mozilla-foundation/common_voice_17_0
Updated • 5.81k • 24
How to use mussacharles60/swahili-tts-female-voice with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-speech", model="mussacharles60/swahili-tts-female-voice") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("mussacharles60/swahili-tts-female-voice")
model = AutoModelForMultimodalLM.from_pretrained("mussacharles60/swahili-tts-female-voice")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
# 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 🤗
Base model
facebook/mms-tts