benny-png/Swahili-model-for-Audio-Text-to-Speech
Domain:
natural language processingRecord type:
model
Creator:
ben
Host:
# Swahili MMS TTS - Finetuned Model
This is a fine-tuned version of the Facebook MMS (Massively Multilingual Speech) model for Swahili Text-to-Speech (TTS). The model was fine-tuned to improve Swahili pronunciation and performance using custom audio datasets.
## Model Details
- **Model Name**: Swahili MMS TTS - Finetuned
- **Languages Supported**: Swahili
- **Base Model**: Facebook MMS
- **Use Case**: Text-to-Speech for Swahili language, suitable for generating high-quality speech from text.
## Training Details
The fine-tuning process was done using a custom dataset of Swahili voice samples to improve the fluency and accuracy of the original MMS model in Swahili. This resulted in enhanced pronunciation and natural-sounding speech for Swahili.
You can check out the code and process used in the fine-tuning by visiting the GitHub repository.
## How to Use
You can load and use the model directly from the Hugging Face model hub using either the `pipeline` API or by manually downloading the model and tokenizer.
### 1. Download and Run the Model Directly
You can also download the model and tokenizer manually and run the text-to-speech pipeline without the Hugging Face `pipeline` helper. Here's how:
```python
import torch
import numpy as np
import scipy.io.wavfile
from transformers import VitsModel, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "Benjamin-png/swahili-mms-tts-finetuned"
text = "Habari, karibu kwenye mfumo wetu wa kusikiliza kwa Kiswahili."
audio_file_path = "swahili_speech.wav"
# Load model and tokenizer dynamically based on the provided model name
model = VitsModel.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Step 1: Tokenize the input text
inputs = tokenizer(text, return_tensors="pt").to(device)
# Step 2: Generate waveform
with torch.no_grad():
output = model(**inputs).waveform
# Step 3: Convert PyTorch tensor to NumPy array
output_np = out …