 --> 01:50.000]  Music is life.
[01:50.000 --> 02:00.000]  Music is life.
[02:00.000 --> 02:10.000]  Music is life.
[02:10.000 --> 02:34.000]  Music is life.
[02:34.000 --> 02:44.000]  Music is life.
[02:44.000 --> 03:08.000]  Music is life.
[03:08.000 --> 03:18.000]  Music is life.
[03:18.000 --> 03:40.000]  Music is life.
[03:40.000 --> 03:49.000]  I'm not so much of a technical player myself, probably not that much of an intellectual
[03:49.000 --> 03:53.000]  player as some other musicians.
[03:53.000 --> 04:21.000]  What I do is express.
[04:21.000 --> 04:31.000]  Music is life.
[04:31.000 --> 04:55.000]  Music is life.
[04:55.000 --> 05:05.000]  Music is life.
[05:05.000 --> 05:29.000]  Music is life.
[05:29.000 --> 05:49.000]  Music is life.
[05:49.000 --> 05:59.000]  Music is life.
[05:59.000 --> 06:23.000]  Music is life.
[06:23.000 --> 06:33.000]  Music is life.

it would be nice if i could pass whisper the list of "speech" segments timecodes (see speech/music bar in Trainspodder image above) so that it could concentrate on places where speech is happening, and skip music segments in mixed longform episodes like podcasts.

[DrMemoryFish]

can i ask why we get the message: "UserWarning: FP16 is not supported on CPU; using FP32 instead"? and how do we make use of the GPU rather than CPU?

[favstats]

I have the same question!

[KnuniK]

import torch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = whisper.load_model("base", device = "cpu" )

[docsane]

Getting the same results on an anime episode, using the following arguments:

!whisper audio_file.mp3 --model large --language ja --best_of 5 --beam_size 5 --patience 0.0 --suppress_tokens -1 --fp16 True

Whisper does a very good job up till the point it reaches a long action sequence, whereupon it just gives me a repeating sentence fragment till the end of the episode.

[NielsMayer]

