speecht5_tts_tr_v1.0

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SpeechT5 TTS Turkish

This model is a fine-tuned version of microsoft/speecht5_tts on the turkishvoicedataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.3079

Model description

More information needed

Intended uses & limitations

More information needed

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: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 6000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 2.4.1+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1

Usage

installs

!pip install datasets soundfile speechbrain

inference

from transformers import pipeline
from datasets import load_dataset
import soundfile as sf
import torch
from IPython.display import Audio

synthesiser = pipeline("text-to-speech", "umarigan/speecht5_tts_tr_v1.0")

embeddings_dataset = load_dataset("umarigan/turkish_voice_dataset_embedded", split="train")
speaker_embedding = torch.tensor(embeddings_dataset[736]["speaker_embeddings"]).unsqueeze(0)

# Synthesize speech using the embedding
speech = synthesiser("Bir berber bir berbere gel beraber bir berber kuralım demiş", forward_params={"speaker_embeddings": speaker_embedding})

# Save the generated audio to a file
sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])

# Play the audio in the notebook
Audio("speech.wav")