metadata
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: speecht5_tts
results: []
speecht5_tts
This model is a fine-tuned version of microsoft/speecht5_tts on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5888
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: 1e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 80000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 1.25 | 250 | 0.6821 |
0.8074 | 2.5 | 500 | 0.5618 |
0.8074 | 3.75 | 750 | 0.5362 |
0.5957 | 5.0 | 1000 | 0.5279 |
0.5957 | 6.25 | 1250 | 0.5183 |
0.567 | 7.5 | 1500 | 0.5216 |
0.567 | 8.75 | 1750 | 0.5096 |
0.5507 | 10.0 | 2000 | 0.5097 |
0.5507 | 11.25 | 2250 | 0.5111 |
0.5396 | 12.5 | 2500 | 0.5090 |
0.5396 | 13.75 | 2750 | 0.5072 |
0.5348 | 15.0 | 3000 | 0.5099 |
0.5348 | 16.25 | 3250 | 0.5098 |
0.5269 | 17.5 | 3500 | 0.5094 |
0.5269 | 18.75 | 3750 | 0.5081 |
0.5209 | 20.0 | 4000 | 0.5088 |
0.5209 | 21.25 | 4250 | 0.5100 |
0.5176 | 22.5 | 4500 | 0.5084 |
0.5176 | 23.75 | 4750 | 0.5107 |
0.5129 | 25.0 | 5000 | 0.5131 |
0.5129 | 26.25 | 5250 | 0.5205 |
0.5081 | 27.5 | 5500 | 0.5174 |
0.5081 | 28.75 | 5750 | 0.5131 |
0.5033 | 30.0 | 6000 | 0.5127 |
0.5033 | 31.25 | 6250 | 0.5272 |
0.505 | 32.5 | 6500 | 0.5208 |
0.505 | 33.75 | 6750 | 0.5263 |
0.4933 | 35.0 | 7000 | 0.5257 |
0.4933 | 36.25 | 7250 | 0.5270 |
0.4929 | 37.5 | 7500 | 0.5240 |
0.4929 | 38.75 | 7750 | 0.5272 |
0.4942 | 40.0 | 8000 | 0.5266 |
0.4942 | 41.25 | 8250 | 0.5364 |
0.4883 | 42.5 | 8500 | 0.5339 |
0.4883 | 43.75 | 8750 | 0.5313 |
0.4874 | 45.0 | 9000 | 0.5335 |
0.4874 | 46.25 | 9250 | 0.5300 |
0.4849 | 47.5 | 9500 | 0.5357 |
0.4849 | 48.75 | 9750 | 0.5361 |
0.483 | 50.0 | 10000 | 0.5306 |
0.483 | 51.25 | 10250 | 0.5330 |
0.4812 | 52.5 | 10500 | 0.5234 |
0.4812 | 53.75 | 10750 | 0.5248 |
0.484 | 55.0 | 11000 | 0.5364 |
0.484 | 56.25 | 11250 | 0.5381 |
0.4786 | 57.5 | 11500 | 0.5340 |
0.4786 | 58.75 | 11750 | 0.5385 |
0.4794 | 60.0 | 12000 | 0.5365 |
0.4794 | 61.25 | 12250 | 0.5411 |
0.4719 | 62.5 | 12500 | 0.5358 |
0.4719 | 63.75 | 12750 | 0.5377 |
0.479 | 65.0 | 13000 | 0.5378 |
0.479 | 66.25 | 13250 | 0.5426 |
0.474 | 67.5 | 13500 | 0.5370 |
0.474 | 68.75 | 13750 | 0.5402 |
0.473 | 70.0 | 14000 | 0.5400 |
0.473 | 71.25 | 14250 | 0.5453 |
0.4717 | 72.5 | 14500 | 0.5453 |
