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objects76
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synthetic-all-2.25sec-250416_1332

Transformers
TensorBoard
Safetensors
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
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  1. README.md +135 -0
  2. config.json +18 -0
  3. model.safetensors +3 -0
  4. runs/Apr16_13-32-06_Jupiter/events.out.tfevents.1744777935.Jupiter +3 -0
  5. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: pyannote/segmentation-3.0
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+ tags:
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+ - speaker-diarization
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+ - speaker-segmentation
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+ - generated_from_trainer
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+ datasets:
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+ - objects76/synthetic-all2
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+ model-index:
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+ - name: synthetic-speaker-all2
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # synthetic-speaker-all2
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+
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+ This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the objects76/synthetic-all2 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2023
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+ - Der: 0.0619
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+ - False Alarm: 0.0207
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+ - Missed Detection: 0.0284
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+ - Confusion: 0.0127
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 1536
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+ - eval_batch_size: 1536
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 70
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
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+ | No log | 1.0 | 6 | 0.6450 | 0.1856 | 0.0603 | 0.0739 | 0.0514 |
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+ | No log | 2.0 | 12 | 0.4891 | 0.1391 | 0.0450 | 0.0605 | 0.0336 |
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+ | No log | 3.0 | 18 | 0.4150 | 0.1212 | 0.0229 | 0.0661 | 0.0323 |
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+ | No log | 4.0 | 24 | 0.3674 | 0.1107 | 0.0246 | 0.0579 | 0.0283 |
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+ | 0.5602 | 5.0 | 30 | 0.3349 | 0.1019 | 0.0354 | 0.0432 | 0.0233 |
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+ | 0.5602 | 6.0 | 36 | 0.3133 | 0.0965 | 0.0367 | 0.0390 | 0.0208 |
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+ | 0.5602 | 7.0 | 42 | 0.3003 | 0.0903 | 0.0280 | 0.0435 | 0.0189 |
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+ | 0.5602 | 8.0 | 48 | 0.2887 | 0.0859 | 0.0263 | 0.0411 | 0.0185 |
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+ | 0.3151 | 9.0 | 54 | 0.2791 | 0.0837 | 0.0310 | 0.0351 | 0.0176 |
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+ | 0.3151 | 10.0 | 60 | 0.2727 | 0.0823 | 0.0326 | 0.0325 | 0.0172 |
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+ | 0.3151 | 11.0 | 66 | 0.2672 | 0.0805 | 0.0251 | 0.0374 | 0.0180 |
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+ | 0.3151 | 12.0 | 72 | 0.2632 | 0.0790 | 0.0254 | 0.0357 | 0.0179 |
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+ | 0.2678 | 13.0 | 78 | 0.2586 | 0.0778 | 0.0298 | 0.0313 | 0.0167 |
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+ | 0.2678 | 14.0 | 84 | 0.2527 | 0.0758 | 0.0273 | 0.0320 | 0.0165 |
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+ | 0.2678 | 15.0 | 90 | 0.2470 | 0.0742 | 0.0242 | 0.0342 | 0.0158 |
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+ | 0.2678 | 16.0 | 96 | 0.2454 | 0.0737 | 0.0247 | 0.0331 | 0.0160 |
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+ | 0.2448 | 17.0 | 102 | 0.2461 | 0.0741 | 0.0262 | 0.0315 | 0.0164 |
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+ | 0.2448 | 18.0 | 108 | 0.2434 | 0.0733 | 0.0266 | 0.0302 | 0.0165 |
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+ | 0.2448 | 19.0 | 114 | 0.2396 | 0.0722 | 0.0246 | 0.0312 | 0.0163 |
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+ | 0.2448 | 20.0 | 120 | 0.2344 | 0.0706 | 0.0248 | 0.0301 | 0.0157 |
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+ | 0.2261 | 21.0 | 126 | 0.2315 | 0.0714 | 0.0239 | 0.0309 | 0.0165 |
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+ | 0.2261 | 22.0 | 132 | 0.2302 | 0.0712 | 0.0223 | 0.0323 | 0.0167 |
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+ | 0.2261 | 23.0 | 138 | 0.2298 | 0.0712 | 0.0238 | 0.0309 | 0.0165 |
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+ | 0.2261 | 24.0 | 144 | 0.2253 | 0.0696 | 0.0234 | 0.0307 | 0.0155 |
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+ | 0.2139 | 25.0 | 150 | 0.2235 | 0.0682 | 0.0220 | 0.0313 | 0.0148 |
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+ | 0.2139 | 26.0 | 156 | 0.2203 | 0.0667 | 0.0214 | 0.0313 | 0.0139 |
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+ | 0.2139 | 27.0 | 162 | 0.2192 | 0.0662 | 0.0240 | 0.0288 | 0.0134 |
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+ | 0.2139 | 28.0 | 168 | 0.2166 | 0.0650 | 0.0239 | 0.0284 | 0.0127 |
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+ | 0.2139 | 29.0 | 174 | 0.2158 | 0.0647 | 0.0214 | 0.0303 | 0.0129 |
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+ | 0.1974 | 30.0 | 180 | 0.2148 | 0.0650 | 0.0218 | 0.0299 | 0.0133 |
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+ | 0.1974 | 31.0 | 186 | 0.2155 | 0.0648 | 0.0240 | 0.0273 | 0.0134 |
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+ | 0.1974 | 32.0 | 192 | 0.2151 | 0.0644 | 0.0242 | 0.0266 | 0.0137 |
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+ | 0.1974 | 33.0 | 198 | 0.2118 | 0.0644 | 0.0197 | 0.0307 | 0.0140 |
