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---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the italian section of voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4896
## Model description
This model do text to speeche task in italian language
## Intended uses & limitations
More information needed
Example:
from transformers import AutoProcessor, SpeechT5ForTextToSpeech
processor = AutoProcessor.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it")
model = SpeechT5ForTextToSpeech.from_pretrained("jjsprockel/speecht5_finetuned_voxpopuli_it")
speaker_embeddings = torch.tensor(example["speaker_embeddings"]).unsqueeze(0)
text = "Quando pensi che sarà possibile viaggiare?"
inputs = processor(text=text, return_tensors="pt")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
from IPython.display import Audio
Audio(speech.numpy(), rate=16000)
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5445 | 6.13 | 1000 | 0.5106 |
| 0.5262 | 12.26 | 2000 | 0.4964 |
| 0.5154 | 18.39 | 3000 | 0.4918 |
| 0.5186 | 24.52 | 4000 | 0.4896 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |