speecht5_tr_commonvoice_2

This model is a fine-tuned version of microsoft/speecht5_tts on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5934

Model description

import torch
from transformers import pipeline
from datasets import load_dataset
import soundfile as sf

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

from transformers import pipeline
pipe = pipeline("text-to-audio", model="Chan-Y/speecht5_finetuned_tr_commonvoice")

text = "bugün okula erken geldim, çalışmam lazım. çok sıkıcı bir dersim var."
result = pipe(text, forward_params={"speaker_embeddings": speaker_embedding})

sf.write("speech.wav", result["audio"], samplerate=result["sampling_rate"])

from IPython.display import Audio
Audio("speech.wav")

Training and evaluation data

I used CommonVoice Turkish Corpus 19.0

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-06
  • train_batch_size: 8
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
0.7533 1.2972 1000 0.6445
0.6745 2.5945 2000 0.6106
0.6535 3.8917 3000 0.5953
0.6593 5.1889 4000 0.5934

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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