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+ | 0.1897 | 34.0 | 204 | 0.2134 | 0.0650 | 0.0204 | 0.0300 | 0.0146 |
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+ | 0.1897 | 35.0 | 210 | 0.2132 | 0.0648 | 0.0219 | 0.0284 | 0.0145 |
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+ | 0.1897 | 36.0 | 216 | 0.2115 | 0.0648 | 0.0216 | 0.0289 | 0.0143 |
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+ | 0.1897 | 37.0 | 222 | 0.2110 | 0.0644 | 0.0208 | 0.0297 | 0.0140 |
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+ | 0.1883 | 38.0 | 228 | 0.2106 | 0.0635 | 0.0211 | 0.0288 | 0.0136 |
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+ | 0.1883 | 39.0 | 234 | 0.2136 | 0.0642 | 0.0222 | 0.0281 | 0.0140 |
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+ | 0.1883 | 40.0 | 240 | 0.2138 | 0.0644 | 0.0213 | 0.0292 | 0.0139 |
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+ | 0.1883 | 41.0 | 246 | 0.2133 | 0.0640 | 0.0218 | 0.0286 | 0.0137 |
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+ | 0.1808 | 42.0 | 252 | 0.2102 | 0.0633 | 0.0224 | 0.0276 | 0.0133 |
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+ | 0.1808 | 43.0 | 258 | 0.2079 | 0.0626 | 0.0222 | 0.0274 | 0.0130 |
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+ | 0.1808 | 44.0 | 264 | 0.2087 | 0.0628 | 0.0220 | 0.0275 | 0.0133 |
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+ | 0.1808 | 45.0 | 270 | 0.2095 | 0.0632 | 0.0220 | 0.0274 | 0.0138 |
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+ | 0.1734 | 46.0 | 276 | 0.2061 | 0.0627 | 0.0202 | 0.0289 | 0.0136 |
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+ | 0.1734 | 47.0 | 282 | 0.2029 | 0.0620 | 0.0196 | 0.0293 | 0.0131 |
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+ | 0.1734 | 48.0 | 288 | 0.2026 | 0.0620 | 0.0204 | 0.0287 | 0.0129 |
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+ | 0.1734 | 49.0 | 294 | 0.2028 | 0.0620 | 0.0213 | 0.0278 | 0.0129 |
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+ | 0.1715 | 50.0 | 300 | 0.2034 | 0.0622 | 0.0217 | 0.0275 | 0.0130 |
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+ | 0.1715 | 51.0 | 306 | 0.2029 | 0.0620 | 0.0214 | 0.0277 | 0.0128 |
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+ | 0.1715 | 52.0 | 312 | 0.2024 | 0.0618 | 0.0210 | 0.0281 | 0.0127 |
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+ | 0.1715 | 53.0 | 318 | 0.2017 | 0.0617 | 0.0204 | 0.0287 | 0.0126 |
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+ | 0.1715 | 54.0 | 324 | 0.2017 | 0.0618 | 0.0204 | 0.0288 | 0.0126 |
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+ | 0.1708 | 55.0 | 330 | 0.2015 | 0.0617 | 0.0210 | 0.0282 | 0.0125 |
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+ | 0.1708 | 56.0 | 336 | 0.2013 | 0.0618 | 0.0212 | 0.0279 | 0.0126 |
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+ | 0.1708 | 57.0 | 342 | 0.2021 | 0.0620 | 0.0213 | 0.0280 | 0.0128 |
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+ | 0.1708 | 58.0 | 348 | 0.2025 | 0.0621 | 0.0214 | 0.0280 | 0.0128 |
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+ | 0.172 | 59.0 | 354 | 0.2026 | 0.0620 | 0.0212 | 0.0281 | 0.0127 |
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+ | 0.172 | 60.0 | 360 | 0.2024 | 0.0621 | 0.0211 | 0.0283 | 0.0127 |
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+ | 0.172 | 61.0 | 366 | 0.2023 | 0.0618 | 0.0208 | 0.0284 | 0.0126 |
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+ | 0.172 | 62.0 | 372 | 0.2027 | 0.0620 | 0.0208 | 0.0284 | 0.0127 |
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+ | 0.1692 | 63.0 | 378 | 0.2029 | 0.0621 | 0.0208 | 0.0285 | 0.0128 |
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+ | 0.1692 | 64.0 | 384 | 0.2025 | 0.0619 | 0.0207 | 0.0285 | 0.0127 |
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+ | 0.1692 | 65.0 | 390 | 0.2024 | 0.0619 | 0.0207 | 0.0285 | 0.0127 |
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+ | 0.1692 | 66.0 | 396 | 0.2023 | 0.0619 | 0.0207 | 0.0285 | 0.0127 |
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+ | 0.1711 | 67.0 | 402 | 0.2023 | 0.0619 | 0.0207 | 0.0285 | 0.0127 |
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+ | 0.1711 | 68.0 | 408 | 0.2023 | 0.0619 | 0.0207 | 0.0284 | 0.0127 |
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+ | 0.1711 | 69.0 | 414 | 0.2023 | 0.0619 | 0.0207 | 0.0285 | 0.0127 |
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+ | 0.1711 | 70.0 | 420 | 0.2023 | 0.0619 | 0.0207 | 0.0284 | 0.0127 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.51.0
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.5.0
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+ - Tokenizers 0.21.1
config.json ADDED
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+ {
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+ "architectures": [
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+ "SegmentationModel"
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+ ],
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+ "chunk_duration": 2.25,
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+ "max_speakers_per_chunk": 2,
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+ "max_speakers_per_frame": 2,
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+ "min_duration": null,
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+ "model_type": "pyannet",
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+ "sample_rate": 16000,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.51.0",
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+ "warm_up": [
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+ 0.0,
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+ 0.0
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+ ],
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+ "weigh_by_cardinality": false
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+ }
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