Total failure on https://www.bbc.co.uk/programmes/m001c3z4 which structurally looks like this with definite speech and music segments intermixed.  Transcript is pure garbage with lots of repeated sections (done Via ROCm on Linux 20.04 using AMD Radeon Rx Vega 56 GPU). ``` 1 0:00:00.000 --> 0:00:02.000 Six Music. 2 0:00:02.000 --> 0:00:04.000 Six Music. 3 0:00:04.000 --> 0:00:30.000 Right now, it's Stuart Vakoni's Freak Zone. 4 0:00:30.000 --> 0:00:45.000 Six Music. 5 0:00:45.000 --> 0:01:00.000 Six Music. 6 0:01:00.000 --> 0:01:15.000 Six Music. 7 0:01:15.000 --> 0:01:30.000 Six Music. 8 0:01:30.000 --> 0:01:45.000 Six Music. 9 0:01:45.000 --> 0:02:00.000 Six Music. 10 0:02:00.000 --> 0:02:15.000 Six Music. 11 0:02:15.000 --> 0:02:30.000 Six Music. 12 0:02:30.000 --> 0:02:45.000 Six Music. 13 0:02:45.000 --> 0:03:04.000 Six Music. 14 0:03:04.000 --> 0:03:19.000 Six Music. 15 0:03:19.000 --> 0:03:44.000 Six Music. 16 0:03:44.000 --> 0:04:04.000 Six Music. 17 0:04:04.000 --> 0:04:19.000 Six Music. 18 0:04:19.000 --> 0:04:34.000 Six Music. 19 0:04:34.000 --> 0:04:49.000 Six Music. 20 0:04:49.000 --> 0:05:04.000 Six Music. 21 0:05:04.000 --> 0:05:19.000 Six Music. 22 0:05:19.000 --> 0:05:34.000 Six Music. 23 0:05:34.000 --> 0:05:49.000 Six Music. 24 0:05:49.000 --> 0:06:04.000 Six Music. 25 0:06:04.000 --> 0:06:19.000 Six Music. 26 0:06:19.000 --> 0:06:34.000 Six Music. 27 0:06:34.000 --> 0:06:49.000 Six Music. 28 0:06:49.000 --> 0:07:04.000 Six Music. 29 0:07:04.000 --> 0:07:19.000 Six Music. 30 0:07:19.000 --> 0:07:34.000 Six Music. 31 0:07:34.000 --> 0:07:49.000 Six Music. 32 0:07:49.000 --> 0:08:04.000 Six Music. 33 0:08:04.000 --> 0:08:19.000 Six Music. 34 0:08:19.000 --> 0:08:34.000 Six Music. 35 0:08:34.000 --> 0:08:49.000 Six Music. 36 0:08:49.000 --> 0:09:04.000 Six Music. 37 0:09:04.000 --> 0:09:19.000 Six Music. 38 0:09:19.000 --> 0:09:34.000 Six Music. 39 0:09:34.000 --> 0:09:49.000 Six Music. 40 0:09:49.000 --> 0:10:04.000 Six Music. 41 0:10:04.000 --> 0:10:19.000 Six Music. 42 0:10:19.000 --> 0:10:34.000 Six Music. 43 0:10:34.000 --> 0:10:49.000 Six Music. 44 0:10:49.000 --> 0:11:04.000 Six Music. 45 0:11:04.000 --> 0:11:19.000 Six Music. 46 0:11:19.000 --> 0:11:34.000 Six Music. 47 0:11:34.000 --> 0:11:49.000 Six Music. 48 0:11:49.000 --> 0:12:04.000 Six Music. 49 0:12:04.000 --> 0:12:19.000 Six Music. 50 0:12:19.000 --> 0:12:34.000 Six Music. 51 0:12:34.000 --> 0:12:49.000 Six Music. 52 0:12:49.000 --> 0:13:04.000 Six Music. 53 0:13:04.000 --> 0:13:19.000 Six Music. 54 0:13:19.000 --> 0:13:34.000 Six Music. 55 0:13:34.000 --> 0:13:49.000 Six Music. 56 0:13:49.000 --> 0:14:04.000 Six Music. 57 0:14:04.000 --> 0:14:19.000 Six Music. 58 0:14:19.000 --> 0:14:34.000 Six Music. 59 0:14:34.000 --> 0:14:49.000 Six Music. 60 0:14:49.000 --> 0:15:04.000 Six Music. 61 0:15:04.000 --> 0:15:19.000 Six Music. 62 0:15:19.000 --> 0:15:34.000 Six Music. 63 0:15:34.000 --> 0:15:49.000 Six Music. 64 0:15:49.000 --> 0:16:04.000 Six Music. 65 0:16:04.000 --> 0:16:19.000 Six Music. 66 0:16:19.000 --> 0:16:34.000 Six Music. 67 0:16:34.000 --> 0:16:49.000 Six Music. 68 0:16:49.000 --> 0:17:04.000 Six Music. 69 0:17:04.000 --> 0:17:19.000 Six Music. 70 0:17:19.000 --> 0:17:34.000 Six Music. 71 0:17:34.000 --> 0:17:49.000 Six Music. 72 0:17:49.000 --> 0:18:04.000 Six Music. 73 0:18:04.000 --> 0:18:19.000 Six Music. 74 0:18:19.000 --> 0:18:34.000 Six Music. 75 0:18:34.000 --> 0:18:49.000 Six Music. 76 0:18:49.000 --> 0:19:04.000 Six Music. 77 0:19:04.000 --> 0:19:19.000 Six Music. 78 0:19:19.000 --> 0:19:34.000 Six Music. 79 0:19:34.000 --> 0:19:49.000 Six Music. 80 0:19:49.000 --> 0:20:04.000 Six Music. 81 0:20:04.000 --> 0:20:19.000 Six Music. 82 0:20:19.000 --> 0:20:34.000 Six Music. 83 0:20:34.000 --> 0:20:49.000 Six Music. 84 0:20:49.000 --> 0:21:04.000 Six Music. 85 0:21:04.000 --> 0:21:19.000 Six Music. 86 0:21:19.000 --> 0:21:34.000 Six Music. 87 0:21:34.000 --> 0:21:49.000 Six Music. 88 0:21:49.000 --> 0:22:04.000 Six Music. 89 0:22:04.000 --> 0:22:19.000 Six Music. 90 0:22:19.000 --> 0:22:34.000 Six Music. 91 0:22:34.000 --> 0:22:49.000 Six Music. 92 0:22:49.000 --> 0:23:04.000 Six Music. 93 0:23:04.000 --> 0:23:19.000 Six Music. 94 0:23:19.000 --> 0:23:34.000 Six Music. 95 0:23:34.000 --> 0:23:49.000 Six Music. 96 0:23:49.000 --> 0:24:04.000 Six Music. 97 0:24:04.000 --> 0:24:19.000 Six Music. 98 0:24:19.000 --> 0:24:34.000 Six Music. 99 0:24:34.000 --> 0:24:49.000 Six Music. 100 0:24:49.000 --> 0:25:04.000 Six Music. 101 0:25:04.000 --> 0:25:19.000 Six Music. 102 0:25:19.000 --> 0:25:34.000 Six Music. 103 0:25:34.000 --> 0:25:49.000 Six Music. 104 0:25:49.000 --> 0:26:04.000 Six Music. 105 0:26:04.000 --> 0:26:19.000 Six Music. 106 0:26:19.000 --> 0:26:34.000 Six Music. 107 0:26:34.000 --> 0:26:49.000 Six Music. 108 0:26:49.000 --> 0:27:04.000 Six Music. 109 0:27:04.000 --> 0:27:19.000 Six Music. 110 0:27:19.000 --> 0:27:34.000 Six Music. 111 0:27:34.000 --> 0:27:49.000 Six Music. 112 0:27:49.000 --> 0:28:04.000 Six Music. 113 0:28:04.000 --> 0:28:19.000 Six Music. 114 0:28:19.000 --> 0:28:34.000 Six Music. 115 0:28:34.000 --> 0:28:49.000 Six Music. 116 0:28:49.000 --> 0:29:04.000 Six Music. 117 0:29:04.000 --> 0:29:19.000 Six Music. 118 0:29:19.000 --> 0:29:34.000 Six Music. 119 0:29:34.000 --> 0:29:49.000 Six Music. 120 0:29:49.000 --> 0:30:04.000 Six Music. 121 0:30:04.000 --> 0:30:19.000 Six Music. 122 0:30:19.000 --> 0:30:34.000 Six Music. 123 0:30:34.000 --> 0:30:49.000 Six Music. 124 0:30:49.000 --> 0:31:04.000 Six Music. 125 0:31:04.000 --> 0:31:19.000 Six Music. 126 0:31:19.000 --> 0:31:34.000 Six Music. 127 0:31:34.000 --> 0:31:49.000 Six Music. 128 0:31:49.000 --> 0:32:04.000 Six Music. 129 0:32:04.000 --> 0:32:19.000 Six Music. 130 0:32:19.000 --> 0:32:34.000 Six Music. 131 0:32:34.000 --> 0:32:49.000 Six Music. 132 0:32:49.000 --> 0:33:04.000 Six Music. 133 0:33:04.000 --> 0:33:19.000 Six Music. 134 0:33:19.000 --> 0:33:34.000 Six Music. 135 0:33:34.000 --> 0:33:49.000 Six Music. 136 0:33:49.000 --> 0:34:04.000 Six Music. 137 0:34:04.000 --> 0:34:19.000 Six Music. 