0.4717 | 73.75 | 14750 | 0.5419 |
0.4663 | 75.0 | 15000 | 0.5407 |
0.4663 | 76.25 | 15250 | 0.5427 |
0.4631 | 77.5 | 15500 | 0.5408 |
0.4631 | 78.75 | 15750 | 0.5408 |
0.4665 | 80.0 | 16000 | 0.5400 |
0.4665 | 81.25 | 16250 | 0.5486 |
0.4658 | 82.5 | 16500 | 0.5429 |
0.4658 | 83.75 | 16750 | 0.5395 |
0.4657 | 85.0 | 17000 | 0.5361 |
0.4657 | 86.25 | 17250 | 0.5415 |
0.4647 | 87.5 | 17500 | 0.5464 |
0.4647 | 88.75 | 17750 | 0.5428 |
0.4646 | 90.0 | 18000 | 0.5412 |
0.4646 | 91.25 | 18250 | 0.5478 |
0.4649 | 92.5 | 18500 | 0.5479 |
0.4649 | 93.75 | 18750 | 0.5463 |
0.4622 | 95.0 | 19000 | 0.5447 |
0.4622 | 96.25 | 19250 | 0.5440 |
0.4598 | 97.5 | 19500 | 0.5524 |
0.4598 | 98.75 | 19750 | 0.5518 |
0.461 | 100.0 | 20000 | 0.5470 |
0.461 | 101.25 | 20250 | 0.5507 |
0.4608 | 102.5 | 20500 | 0.5486 |
0.4608 | 103.75 | 20750 | 0.5481 |
0.4565 | 105.0 | 21000 | 0.5509 |
0.4565 | 106.25 | 21250 | 0.5532 |
0.4561 | 107.5 | 21500 | 0.5488 |
0.4561 | 108.75 | 21750 | 0.5448 |
0.4577 | 110.0 | 22000 | 0.5492 |
0.4577 | 111.25 | 22250 | 0.5539 |
0.4545 | 112.5 | 22500 | 0.5497 |
0.4545 | 113.75 | 22750 | 0.5536 |
0.4548 | 115.0 | 23000 | 0.5497 |
0.4548 | 116.25 | 23250 | 0.5520 |
0.4555 | 117.5 | 23500 | 0.5445 |
0.4555 | 118.75 | 23750 | 0.5518 |
0.456 | 120.0 | 24000 | 0.5520 |
0.456 | 121.25 | 24250 | 0.5512 |
0.4526 | 122.5 | 24500 | 0.5516 |
0.4526 | 123.75 | 24750 | 0.5534 |
0.4528 | 125.0 | 25000 | 0.5524 |
0.4528 | 126.25 | 25250 | 0.5512 |
0.4506 | 127.5 | 25500 | 0.5530 |
0.4506 | 128.75 | 25750 | 0.5534 |
0.4512 | 130.0 | 26000 | 0.5528 |
0.4512 | 131.25 | 26250 | 0.5524 |
0.4504 | 132.5 | 26500 | 0.5569 |
0.4504 | 133.75 | 26750 | 0.5489 |
0.4472 | 135.0 | 27000 | 0.5530 |
0.4472 | 136.25 | 27250 | 0.5571 |
0.447 | 137.5 | 27500 | 0.5566 |
0.447 | 138.75 | 27750 | 0.5562 |
0.4465 | 140.0 | 28000 | 0.5546 |
0.4465 | 141.25 | 28250 | 0.5579 |
0.4455 | 142.5 | 28500 | 0.5557 |
0.4455 | 143.75 | 28750 | 0.5533 |
0.4487 | 145.0 | 29000 | 0.5528 |
0.4487 | 146.25 | 29250 | 0.5576 |
0.445 | 147.5 | 29500 | 0.5574 |
0.445 | 148.75 | 29750 | 0.5593 |
0.4455 | 150.0 | 30000 | 0.5579 |
0.4455 | 151.25 | 30250 | 0.5539 |
0.4467 | 152.5 | 30500 | 0.5551 |
0.4467 | 153.75 | 30750 | 0.5654 |
0.4448 | 155.0 | 31000 | 0.5555 |
