metadata

library_name: transformers
tags:
  - generated_from_trainer
model-index:
  - name: speecht5_finetuned_tr_commonvoice
    results: []
language:
  - tr
base_model:
  - microsoft/speecht5_tts

speecht5_finetuned_tr_commonvoice

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 0.5179
eval_runtime: 361.0936
eval_samples_per_second: 32.161
eval_steps_per_second: 16.082
epoch: 1.6783
step: 2000

Model description

import torch
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."
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 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: 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

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3