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---
base_model: microsoft/wavlm-base
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wavlm-base_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wavlm-base_2
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0244
- Accuracy: 0.9966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 2
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4872 | 0.25 | 100 | 0.2180 | 0.8974 |
| 0.1571 | 0.5 | 200 | 0.2582 | 0.9334 |
| 0.0644 | 0.76 | 300 | 0.0244 | 0.9966 |
| 0.0553 | 1.01 | 400 | 0.1156 | 0.9928 |
| 0.1108 | 1.26 | 500 | 0.1576 | 0.9898 |
| 0.0849 | 1.51 | 600 | 0.0871 | 0.9947 |
| 0.0635 | 1.76 | 700 | 0.1088 | 0.9939 |
| 0.0504 | 2.02 | 800 | 0.4074 | 0.9790 |
| 0.1075 | 2.27 | 900 | 0.2955 | 0.9814 |
| 0.2387 | 2.52 | 1000 | 0.0651 | 0.9956 |
| 0.3052 | 2.77 | 1100 | 0.2379 | 0.8974 |
| 0.3336 | 3.02 | 1200 | 0.3527 | 0.8974 |
| 0.3322 | 3.28 | 1300 | 0.3307 | 0.8974 |
| 0.3201 | 3.53 | 1400 | 0.3405 | 0.8974 |
| 0.3406 | 3.78 | 1500 | 0.3335 | 0.8974 |
| 0.3475 | 4.03 | 1600 | 0.3341 | 0.8974 |
| 0.3312 | 4.28 | 1700 | 0.3361 | 0.8974 |
| 0.3367 | 4.54 | 1800 | 0.3310 | 0.8974 |
| 0.3284 | 4.79 | 1900 | 0.3339 | 0.8974 |
| 0.3267 | 5.04 | 2000 | 0.3350 | 0.8974 |
| 0.338 | 5.29 | 2100 | 0.3308 | 0.8974 |
| 0.3277 | 5.55 | 2200 | 0.3309 | 0.8974 |
| 0.3294 | 5.8 | 2300 | 0.3313 | 0.8974 |
| 0.3315 | 6.05 | 2400 | 0.3360 | 0.8974 |
| 0.3397 | 6.3 | 2500 | 0.3307 | 0.8974 |
| 0.3318 | 6.55 | 2600 | 0.3359 | 0.8974 |
| 0.3312 | 6.81 | 2700 | 0.3308 | 0.8974 |
| 0.3155 | 7.06 | 2800 | 0.3317 | 0.8974 |
| 0.3304 | 7.31 | 2900 | 0.3362 | 0.8974 |
| 0.338 | 7.56 | 3000 | 0.3342 | 0.8974 |
| 0.3241 | 7.81 | 3100 | 0.3310 | 0.8974 |
| 0.3325 | 8.07 | 3200 | 0.3326 | 0.8974 |
| 0.3202 | 8.32 | 3300 | 0.3345 | 0.8974 |
| 0.3315 | 8.57 | 3400 | 0.3335 | 0.8974 |
| 0.3288 | 8.82 | 3500 | 0.3312 | 0.8974 |
| 0.3371 | 9.07 | 3600 | 0.3401 | 0.8974 |
| 0.3409 | 9.33 | 3700 | 0.3330 | 0.8974 |
| 0.3236 | 9.58 | 3800 | 0.3330 | 0.8974 |
| 0.3224 | 9.83 | 3900 | 0.3321 | 0.8974 |
| 0.3439 | 10.08 | 4000 | 0.3326 | 0.8974 |
| 0.3382 | 10.33 | 4100 | 0.3310 | 0.8974 |
| 0.3307 | 10.59 | 4200 | 0.3382 | 0.8974 |
| 0.3231 | 10.84 | 4300 | 0.3325 | 0.8974 |
| 0.3095 | 11.09 | 4400 | 0.3348 | 0.8974 |
| 0.3442 | 11.34 | 4500 | 0.3327 | 0.8974 |
