benny-png/Swahili-model-for-Audio-Text-to-Speech

# 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 …