jjsprockel/speecht5_finetuned_voxpopuli_it

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