| 0.3269 | 11.59 | 4600 | 0.3326 | 0.8974 |
| 0.3323 | 11.85 | 4700 | 0.3308 | 0.8974 |
| 0.3313 | 12.1 | 4800 | 0.3308 | 0.8974 |
| 0.3283 | 12.35 | 4900 | 0.3314 | 0.8974 |
| 0.3331 | 12.6 | 5000 | 0.3307 | 0.8974 |
| 0.3317 | 12.85 | 5100 | 0.3344 | 0.8974 |
| 0.3283 | 13.11 | 5200 | 0.3320 | 0.8974 |
| 0.3263 | 13.36 | 5300 | 0.3311 | 0.8974 |
| 0.3421 | 13.61 | 5400 | 0.3307 | 0.8974 |
| 0.3164 | 13.86 | 5500 | 0.3318 | 0.8974 |
| 0.3315 | 14.11 | 5600 | 0.3335 | 0.8974 |
| 0.3415 | 14.37 | 5700 | 0.3315 | 0.8974 |
| 0.3325 | 14.62 | 5800 | 0.3307 | 0.8974 |
| 0.3264 | 14.87 | 5900 | 0.3330 | 0.8974 |
| 0.3223 | 15.12 | 6000 | 0.3307 | 0.8974 |
| 0.3289 | 15.37 | 6100 | 0.3329 | 0.8974 |
| 0.3353 | 15.63 | 6200 | 0.3311 | 0.8974 |
| 0.3246 | 15.88 | 6300 | 0.3311 | 0.8974 |
| 0.3425 | 16.13 | 6400 | 0.3307 | 0.8974 |
| 0.331 | 16.38 | 6500 | 0.3307 | 0.8974 |
| 0.3293 | 16.64 | 6600 | 0.3353 | 0.8974 |
| 0.3249 | 16.89 | 6700 | 0.3339 | 0.8974 |
| 0.3214 | 17.14 | 6800 | 0.3338 | 0.8974 |
| 0.3259 | 17.39 | 6900 | 0.3327 | 0.8974 |
| 0.3408 | 17.64 | 7000 | 0.3318 | 0.8974 |
| 0.3258 | 17.9 | 7100 | 0.3318 | 0.8974 |
| 0.3299 | 18.15 | 7200 | 0.3308 | 0.8974 |
| 0.327 | 18.4 | 7300 | 0.3371 | 0.8974 |
| 0.3317 | 18.65 | 7400 | 0.3308 | 0.8974 |
| 0.3291 | 18.9 | 7500 | 0.3310 | 0.8974 |
| 0.3263 | 19.16 | 7600 | 0.3325 | 0.8974 |
| 0.3223 | 19.41 | 7700 | 0.3346 | 0.8974 |
| 0.3403 | 19.66 | 7800 | 0.3316 | 0.8974 |
| 0.3265 | 19.91 | 7900 | 0.3309 | 0.8974 |
| 0.33 | 20.16 | 8000 | 0.3318 | 0.8974 |
| 0.3488 | 20.42 | 8100 | 0.3313 | 0.8974 |
| 0.3293 | 20.67 | 8200 | 0.3335 | 0.8974 |
| 0.3095 | 20.92 | 8300 | 0.3356 | 0.8974 |
| 0.3366 | 21.17 | 8400 | 0.3332 | 0.8974 |
| 0.317 | 21.42 | 8500 | 0.3338 | 0.8974 |
| 0.3299 | 21.68 | 8600 | 0.3308 | 0.8974 |
| 0.3434 | 21.93 | 8700 | 0.3310 | 0.8974 |
| 0.3208 | 22.18 | 8800 | 0.3309 | 0.8974 |
| 0.3351 | 22.43 | 8900 | 0.3324 | 0.8974 |
| 0.3301 | 22.68 | 9000 | 0.3308 | 0.8974 |
| 0.3196 | 22.94 | 9100 | 0.3330 | 0.8974 |
| 0.3339 | 23.19 | 9200 | 0.3333 | 0.8974 |
| 0.3249 | 23.44 | 9300 | 0.3308 | 0.8974 |
| 0.3247 | 23.69 | 9400 | 0.3338 | 0.8974 |
| 0.3369 | 23.94 | 9500 | 0.3313 | 0.8974 |
| 0.3291 | 24.2 | 9600 | 0.3320 | 0.8974 |
| 0.3307 | 24.45 | 9700 | 0.3309 | 0.8974 |
| 0.3328 | 24.7 | 9800 | 0.3307 | 0.8974 |