138 0:34:19.000 --> 0:34:34.000 Six Music. 139 0:34:34.000 --> 0:34:49.000 Six Music. 140 0:34:49.000 --> 0:35:04.000 Six Music. 141 0:35:04.000 --> 0:35:19.000 Six Music. 142 0:35:19.000 --> 0:35:34.000 Six Music. 143 0:35:34.000 --> 0:35:49.000 Six Music. 144 0:35:49.000 --> 0:36:04.000 Six Music. 145 0:36:04.000 --> 0:36:19.000 Six Music. 146 0:36:19.000 --> 0:36:34.000 Six Music. 147 0:36:34.000 --> 0:36:49.000 Six Music. 148 0:36:49.000 --> 0:37:04.000 Six Music. 149 0:37:04.000 --> 0:37:19.000 Six Music. 150 0:37:19.000 --> 0:37:34.000 Six Music. 151 0:37:34.000 --> 0:37:49.000 Six Music. 152 0:37:49.000 --> 0:38:04.000 Six Music. 153 0:38:04.000 --> 0:38:19.000 Six Music. 154 0:38:19.000 --> 0:38:34.000 Six Music. 155 0:38:34.000 --> 0:38:49.000 Six Music. 156 0:38:49.000 --> 0:39:04.000 Six Music. 157 0:39:04.000 --> 0:39:19.000 Six Music. 158 0:39:19.000 --> 0:39:34.000 Six Music. 159 0:39:34.000 --> 0:39:49.000 Six Music. 160 0:39:49.000 --> 0:40:04.000 Six Music. 161 0:40:04.000 --> 0:40:19.000 Six Music. 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0:46:04.000 --> 0:46:19.000 Six Music. 186 0:46:19.000 --> 0:46:34.000 Six Music. 187 0:46:34.000 --> 0:46:49.000 Six Music. 188 0:46:49.000 --> 0:47:04.000 Six Music. 189 0:47:04.000 --> 0:47:19.000 Six Music. 190 0:47:19.000 --> 0:47:34.000 Six Music. 191 0:47:34.000 --> 0:47:49.000 Six Music. 192 0:47:49.000 --> 0:48:04.000 Six Music. 193 0:48:04.000 --> 0:48:19.000 Six Music. 194 0:48:19.000 --> 0:48:34.000 Six Music. 195 0:48:34.000 --> 0:48:49.000 Six Music. 196 0:48:49.000 --> 0:49:04.000 Six Music. 197 0:49:04.000 --> 0:49:19.000 Six Music. 198 0:49:19.000 --> 0:49:34.000 Six Music. 199 0:49:34.000 --> 0:49:49.000 Six Music. 200 0:49:49.000 --> 0:50:04.000 Six Music. 201 0:50:04.000 --> 0:50:19.000 Six Music. 202 0:50:19.000 --> 0:50:34.000 Six Music. 203 0:50:34.000 --> 0:50:49.000 Six Music. 204 0:50:49.000 --> 0:51:04.000 Six Music. 205 0:51:04.000 --> 0:51:19.000 Six Music. 206 0:51:19.000 --> 0:51:34.000 Six Music. 207 0:51:34.000 --> 0:51:49.000 Six Music. 208 0:51:49.000 --> 0:52:04.000 Six Music. 209 0:52:04.000 --> 0:52:19.000 Six Music. 210 0:52:19.000 --> 0:52:34.000 Six Music. 211 0:52:34.000 --> 0:52:49.000 Six Music. 212 0:52:49.000 --> 0:53:04.000 Six Music. 213 0:53:04.000 --> 0:53:19.000 Six Music. 214 0:53:19.000 --> 0:53:34.000 Six Music. 215 0:53:34.000 --> 0:53:49.000 Six Music. 216 0:53:49.000 --> 0:54:04.000 Six Music. 217 0:54:04.000 --> 0:54:19.000 Six Music. 218 0:54:19.000 --> 0:54:34.000 Six Music. 219 0:54:34.000 --> 0:54:49.000 Six Music. 220 0:54:49.000 --> 0:55:04.000 Six Music. 221 0:55:04.000 --> 0:55:19.000 Six Music. 222 0:55:19.000 --> 0:55:34.000 Six Music. 223 0:55:34.000 --> 0:55:49.000 Six Music. 224 0:55:49.000 --> 0:56:04.000 Six Music. 225 0:56:04.000 --> 0:56:19.000 Six Music. 226 0:56:19.000 --> 0:56:34.000 Six Music. 227 0:56:34.000 --> 0:56:49.000 Six Music. 228 0:56:49.000 --> 0:57:04.000 Six Music. 229 0:57:04.000 --> 0:57:19.000 Six Music. 230 0:57:19.000 --> 0:57:34.000 Six Music. 