---
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