End of training
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README.md
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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_16_0
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model-index:
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- name: speecht5_finetuned_commonvoice_da
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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_finetuned_commonvoice_da
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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_16_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4693
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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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: 4000
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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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| 0.6003 | 5.13 | 500 | 0.5255 |
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| 0.5378 | 10.27 | 1000 | 0.4966 |
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| 0.5223 | 15.4 | 1500 | 0.4840 |
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| 0.5129 | 20.54 | 2000 | 0.4760 |
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| 0.5059 | 25.67 | 2500 | 0.4714 |
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| 0.4942 | 30.81 | 3000 | 0.4711 |
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| 0.4944 | 35.94 | 3500 | 0.4691 |
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| 0.497 | 41.08 | 4000 | 0.4693 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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"eos_token_id": 2,
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"max_length": 1876,
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"pad_token_id": 1,
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"transformers_version": "4.35.2"
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}
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model.safetensors
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runs/Jan08_09-12-08_b3c59b820b90/events.out.tfevents.1704705328.b3c59b820b90.503.0
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training_args.bin
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