---
library_name: transformers
datasets:
- mesolitica/tts-combine-annotated
language:
- ms
---

# Malay Parler TTS Mini V1

Finetuned https://huggingface.co/parler-tts/parler-tts-mini-v1 on [Malay TTS dataset](https://huggingface.co/datasets/mesolitica/tts-combine-annotated)

Source code at https://github.com/mesolitica/malaya-speech/tree/master/session/parler-tts

Wandb at https://wandb.ai/huseinzol05/parler-speech?nw=nwuserhuseinzol05

## requirements

```bash
pip3 install git+https://github.com/mesolitica/async-parler-tts
```

## how to

```python
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf

device = "cuda:0" if torch.cuda.is_available() else "cpu"

model = ParlerTTSForConditionalGeneration.from_pretrained("mesolitica/malay-parler-tts-mini-v1").to(device)
tokenizer = AutoTokenizer.from_pretrained("mesolitica/malay-parler-tts-mini-v1")

speakers = [
    'Yasmin',
    'Osman',
    'Bunga',
    'Ariff',
    'Ayu',
    'Kamarul',
    'Danial',
    'Elina',
]

prompt = 'Husein zolkepli sangat comel dan kacak suka makan cendol'

for s in speakers:
    description = f"{s}'s voice, delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."

    input_ids = tokenizer(description, return_tensors="pt").to(device)
    prompt_input_ids = tokenizer(prompt, return_tensors="pt").to(device)

    generation = model.generate(
      input_ids=input_ids.input_ids,
      attention_mask=input_ids.attention_mask,
      prompt_input_ids=prompt_input_ids.input_ids,
      prompt_attention_mask=prompt_input_ids.attention_mask,
    )

    audio_arr = generation.cpu()
    sf.write(f'{s}.mp3', audio_arr.numpy().squeeze(), 44100)
```