231 0:57:34.000 --> 0:57:49.000 Six Music. 232 0:57:49.000 --> 0:58:04.000 Six Music. 233 0:58:04.000 --> 0:58:19.000 Six Music. 234 0:58:19.000 --> 0:58:34.000 Six Music. 235 0:58:34.000 --> 0:58:49.000 Six Music. 236 0:58:49.000 --> 0:59:04.000 Six Music. 237 0:59:04.000 --> 0:59:19.000 Six Music. 238 0:59:19.000 --> 0:59:34.000 Six Music. 239 0:59:34.000 --> 0:59:49.000 Six Music. 240 0:59:49.000 --> 1:00:04.000 Six Music. 241 1:00:04.000 --> 1:00:19.000 Six Music. 242 1:00:19.000 --> 1:00:34.000 Six Music. 243 1:00:34.000 --> 1:00:49.000 Six Music. 244 1:00:49.000 --> 1:01:04.000 Six Music. 245 1:01:04.000 --> 1:01:19.000 Six Music. 246 1:01:19.000 --> 1:01:34.000 Six Music. 247 1:01:34.000 --> 1:01:49.000 Six Music. 248 1:01:49.000 --> 1:02:04.000 Six Music. 249 1:02:04.000 --> 1:02:19.000 Six Music. 250 1:02:19.000 --> 1:02:34.000 Six Music. 251 1:02:34.000 --> 1:02:49.000 Six Music. 252 1:02:49.000 --> 1:03:04.000 Six Music. 253 1:03:04.000 --> 1:03:19.000 Six Music. 254 1:03:19.000 --> 1:03:34.000 Six Music. 255 1:03:34.000 --> 1:03:49.000 Six Music. 256 1:03:49.000 --> 1:04:04.000 Six Music. 257 1:04:04.000 --> 1:04:19.000 Six Music. 258 1:04:19.000 --> 1:04:34.000 Six Music. 259 1:04:34.000 --> 1:04:49.000 Six Music. 260 1:04:49.000 --> 1:05:09.000 Six Music. 261 1:05:09.000 --> 1:05:20.000 Six Music. 262 1:05:20.000 --> 1:05:42.000 Six Music. 263 1:05:42.000 --> 1:05:57.000 Six Music. 264 1:05:57.000 --> 1:06:12.000 Six Music. 265 1:06:12.000 --> 1:06:27.000 Six Music. 266 1:06:27.000 --> 1:06:42.000 Six Music. 267 1:06:42.000 --> 1:06:57.000 Six Music. 268 1:06:57.000 --> 1:07:12.000 Six Music. 269 1:07:12.000 --> 1:07:27.000 Six Music. 270 1:07:27.000 --> 1:07:42.000 Six Music. 271 1:07:42.000 --> 1:07:57.000 Six Music. 272 1:07:57.000 --> 1:08:12.000 Six Music. 273 1:08:12.000 --> 1:08:27.000 Six Music. 274 1:08:27.000 --> 1:08:42.000 Six Music. 275 1:08:42.000 --> 1:08:57.000 Six Music. 276 1:08:57.000 --> 1:09:12.000 Six Music. 277 1:09:12.000 --> 1:09:27.000 Six Music. 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[rodgermoore]

Try the medium.en or large model...

[awexandrr]

i think you can refer to #192 for this one.

[lmmx]

You can also try preprocessing with a source separation model and only transcribing portions of the vocal track with speech. The model is intended as speech to text. If you pass in music with no vocals, it’s not exactly a mistake when you get transcribed speech [where no speech was present] as a mistaken use. Source separation allows you to correctly specify the task, and while you could pass in the full audio with large silences [where the music had been separated out], I think this causes errors in Whisper, so you should split up based on intensity.

[vicnaum]

Which source separation model can you recommend?

[phineas-pta]

SileroVAD to remove silence, Demucs to extract vocals

see #679

code example: https://github.com/EtienneAb3d/WhisperHallu

[ggodzillai]

Hi quality lavalier recordings = 2 bugs every 10 audio files.
Not possible to use professionally.