| 0.3277 | 24.95 | 9900 | 0.3342 | 0.8974 |
| 0.3278 | 25.2 | 10000 | 0.3310 | 0.8974 |
| 0.3197 | 25.46 | 10100 | 0.3349 | 0.8974 |
| 0.3273 | 25.71 | 10200 | 0.3321 | 0.8974 |
| 0.3345 | 25.96 | 10300 | 0.3312 | 0.8974 |
| 0.3351 | 26.21 | 10400 | 0.3325 | 0.8974 |
| 0.3144 | 26.47 | 10500 | 0.3346 | 0.8974 |
| 0.3361 | 26.72 | 10600 | 0.3311 | 0.8974 |
| 0.3334 | 26.97 | 10700 | 0.3307 | 0.8974 |
| 0.3287 | 27.22 | 10800 | 0.3373 | 0.8974 |
| 0.3374 | 27.47 | 10900 | 0.3307 | 0.8974 |
| 0.3302 | 27.73 | 11000 | 0.3307 | 0.8974 |
| 0.3245 | 27.98 | 11100 | 0.3315 | 0.8974 |
| 0.3353 | 28.23 | 11200 | 0.3335 | 0.8974 |
| 0.3191 | 28.48 | 11300 | 0.3336 | 0.8974 |
| 0.3226 | 28.73 | 11400 | 0.3308 | 0.8974 |
| 0.3384 | 28.99 | 11500 | 0.3322 | 0.8974 |
| 0.3368 | 29.24 | 11600 | 0.3337 | 0.8974 |
| 0.3224 | 29.49 | 11700 | 0.3332 | 0.8974 |
| 0.3224 | 29.74 | 11800 | 0.3318 | 0.8974 |
| 0.3363 | 29.99 | 11900 | 0.3310 | 0.8974 |
| 0.327 | 30.25 | 12000 | 0.3307 | 0.8974 |
| 0.3291 | 30.5 | 12100 | 0.3307 | 0.8974 |
| 0.3369 | 30.75 | 12200 | 0.3322 | 0.8974 |
| 0.3211 | 31.0 | 12300 | 0.3329 | 0.8974 |
| 0.329 | 31.25 | 12400 | 0.3321 | 0.8974 |
| 0.3206 | 31.51 | 12500 | 0.3309 | 0.8974 |
| 0.3339 | 31.76 | 12600 | 0.3332 | 0.8974 |
| 0.3323 | 32.01 | 12700 | 0.3316 | 0.8974 |
| 0.3273 | 32.26 | 12800 | 0.3323 | 0.8974 |
| 0.3362 | 32.51 | 12900 | 0.3307 | 0.8974 |
| 0.3387 | 32.77 | 13000 | 0.3309 | 0.8974 |
| 0.3173 | 33.02 | 13100 | 0.3311 | 0.8974 |
| 0.3291 | 33.27 | 13200 | 0.3309 | 0.8974 |
| 0.3316 | 33.52 | 13300 | 0.3315 | 0.8974 |
| 0.3366 | 33.77 | 13400 | 0.3332 | 0.8974 |
| 0.3115 | 34.03 | 13500 | 0.3383 | 0.8974 |
| 0.3275 | 34.28 | 13600 | 0.3324 | 0.8974 |
| 0.3373 | 34.53 | 13700 | 0.3315 | 0.8974 |
| 0.3247 | 34.78 | 13800 | 0.3313 | 0.8974 |
| 0.3349 | 35.03 | 13900 | 0.3325 | 0.8974 |
| 0.3223 | 35.29 | 14000 | 0.3312 | 0.8974 |
| 0.3321 | 35.54 | 14100 | 0.3308 | 0.8974 |
| 0.3304 | 35.79 | 14200 | 0.3316 | 0.8974 |
| 0.3262 | 36.04 | 14300 | 0.3320 | 0.8974 |
| 0.3239 | 36.29 | 14400 | 0.3317 | 0.8974 |
| 0.3325 | 36.55 | 14500 | 0.3308 | 0.8974 |
| 0.325 | 36.8 | 14600 | 0.3316 | 0.8974 |
| 0.3416 | 37.05 | 14700 | 0.3311 | 0.8974 |
| 0.3226 | 37.3 | 14800 | 0.3309 | 0.8974 |
| 0.3286 | 37.56 | 14900 | 0.3307 | 0.8974 |
| 0.3284 | 37.81 | 15000 | 0.3312 | 0.8974 |
