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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
Inference Endpoints
Model card Files Metrics Community
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  1. README.md +209 -8
  2. model.safetensors +1 -1
  3. runs/Nov19_21-50-59_yifanhua-threadripper/events.out.tfevents.1700448660.yifanhua-threadripper +2 -2
README.md CHANGED
@@ -1,24 +1,23 @@
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  ---
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- language:
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- - en
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  license: mit
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  base_model: microsoft/speecht5_tts
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  tags:
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- - en_accent,mozilla,t5,common_voice_1_0
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  - generated_from_trainer
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  datasets:
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- - mozilla-foundation/common_voice_1_0
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  model-index:
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- - name: SpeechT5 TTS English Accented
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # SpeechT5 TTS English Accented
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- This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice dataset.
 
 
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  ## Model description
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@@ -44,11 +43,213 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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- - training_steps: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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  ### Framework versions
 
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  ---
 
 
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  license: mit
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  base_model: microsoft/speecht5_tts
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  tags:
 
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  - generated_from_trainer
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  datasets:
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+ - common_voice_13_0
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  model-index:
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+ - name: speecht5_tts
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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+ # speecht5_tts
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+ This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5720
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 500
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+ - training_steps: 20000
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:-----:|:---------------:|
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+ | 1.0398 | 1.0 | 100 | 0.7784 |
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+ | 0.8663 | 2.0 | 200 | 0.7059 |
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+ | 0.7824 | 3.0 | 300 | 0.6734 |
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+ | 0.7181 | 4.0 | 400 | 0.5776 |
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+ | 0.6418 | 5.0 | 500 | 0.5584 |
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+ | 0.6127 | 6.0 | 600 | 0.5452 |
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+ | 0.5939 | 7.0 | 700 | 0.5386 |
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+ | 0.5924 | 8.0 | 800 | 0.5420 |
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+ | 0.5887 | 9.0 | 900 | 0.5392 |
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+ | 0.5769 | 10.0 | 1000 | 0.5319 |
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+ | 0.578 | 11.0 | 1100 | 0.5345 |
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+ | 0.5799 | 12.0 | 1200 | 0.5257 |
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+ | 0.5614 | 13.0 | 1300 | 0.5342 |
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+ | 0.554 | 14.0 | 1400 | 0.5223 |
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+ | 0.551 | 15.0 | 1500 | 0.5209 |
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+ | 0.5587 | 16.0 | 1600 | 0.5221 |
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+ | 0.5485 | 17.0 | 1700 | 0.5193 |
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+ | 0.5354 | 18.0 | 1800 | 0.5216 |
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+ | 0.5417 | 19.0 | 1900 | 0.5260 |
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+ | 0.5319 | 20.0 | 2000 | 0.5218 |
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+ | 0.5354 | 21.0 | 2100 | 0.5212 |
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+ | 0.5316 | 22.0 | 2200 | 0.5233 |
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+ | 0.5295 | 23.0 | 2300 | 0.5222 |
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+ | 0.5407 | 24.0 | 2400 | 0.5317 |
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+ | 0.5309 | 25.0 | 2500 | 0.5258 |
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+ | 0.5196 | 26.0 | 2600 | 0.5317 |
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+ | 0.5195 | 27.0 | 2700 | 0.5325 |
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+ | 0.5134 | 28.0 | 2800 | 0.5193 |
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+ | 0.5143 | 29.0 | 2900 | 0.5254 |
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+ | 0.5227 | 30.0 | 3000 | 0.5260 |
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+ | 0.5157 | 31.0 | 3100 | 0.5311 |
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+ | 0.5214 | 32.0 | 3200 | 0.5292 |
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+ | 0.5196 | 33.0 | 3300 | 0.5283 |
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+ | 0.522 | 34.0 | 3400 | 0.5296 |
