speecht5_finetuned_voxpopuli_nl

This model is a fine-tuned version of 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

jjsprockel
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speecht5_finetuned_voxpopuli_it