0.4448 | 156.25 | 31250 | 0.5602 |
0.4438 | 157.5 | 31500 | 0.5595 |
0.4438 | 158.75 | 31750 | 0.5575 |
0.4426 | 160.0 | 32000 | 0.5592 |
0.4426 | 161.25 | 32250 | 0.5618 |
0.4451 | 162.5 | 32500 | 0.5628 |
0.4451 | 163.75 | 32750 | 0.5623 |
0.4406 | 165.0 | 33000 | 0.5583 |
0.4406 | 166.25 | 33250 | 0.5575 |
0.443 | 167.5 | 33500 | 0.5580 |
0.443 | 168.75 | 33750 | 0.5606 |
0.4423 | 170.0 | 34000 | 0.5575 |
0.4423 | 171.25 | 34250 | 0.5616 |
0.4379 | 172.5 | 34500 | 0.5660 |
0.4379 | 173.75 | 34750 | 0.5600 |
0.4424 | 175.0 | 35000 | 0.5624 |
0.4424 | 176.25 | 35250 | 0.5656 |
0.4414 | 177.5 | 35500 | 0.5653 |
0.4414 | 178.75 | 35750 | 0.5645 |
0.4401 | 180.0 | 36000 | 0.5608 |
0.4401 | 181.25 | 36250 | 0.5639 |
0.4374 | 182.5 | 36500 | 0.5659 |
0.4374 | 183.75 | 36750 | 0.5655 |
0.443 | 185.0 | 37000 | 0.5660 |
0.443 | 186.25 | 37250 | 0.5664 |
0.4406 | 187.5 | 37500 | 0.5676 |
0.4406 | 188.75 | 37750 | 0.5631 |
0.4372 | 190.0 | 38000 | 0.5640 |
0.4372 | 191.25 | 38250 | 0.5661 |
0.4403 | 192.5 | 38500 | 0.5656 |
0.4403 | 193.75 | 38750 | 0.5696 |
0.4339 | 195.0 | 39000 | 0.5651 |
0.4339 | 196.25 | 39250 | 0.5642 |
0.4403 | 197.5 | 39500 | 0.5661 |
0.4403 | 198.75 | 39750 | 0.5659 |
0.4359 | 200.0 | 40000 | 0.5656 |
0.4359 | 201.25 | 40250 | 0.5692 |
0.4373 | 202.5 | 40500 | 0.5646 |
0.4373 | 203.75 | 40750 | 0.5695 |
0.4362 | 205.0 | 41000 | 0.5658 |
0.4362 | 206.25 | 41250 | 0.5696 |
0.4354 | 207.5 | 41500 | 0.5665 |
0.4354 | 208.75 | 41750 | 0.5684 |
0.4359 | 210.0 | 42000 | 0.5672 |
0.4359 | 211.25 | 42250 | 0.5665 |
0.4334 | 212.5 | 42500 | 0.5690 |
0.4334 | 213.75 | 42750 | 0.5645 |
0.436 | 215.0 | 43000 | 0.5704 |
0.436 | 216.25 | 43250 | 0.5696 |
0.4373 | 217.5 | 43500 | 0.5689 |
0.4373 | 218.75 | 43750 | 0.5698 |
0.4353 | 220.0 | 44000 | 0.5706 |
0.4353 | 221.25 | 44250 | 0.5679 |
0.4344 | 222.5 | 44500 | 0.5676 |
0.4344 | 223.75 | 44750 | 0.5709 |
0.4357 | 225.0 | 45000 | 0.5717 |
0.4357 | 226.25 | 45250 | 0.5646 |
0.4319 | 227.5 | 45500 | 0.5676 |
0.4319 | 228.75 | 45750 | 0.5709 |
0.4333 | 230.0 | 46000 | 0.5746 |
0.4333 | 231.25 | 46250 | 0.5734 |
0.4322 | 232.5 | 46500 | 0.5732 |
0.4322 | 233.75 | 46750 | 0.5726 |
0.4299 | 235.0 | 47000 | 0.5659 |
0.4299 | 236.25 | 47250 | 0.5723 |
0.4308 | 237.5 | 47500 | 0.5709 |
