README.md

---
license: mit
datasets:
- mussacharles60/mcv-sw-female-dataset
- mozilla-foundation/common_voice_17_0
language:
- sw
base_model:
- facebook/mms-tts
tags:
- text-to-speech
- swahili
- swahili-text-to-speech
- tts
- swahili-tts
library_name: transformers
---


Swahili female voice text-to-speech model

This is a continuous development of text-to-speech model for female voice using Swahili language 

Please give it a try

for inference try the following

```python
# import all required libraries
from transformers import VitsModel, AutoTokenizer
import torch
import numpy as np
import scipy.io.wavfile

# Load model and tokenizer
model = VitsModel.from_pretrained("mussacharles60/swahili-tts-female-voice")
tokenizer = AutoTokenizer.from_pretrained("mussacharles60/swahili-tts-female-voice")

# Running the TTS
text = "Mambo vipi ?, Hii ni Myssa Tech sauti ya A.I, kujaribishwa na Mussa Charles"
inputs = tokenizer(text, return_tensors="pt")

# Generate waveform
with torch.no_grad():
    output = model(**inputs).waveform

# Convert PyTorch tensor to NumPy array
output_np = output.squeeze().cpu().numpy()

# Write to WAV file
scipy.io.wavfile.write("female_voice_test.wav", rate=model.config.sampling_rate, data=output_np)

```

You're all welcome to contribute.

Thanks 🤗