Hugging Face's logo

JBZhang2342
/
speecht5_tts

Text-to-Audio
Transformers
TensorBoard
Safetensors
English
speecht5
en_accent,mozilla,t5,common_voice_1_0
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
speecht5_tts / README.md
JBZhang2342's picture
JBZhang2342
Model save
d015c78
preview code
|
raw
history blame
No virus
18.3 kB
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