0.4308 | 238.75 | 47750 | 0.5735 |
0.4323 | 240.0 | 48000 | 0.5688 |
0.4323 | 241.25 | 48250 | 0.5724 |
0.4348 | 242.5 | 48500 | 0.5740 |
0.4348 | 243.75 | 48750 | 0.5762 |
0.4292 | 245.0 | 49000 | 0.5706 |
0.4292 | 246.25 | 49250 | 0.5736 |
0.4328 | 247.5 | 49500 | 0.5722 |
0.4328 | 248.75 | 49750 | 0.5760 |
0.4321 | 250.0 | 50000 | 0.5710 |
0.4321 | 251.25 | 50250 | 0.5754 |
0.4275 | 252.5 | 50500 | 0.5721 |
0.4275 | 253.75 | 50750 | 0.5729 |
0.4301 | 255.0 | 51000 | 0.5737 |
0.4301 | 256.25 | 51250 | 0.5731 |
0.4304 | 257.5 | 51500 | 0.5736 |
0.4304 | 258.75 | 51750 | 0.5744 |
0.4298 | 260.0 | 52000 | 0.5787 |
0.4298 | 261.25 | 52250 | 0.5767 |
0.4296 | 262.5 | 52500 | 0.5750 |
0.4296 | 263.75 | 52750 | 0.5739 |
0.4308 | 265.0 | 53000 | 0.5754 |
0.4308 | 266.25 | 53250 | 0.5726 |
0.4299 | 267.5 | 53500 | 0.5770 |
0.4299 | 268.75 | 53750 | 0.5775 |
0.4282 | 270.0 | 54000 | 0.5777 |
0.4282 | 271.25 | 54250 | 0.5800 |
0.4273 | 272.5 | 54500 | 0.5789 |
0.4273 | 273.75 | 54750 | 0.5787 |
0.4284 | 275.0 | 55000 | 0.5757 |
0.4284 | 276.25 | 55250 | 0.5755 |
0.4267 | 277.5 | 55500 | 0.5777 |
0.4267 | 278.75 | 55750 | 0.5764 |
0.4241 | 280.0 | 56000 | 0.5764 |
0.4241 | 281.25 | 56250 | 0.5772 |
0.43 | 282.5 | 56500 | 0.5782 |
0.43 | 283.75 | 56750 | 0.5777 |
0.4273 | 285.0 | 57000 | 0.5787 |
0.4273 | 286.25 | 57250 | 0.5789 |
0.4261 | 287.5 | 57500 | 0.5769 |
0.4261 | 288.75 | 57750 | 0.5766 |
0.4244 | 290.0 | 58000 | 0.5792 |
0.4244 | 291.25 | 58250 | 0.5788 |
0.4237 | 292.5 | 58500 | 0.5770 |
0.4237 | 293.75 | 58750 | 0.5804 |
0.427 | 295.0 | 59000 | 0.5775 |
0.427 | 296.25 | 59250 | 0.5818 |
0.4259 | 297.5 | 59500 | 0.5808 |
0.4259 | 298.75 | 59750 | 0.5776 |
0.4248 | 300.0 | 60000 | 0.5789 |
0.4248 | 301.25 | 60250 | 0.5793 |
0.4269 | 302.5 | 60500 | 0.5762 |
0.4269 | 303.75 | 60750 | 0.5829 |
0.428 | 305.0 | 61000 | 0.5820 |
0.428 | 306.25 | 61250 | 0.5823 |
0.4246 | 307.5 | 61500 | 0.5848 |
0.4246 | 308.75 | 61750 | 0.5784 |
0.4273 | 310.0 | 62000 | 0.5791 |
0.4273 | 311.25 | 62250 | 0.5798 |
0.4261 | 312.5 | 62500 | 0.5791 |
0.4261 | 313.75 | 62750 | 0.5805 |
0.4275 | 315.0 | 63000 | 0.5812 |
0.4275 | 316.25 | 63250 | 0.5821 |
0.4261 | 317.5 | 63500 | 0.5820 |
0.4261 | 318.75 | 63750 | 0.5751 |
0.4254 | 320.0 | 64000 | 0.5800 |