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+ | 0.5193 | 35.0 | 3500 | 0.5252 |
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+ | 0.5156 | 36.0 | 3600 | 0.5272 |
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+ | 0.5182 | 37.0 | 3700 | 0.5318 |
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+ | 0.5079 | 38.0 | 3800 | 0.5289 |
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+ | 0.5103 | 39.0 | 3900 | 0.5374 |
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+ | 0.5044 | 40.0 | 4000 | 0.5289 |
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+ | 0.5021 | 41.0 | 4100 | 0.5372 |
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+ | 0.5202 | 42.0 | 4200 | 0.5384 |
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+ | 0.5022 | 43.0 | 4300 | 0.5281 |
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+ | 0.498 | 44.0 | 4400 | 0.5327 |
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+ | 0.4991 | 45.0 | 4500 | 0.5351 |
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+ | 0.4972 | 46.0 | 4600 | 0.5383 |
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+ | 0.5075 | 47.0 | 4700 | 0.5319 |
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+ | 0.5063 | 48.0 | 4800 | 0.5365 |
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+ | 0.4964 | 49.0 | 4900 | 0.5361 |
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+ | 0.5021 | 50.0 | 5000 | 0.5353 |
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+ | 0.4914 | 52.0 | 5200 | 0.5398 |
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+ | 0.5016 | 53.0 | 5300 | 0.5499 |
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+ | 0.4911 | 54.0 | 5400 | 0.5484 |
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+ | 0.4851 | 59.0 | 5900 | 0.5444 |
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+ | 0.4898 | 60.0 | 6000 | 0.5461 |
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+ | 0.4824 | 64.0 | 6400 | 0.5466 |
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+ | 0.481 | 67.0 | 6700 | 0.5516 |
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+ | 0.4779 | 74.0 | 7400 | 0.5467 |
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+ | 0.4778 | 75.0 | 7500 | 0.5513 |
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+ | 0.4618 | 97.0 | 9700 | 0.5555 |
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+ | 0.485 | 100.0 | 10000 | 0.5591 |
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+ | 0.4677 | 105.0 | 10500 | 0.5530 |
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+ | 0.4705 | 106.0 | 10600 | 0.5555 |
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+ | 0.4596 | 107.0 | 10700 | 0.5567 |
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+ | 0.4689 | 108.0 | 10800 | 0.5552 |
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+ | 0.4698 | 109.0 | 10900 | 0.5591 |
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+ | 0.4767 | 110.0 | 11000 | 0.5583 |
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+ | 0.466 | 111.0 | 11100 | 0.5594 |
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+ | 0.4792 | 112.0 | 11200 | 0.5604 |
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+ | 0.4692 | 113.0 | 11300 | 0.5635 |
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+ | 0.4675 | 114.0 | 11400 | 0.5597 |
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+ | 0.467 | 115.0 | 11500 | 0.5587 |
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+ | 0.4653 | 116.0 | 11600 | 0.5610 |
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+ | 0.468 | 117.0 | 11700 | 0.5608 |
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+ | 0.4649 | 118.0 | 11800 | 0.5625 |
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+ | 0.4614 | 119.0 | 11900 | 0.5606 |
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+ | 0.4663 | 120.0 | 12000 | 0.5626 |
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+ | 0.4654 | 121.0 | 12100 | 0.5623 |
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+ | 0.4582 | 122.0 | 12200 | 0.5613 |
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+ | 0.4621 | 123.0 | 12300 | 0.5650 |
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+ | 0.459 | 124.0 | 12400 | 0.5617 |
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+ | 0.4538 | 125.0 | 12500 | 0.5609 |
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+ | 0.4647 | 128.0 | 12800 | 0.5585 |
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+ | 0.4616 | 129.0 | 12900 | 0.5638 |
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+ | 0.4691 | 130.0 | 13000 | 0.5658 |
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+ | 0.4711 | 138.0 | 13800 | 0.5650 |
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+ | 0.4595 | 139.0 | 13900 | 0.5678 |
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+ | 0.4586 | 140.0 | 14000 | 0.5638 |
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+ | 0.4562 | 141.0 | 14100 | 0.5668 |
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+ | 0.456 | 142.0 | 14200 | 0.5673 |
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+ | 0.4561 | 143.0 | 14300 | 0.5694 |
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+ | 0.4562 | 144.0 | 14400 | 0.5685 |
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+ | 0.4583 | 145.0 | 14500 | 0.5642 |
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+ | 0.4554 | 158.0 | 15800 | 0.5695 |
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+ | 0.4532 | 159.0 | 15900 | 0.5728 |
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+ | 0.4471 | 160.0 | 16000 | 0.5746 |
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+ | 0.4528 | 161.0 | 16100 | 0.5715 |
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+ | 0.4539 | 164.0 | 16400 | 0.5673 |
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+ | 0.4523 | 200.0 | 20000 | 0.5720 |
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  ### Framework versions
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