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JBZhang2342
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speecht5_tts

Text-to-Audio
Transformers
TensorBoard
Safetensors
English
speecht5
en_accent,mozilla,t5,common_voice_1_0
Generated from Trainer
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speecht5_tts / README.md
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metadata
language:
  - en
license: mit
base_model: microsoft/speecht5_tts
tags:
  - en_accent,mozilla,t5,common_voice_1_0
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_1_0
model-index:
  - name: SpeechT5 TTS English Accented
    results: []

SpeechT5 TTS English Accented

This model is a fine-tuned version of microsoft/speecht5_tts on the Common Voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7806

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • 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: 30000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
No log 0.53 250 0.8506
1.0736 1.06 500 0.8219
1.0736 1.6 750 0.7713
0.8607 2.13 1000 0.7947
0.8607 2.66 1250 0.7537
0.802 3.19 1500 0.7304
0.802 3.72 1750 0.7409
0.7627 4.26 2000 0.7282
0.7627 4.79 2250 0.7224
0.7442 5.32 2500 0.7132
0.7442 5.85 2750 0.7718
0.736 6.38 3000 0.7362
0.736 6.91 3250 0.7283
0.7234 7.45 3500 0.7377
0.7234 7.98 3750 0.7226
0.6968 8.51 4000 0.7285
0.6968 9.04 4250 0.7395
0.692 9.57 4500 0.7306
0.692 10.11 4750 0.7221
0.6807 10.64 5000 0.7349
0.6807 11.17 5250 0.7310
0.6702 11.7 5500 0.7391
0.6702 12.23 5750 0.7299
0.6559 12.77 6000 0.7277
0.6559 13.3 6250 0.7453
0.6511 13.83 6500 0.7303
0.6511 14.36 6750 0.7451
0.6335 14.89 7000 0.7209
0.6335 15.43 7250 0.7421
0.6282 15.96 7500 0.7277
0.6282 16.49 7750 0.7426
0.6286 17.02 8000 0.7724
0.6286 17.55 8250 0.7310
0.6164 18.09 8500 0.7414
0.6164 18.62 8750 0.7411
0.6029 19.15 9000 0.7466
0.6029 19.68 9250 0.7267
0.5986 20.21 9500 0.7593
0.5986 20.74 9750 0.7544
0.595 21.28 10000 0.7441
0.595 21.81 10250 0.7422
0.5905 22.34 10500 0.7399
0.5905 22.87 10750 0.7494
0.5792 23.4 11000 0.7311
0.5792 23.94 11250 0.7479
0.5774 24.47 11500 0.7615
0.5774 25.0 11750 0.7578
0.5684 25.53 12000 0.7603
0.5684 26.06 12250 0.7300
0.5621 26.6 12500 0.7385
0.5621 27.13 12750 0.7447
0.5666 27.66 13000 0.7400
0.5666 28.19 13250 0.7518
0.5525 28.72 13500 0.7462
0.5525 29.26 13750 0.7351
0.5471 29.79 14000 0.7673
0.5471 30.32 14250 0.7325
0.5449 30.85 14500 0.7455
0.5449 31.38 14750 0.7473
0.5349 31.91 15000 0.7549
0.5349 32.45 15250 0.7513
0.5345 32.98 15500 0.7472
0.5345 33.51 15750 0.7542
0.5285 34.04 16000 0.7513
0.5285 34.57 16250 0.7466
0.522 35.11 16500 0.7627
0.522 35.64 16750 0.7609
0.5209 36.17 17000 0.7616
0.5209 36.7 17250 0.7612
0.5151 37.23 17500 0.7601
0.5151 37.77 17750 0.7590
0.5088 38.3 18000 0.7568
0.5088 38.83 18250 0.7551
0.5105 39.36 18500 0.7688
0.5105 39.89 18750 0.7631
0.5046 40.43 19000 0.7654
0.5046 40.96 19250 0.7749
0.5029 41.49 19500 0.7617
0.5029 42.02 19750 0.7735
0.4969 42.55 20000 0.7763
0.4969 43.09 20250 0.7484
0.497 43.62 20500 0.7606
0.497 44.15 20750 0.7726
0.4889 44.68 21000 0.7564
0.4889 45.21 21250 0.7694
0.4842 45.74 21500 0.7639
0.4842 46.28 21750 0.7784
0.4829 46.81 22000 0.7817
0.4829 47.34 22250 0.7727
0.4772 47.87 22500 0.7661
0.4772 48.4 22750 0.7630
0.477 48.94 23000 0.7640
0.477 49.47 23250 0.7730
0.4766 50.0 23500 0.7708
0.4766 50.53 23750 0.7716
0.4717 51.06 24000 0.7670
0.4717 51.6 24250 0.7671
0.4686 52.13 24500 0.7711
0.4686 52.66 24750 0.7704
0.4685 53.19 25000 0.7775
0.4685 53.72 25250 0.7690
0.4635 54.26 25500 0.7839
0.4635 54.79 25750 0.7746
0.4617 55.32 26000 0.7738
0.4617 55.85 26250 0.7753
0.4549 56.38 26500 0.7830
0.4549 56.91 26750 0.7777
0.4564 57.45 27000 0.7758
0.4564 57.98 27250 0.7728
0.4546 58.51 27500 0.7772
0.4546 59.04 27750 0.7795
0.4511 59.57 28000 0.7754
0.4511 60.11 28250 0.7867
0.4467 60.64 28500 0.7838
0.4467 61.17 28750 0.7858
0.4512 61.7 29000 0.7758
0.4512 62.23 29250 0.7819
0.4497 62.77 29500 0.7871
0.4497 63.3 29750 0.7817
0.4463 63.83 30000 0.7806

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.14.1