metadata

library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
  - generated_from_trainer
model-index:
  - name: speecht5_tr_commonvoice_2
    results: []

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