| 0.3298 | 38.06 | 15100 | 0.3326 | 0.8974 |
| 0.3383 | 38.31 | 15200 | 0.3311 | 0.8974 |
| 0.3418 | 38.56 | 15300 | 0.3308 | 0.8974 |
| 0.3123 | 38.82 | 15400 | 0.3311 | 0.8974 |
| 0.3237 | 39.07 | 15500 | 0.3346 | 0.8974 |
| 0.3261 | 39.32 | 15600 | 0.3325 | 0.8974 |
| 0.3269 | 39.57 | 15700 | 0.3312 | 0.8974 |
| 0.3267 | 39.82 | 15800 | 0.3319 | 0.8974 |
| 0.3381 | 40.08 | 15900 | 0.3327 | 0.8974 |
| 0.3238 | 40.33 | 16000 | 0.3326 | 0.8974 |
| 0.3299 | 40.58 | 16100 | 0.3320 | 0.8974 |
| 0.3385 | 40.83 | 16200 | 0.3309 | 0.8974 |
| 0.3268 | 41.08 | 16300 | 0.3322 | 0.8974 |
| 0.3253 | 41.34 | 16400 | 0.3320 | 0.8974 |
| 0.3261 | 41.59 | 16500 | 0.3314 | 0.8974 |
| 0.3362 | 41.84 | 16600 | 0.3324 | 0.8974 |
| 0.3203 | 42.09 | 16700 | 0.3326 | 0.8974 |
| 0.325 | 42.34 | 16800 | 0.3323 | 0.8974 |
| 0.3172 | 42.6 | 16900 | 0.3326 | 0.8974 |
| 0.3361 | 42.85 | 17000 | 0.3308 | 0.8974 |
| 0.3432 | 43.1 | 17100 | 0.3310 | 0.8974 |
| 0.3396 | 43.35 | 17200 | 0.3313 | 0.8974 |
| 0.3163 | 43.6 | 17300 | 0.3328 | 0.8974 |
| 0.3353 | 43.86 | 17400 | 0.3318 | 0.8974 |
| 0.3299 | 44.11 | 17500 | 0.3317 | 0.8974 |
| 0.3213 | 44.36 | 17600 | 0.3319 | 0.8974 |
| 0.3253 | 44.61 | 17700 | 0.3329 | 0.8974 |
| 0.3391 | 44.86 | 17800 | 0.3322 | 0.8974 |
| 0.3179 | 45.12 | 17900 | 0.3330 | 0.8974 |
| 0.3348 | 45.37 | 18000 | 0.3321 | 0.8974 |
| 0.3116 | 45.62 | 18100 | 0.3326 | 0.8974 |
| 0.3334 | 45.87 | 18200 | 0.3322 | 0.8974 |
| 0.3401 | 46.12 | 18300 | 0.3315 | 0.8974 |
| 0.3381 | 46.38 | 18400 | 0.3311 | 0.8974 |
| 0.3154 | 46.63 | 18500 | 0.3327 | 0.8974 |
| 0.3348 | 46.88 | 18600 | 0.3322 | 0.8974 |
| 0.3285 | 47.13 | 18700 | 0.3325 | 0.8974 |
| 0.3256 | 47.39 | 18800 | 0.3329 | 0.8974 |
| 0.3389 | 47.64 | 18900 | 0.3325 | 0.8974 |
| 0.3288 | 47.89 | 19000 | 0.3327 | 0.8974 |
| 0.3172 | 48.14 | 19100 | 0.3327 | 0.8974 |
| 0.3211 | 48.39 | 19200 | 0.3325 | 0.8974 |
| 0.3348 | 48.65 | 19300 | 0.3325 | 0.8974 |
| 0.3327 | 48.9 | 19400 | 0.3326 | 0.8974 |
| 0.3341 | 49.15 | 19500 | 0.3326 | 0.8974 |
| 0.3344 | 49.4 | 19600 | 0.3325 | 0.8974 |
| 0.3207 | 49.65 | 19700 | 0.3326 | 0.8974 |
| 0.3299 | 49.91 | 19800 | 0.3326 | 0.8974 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.0.post302
- Datasets 2.14.5
- Tokenizers 0.13.3
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