0.4254 | 321.25 | 64250 | 0.5816 |
0.4226 | 322.5 | 64500 | 0.5824 |
0.4226 | 323.75 | 64750 | 0.5812 |
0.4263 | 325.0 | 65000 | 0.5841 |
0.4263 | 326.25 | 65250 | 0.5820 |
0.4198 | 327.5 | 65500 | 0.5875 |
0.4198 | 328.75 | 65750 | 0.5855 |
0.4232 | 330.0 | 66000 | 0.5834 |
0.4232 | 331.25 | 66250 | 0.5834 |
0.4252 | 332.5 | 66500 | 0.5839 |
0.4252 | 333.75 | 66750 | 0.5843 |
0.4231 | 335.0 | 67000 | 0.5858 |
0.4231 | 336.25 | 67250 | 0.5847 |
0.4234 | 337.5 | 67500 | 0.5863 |
0.4234 | 338.75 | 67750 | 0.5803 |
0.4251 | 340.0 | 68000 | 0.5842 |
0.4251 | 341.25 | 68250 | 0.5858 |
0.4244 | 342.5 | 68500 | 0.5835 |
0.4244 | 343.75 | 68750 | 0.5830 |
0.4226 | 345.0 | 69000 | 0.5834 |
0.4226 | 346.25 | 69250 | 0.5843 |
0.4221 | 347.5 | 69500 | 0.5864 |
0.4221 | 348.75 | 69750 | 0.5869 |
0.4236 | 350.0 | 70000 | 0.5847 |
0.4236 | 351.25 | 70250 | 0.5860 |
0.4262 | 352.5 | 70500 | 0.5856 |
0.4262 | 353.75 | 70750 | 0.5851 |
0.4213 | 355.0 | 71000 | 0.5869 |
0.4213 | 356.25 | 71250 | 0.5868 |
0.4235 | 357.5 | 71500 | 0.5883 |
0.4235 | 358.75 | 71750 | 0.5890 |
0.4242 | 360.0 | 72000 | 0.5869 |
0.4242 | 361.25 | 72250 | 0.5881 |
0.4221 | 362.5 | 72500 | 0.5874 |
0.4221 | 363.75 | 72750 | 0.5889 |
0.4209 | 365.0 | 73000 | 0.5890 |
0.4209 | 366.25 | 73250 | 0.5870 |
0.4189 | 367.5 | 73500 | 0.5897 |
0.4189 | 368.75 | 73750 | 0.5901 |
0.4252 | 370.0 | 74000 | 0.5885 |
0.4252 | 371.25 | 74250 | 0.5885 |
0.4226 | 372.5 | 74500 | 0.5901 |
0.4226 | 373.75 | 74750 | 0.5886 |
0.4219 | 375.0 | 75000 | 0.5872 |
0.4219 | 376.25 | 75250 | 0.5876 |
0.4196 | 377.5 | 75500 | 0.5894 |
0.4196 | 378.75 | 75750 | 0.5866 |
0.4212 | 380.0 | 76000 | 0.5899 |
0.4212 | 381.25 | 76250 | 0.5871 |
0.4207 | 382.5 | 76500 | 0.5894 |
0.4207 | 383.75 | 76750 | 0.5880 |
0.423 | 385.0 | 77000 | 0.5864 |
0.423 | 386.25 | 77250 | 0.5896 |
0.4213 | 387.5 | 77500 | 0.5909 |
0.4213 | 388.75 | 77750 | 0.5886 |
0.4211 | 390.0 | 78000 | 0.5906 |
0.4211 | 391.25 | 78250 | 0.5878 |
0.4205 | 392.5 | 78500 | 0.5883 |
0.4205 | 393.75 | 78750 | 0.5874 |
0.4244 | 395.0 | 79000 | 0.5879 |
0.4244 | 396.25 | 79250 | 0.5908 |
0.4211 | 397.5 | 79500 | 0.5893 |
0.4211 | 398.75 | 79750 | 0.5902 |
0.4243 | 400.0 | 80000 | 0.5888 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1