swahili mms tts finetuned EU AI Act Compliance Profile

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:

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(**inpu

Your obligations as a swahili mms tts finetuned deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags