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hubert_zeroth_gpu

Automatic Speech Recognition
Transformers
PyTorch
hubert
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - zeroth_korean_asr
metrics:
  - wer
model-index:
  - name: hubert_zeroth_gpu
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: zeroth_korean_asr
          type: zeroth_korean_asr
          config: clean
          split: train
          args: clean
        metrics:
          - name: Wer
            type: wer
            value: 1

hubert_zeroth_gpu

This model is a fine-tuned version of facebook/hubert-base-ls960 on the zeroth_korean_asr dataset. It achieves the following results on the evaluation set:

  • Loss: 4.8302
  • Wer: 1.0

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: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
26.5222 0.14 100 10.9084 1.0
6.6076 0.29 200 4.8783 1.0
4.8383 0.43 300 4.8768 1.0
4.8372 0.57 400 4.8608 1.0
4.8298 0.72 500 4.8625 1.0
4.8377 0.86 600 4.8646 1.0
4.829 1.01 700 4.8472 1.0
4.8282 1.15 800 4.8435 1.0
4.8282 1.29 900 4.8438 1.0
4.8299 1.44 1000 4.8540 1.0
4.8276 1.58 1100 4.8408 1.0
4.8306 1.72 1200 4.8390 1.0
4.8315 1.87 1300 4.8426 1.0
4.8296 2.01 1400 4.8418 1.0
4.829 2.16 1500 4.8475 1.0
4.8324 2.3 1600 4.8409 1.0
4.8299 2.44 1700 4.8360 1.0
4.8285 2.59 1800 4.8419 1.0
4.8267 2.73 1900 4.8355 1.0
4.8232 2.87 2000 4.8445 1.0
4.8179 3.02 2100 4.8390 1.0
4.8248 3.16 2200 4.8506 1.0
4.8184 3.3 2300 4.8392 1.0
4.8268 3.45 2400 4.8509 1.0
4.8315 3.59 2500 4.8469 1.0
4.8249 3.74 2600 4.8457 1.0
4.8244 3.88 2700 4.8414 1.0
4.8226 4.02 2800 4.8333 1.0
4.8275 4.17 2900 4.8344 1.0
4.8218 4.31 3000 4.8351 1.0
4.8199 4.45 3100 4.8386 1.0
4.825 4.6 3200 4.8344 1.0
4.828 4.74 3300 4.8372 1.0
4.8228 4.89 3400 4.8349 1.0
4.8264 5.03 3500 4.8344 1.0
4.8237 5.17 3600 4.8332 1.0
4.8269 5.32 3700 4.8376 1.0
4.833 5.46 3800 4.8380 1.0
4.8188 5.6 3900 4.8352 1.0
4.8208 5.75 4000 4.8354 1.0
4.8177 5.89 4100 4.8291 1.0
4.8208 6.03 4200 4.8500 1.0
4.8242 6.18 4300 4.8369 1.0
4.8222 6.32 4400 4.8366 1.0
4.8259 6.47 4500 4.8369 1.0
4.8231 6.61 4600 4.8319 1.0
4.825 6.75 4700 4.8363 1.0
4.8245 6.9 4800 4.8420 1.0
4.8139 7.04 4900 4.8427 1.0
4.8202 7.18 5000 4.8393 1.0
4.8196 7.33 5100 4.8380 1.0
4.8199 7.47 5200 4.8364 1.0
4.8264 7.61 5300 4.8414 1.0
4.8259 7.76 5400 4.8397 1.0
