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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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- text-to-speech |
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datasets: |
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- voxpopuli |
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model-index: |
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- name: speecht5_finetuned_voxpopuli_nl |
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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_voxpopuli_nl |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the italian section of voxpopuli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4896 |
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## Model description |
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This model do text to speeche task in italian language |
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## Intended uses & limitations |
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More information needed |
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Example: |
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from transformers import AutoProcessor, SpeechT5ForTextToSpeech |
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processor = AutoProcessor.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it") |
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model = SpeechT5ForTextToSpeech.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it") |
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speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0) |
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text = "Quando pensi che sarà possibile viaggiare?" |
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inputs = processor(text=text, return_tensors="pt") |
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vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
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speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) |
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from IPython.display import Audio |
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Audio(speech.numpy(), rate=16000) |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.5445 | 6.13 | 1000 | 0.5106 | |
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| 0.5262 | 12.26 | 2000 | 0.4964 | |
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| 0.5154 | 18.39 | 3000 | 0.4918 | |
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| 0.5186 | 24.52 | 4000 | 0.4896 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