4.8215 7.9 5500 4.8376 1.0
4.8198 8.05 5600 4.8344 1.0
4.828 8.19 5700 4.8314 1.0
4.8246 8.33 5800 4.8361 1.0
4.8167 8.48 5900 4.8336 1.0
4.8174 8.62 6000 4.8345 1.0
4.8283 8.76 6100 4.8363 1.0
4.8231 8.91 6200 4.8345 1.0
4.8191 9.05 6300 4.8327 1.0
4.8144 9.2 6400 4.8299 1.0
4.8206 9.34 6500 4.8281 1.0
4.822 9.48 6600 4.8329 1.0
4.8228 9.63 6700 4.8309 1.0
4.8239 9.77 6800 4.8348 1.0
4.8245 9.91 6900 4.8309 1.0
4.8173 10.06 7000 4.8303 1.0
4.8188 10.2 7100 4.8335 1.0
4.8208 10.34 7200 4.8290 1.0
4.8228 10.49 7300 4.8316 1.0
4.8226 10.63 7400 4.8272 1.0
4.824 10.78 7500 4.8309 1.0
4.8175 10.92 7600 4.8317 1.0
4.8234 11.06 7700 4.8271 1.0
4.8188 11.21 7800 4.8291 1.0
4.8182 11.35 7900 4.8340 1.0
4.8224 11.49 8000 4.8309 1.0
4.8207 11.64 8100 4.8308 1.0
4.8207 11.78 8200 4.8301 1.0
4.822 11.93 8300 4.8281 1.0
4.8199 12.07 8400 4.8301 1.0
4.8198 12.21 8500 4.8337 1.0
4.8212 12.36 8600 4.8310 1.0
4.8211 12.5 8700 4.8304 1.0
4.8226 12.64 8800 4.8303 1.0
4.8224 12.79 8900 4.8312 1.0
4.8146 12.93 9000 4.8362 1.0
4.8173 13.07 9100 4.8321 1.0
4.816 13.22 9200 4.8347 1.0
4.8219 13.36 9300 4.8377 1.0
4.8251 13.51 9400 4.8403 1.0
4.8173 13.65 9500 4.8387 1.0
4.8226 13.79 9600 4.8375 1.0
4.8137 13.94 9700 4.8364 1.0
4.819 14.08 9800 4.8323 1.0
4.8258 14.22 9900 4.8329 1.0
4.8097 14.37 10000 4.8293 1.0
4.8247 14.51 10100 4.8311 1.0
4.8197 14.66 10200 4.8306 1.0
4.8201 14.8 10300 4.8308 1.0
4.8158 14.94 10400 4.8319 1.0
4.818 15.09 10500 4.8306 1.0
4.8216 15.23 10600 4.8343 1.0
4.8096 15.37 10700 4.8326 1.0
4.8248 15.52 10800 4.8323 1.0
4.8178 15.66 10900 4.8358 1.0
4.8191 15.8 11000 4.8338 1.0
4.8248 15.95 11100 4.8359 1.0
4.8095 16.09 11200 4.8392 1.0
4.8196 16.24 11300 4.8374 1.0
4.827 16.38 11400 4.8346 1.0
4.8165 16.52 11500 4.8365 1.0
4.8206 16.67 11600 4.8344 1.0
4.8169 16.81 11700 4.8344 1.0
4.8164 16.95 11800 4.8390 1.0
4.8159 17.1 11900 4.8367 1.0
4.8202 17.24 12000 4.8375 1.0
4.8156 17.39 12100 4.8362 1.0
4.8174 17.53 12200 4.8410 1.0
4.8188 17.67 12300 4.8323 1.0
4.8167 17.82 12400 4.8319 1.0
4.8229 17.96 12500 4.8347 1.0
4.8179 18.1 12600 4.8320 1.0
4.8182 18.25 12700 4.8384 1.0
4.8151 18.39 12800 4.8374 1.0
4.8212 18.53 12900 4.8346 1.0
4.8241 18.68 13000 4.8344 1.0
4.8184 18.82 13100 4.8352 1.0
4.8174 18.97 13200 4.8357 1.0
4.8092 19.11 13300 4.8332 1.0
4.8149 19.25 13400 4.8347 1.0
4.813 19.4 13500 4.8376 1.0
4.8226 19.54 13600 4.8343 1.0
4.8175 19.68 13700 4.8320 1.0
4.8203 19.83 13800 4.8339 1.0
4.8227 19.97 13900 4.8324 1.0
4.8177 20.11 14000 4.8356 1.0
4.824 20.26 14100 4.8339 1.0
4.815 20.4 14200 4.8342 1.0
4.8189 20.55 14300 4.8340 1.0
4.8115 20.69 14400 4.8319 1.0
4.8162 20.83 14500 4.8288 1.0
4.8183 20.98 14600 4.8321 1.0
4.8189 21.12 14700 4.8315 1.0
4.8123 21.26 14800 4.8311 1.0
4.8165 21.41 14900 4.8321 1.0
4.8247 21.55 15000 4.8309 1.0
4.8165 21.7 15100 4.8313 1.0
4.815 21.84 15200 4.8354 1.0
4.8234 21.98 15300 4.8300 1.0
4.8134 22.13 15400 4.8284 1.0
4.8178 22.27 15500 4.8298 1.0
4.8128 22.41 15600 4.8309 1.0
4.8185 22.56 15700 4.8291 1.0
4.8177 22.7 15800 4.8288 1.0
4.8208 22.84 15900 4.8306 1.0
4.8183 22.99 16000 4.8277 1.0
4.8135 23.13 16100 4.8286 1.0
4.8116 23.28 16200 4.8275 1.0
4.816 23.42 16300 4.8290 1.0
4.8203 23.56 16400 4.8292 1.0
4.8198 23.71 16500 4.8299 1.0
4.8203 23.85 16600 4.8294 1.0
4.8177 23.99 16700 4.8286 1.0
4.8153 24.14 16800 4.8275 1.0
4.8201 24.28 16900 4.8259 1.0
4.8189 24.43 17000 4.8289 1.0
4.8219 24.57 17100 4.8280 1.0
4.8148 24.71 17200 4.8284 1.0
4.8113 24.86 17300 4.8286 1.0
4.8133 25.0 17400 4.8293 1.0
4.8164 25.14 17500 4.8302 1.0
4.8231 25.29 17600 4.8278 1.0
4.8136 25.43 17700 4.8296 1.0
4.8118 25.57 17800 4.8288 1.0
4.8139 25.72 17900 4.8280 1.0
4.8144 25.86 18000 4.8282 1.0
4.8206 26.01 18100 4.8279 1.0
4.8096 26.15 18200 4.8281 1.0
4.8177 26.29 18300 4.8271 1.0
4.8222 26.44 18400 4.8289 1.0
4.8148 26.58 18500 4.8282 1.0
4.8148 26.72 18600 4.8277 1.0
4.819 26.87 18700 4.8283 1.0
4.8138 27.01 18800 4.8290 1.0
4.8094 27.16 18900 4.8292 1.0
4.8236 27.3 19000 4.8282 1.0
4.8208 27.44 19100 4.8293 1.0
4.816 27.59 19200 4.8281 1.0
4.8103 27.73 19300 4.8294 1.0
4.8152 27.87 19400 4.8297 1.0
4.8158 28.02 19500 4.8305 1.0
4.8121 28.16 19600 4.8294 1.0
4.8199 28.3 19700 4.8292 1.0
4.8185 28.45 19800 4.8288 1.0
4.8199 28.59 19900 4.8288 1.0
4.8102 28.74 20000 4.8292 1.0
4.8168 28.88 20100 4.8291 1.0
4.8117 29.02 20200 4.8304 1.0
4.8156 29.17 20300 4.8295 1.0
4.8126 29.31 20400 4.8296 1.0
4.8193 29.45 20500 4.8302 1.0
4.8175 29.6 20600 4.8301 1.0
4.8167 29.74 20700 4.8301 1.0
4.8137 29.89 20800 4.8302 1.0

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.13.0+cu117
  • Datasets 2.0.0
  • Tokenizers 0.13.2