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import gradio as gr
from gradio.inputs import Textbox
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
import re
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import random
import string
import soundfile as sf
import boto3
from io import BytesIO
import os
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
S3_BUCKET_NAME = os.getenv("BUCKET_NAME")
device = "cuda" if torch.cuda.is_available() else "cpu"
# load the processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
# load the model
model = SpeechT5ForTextToSpeech.from_pretrained(
"microsoft/speecht5_tts").to(device)
# load the vocoder, that is the voice encoder
vocoder = SpeechT5HifiGan.from_pretrained(
"microsoft/speecht5_hifigan").to(device)
# load the dataset to get the speaker embeddings
embeddings_dataset = load_dataset(
"Matthijs/cmu-arctic-xvectors", split="validation")
# speaker ids from the embeddings dataset
speakers = {
'awb': 0, # Scottish male
'bdl': 1138, # US male
'clb': 2271, # US female
'jmk': 3403, # Canadian male
'ksp': 4535, # Indian male
'rms': 5667, # US male
'slt': 6799 # US female
}
def generateAudio(text_to_audio, s3_save_as):
def cut_text(text, max_tokens=500):
# Remove non-alphanumeric characters, except periods and commas
text = re.sub(r"[^\w\s.,]", "", text)
tokens = word_tokenize(text_to_audio)
if len(tokens) <= max_tokens:
return text
cut = ' '.join(tokens[:max_tokens])
return cut
def save_text_to_speech(text, speaker=None):
# Preprocess text and recortar
text = cut_text(text, max_tokens=500)
# preprocess text
inputs = processor(text=text, return_tensors="pt").to(device)
if speaker is not None:
# load xvector containing speaker's voice characteristics from a dataset
speaker_embeddings = torch.tensor(
embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device)
else:
# random vector, meaning a random voice
speaker_embeddings = torch.randn((1, 512)).to(device)
# generate speech with the models
speech = model.generate_speech(
inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
if speaker is not None:
# if we have a speaker, we use the speaker's ID in the filename
output_filename = f"{speaker}-{'-'.join(text.split()[:6])}.mp3"
else:
# if we don't have a speaker, we use a random string in the filename
random_str = ''.join(random.sample(
string.ascii_letters+string.digits, k=5))
output_filename = f"{random_str}-{'-'.join(text.split()[:6])}.mp3"
# Save the generated speech to BytesIO buffer
audio_buffer = BytesIO()
sf.write(audio_buffer, speech.cpu().numpy(), samplerate=16000)
audio_buffer.seek(0)
# Upload the audio buffer to S3
s3_key = f"{s3_save_as}.mp3"
s3 = boto3.client(
's3',
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY
)
s3.upload_fileobj(audio_buffer, S3_BUCKET_NAME, s3_key)
# Return the S3 URL of the uploaded audio file
s3_url = f"https://{S3_BUCKET_NAME}.s3.amazonaws.com/{s3_key}"
return s3_url
s3_url = save_text_to_speech(text_to_audio, speakers["clb"])
return f"Saved audio: {s3_url}"
iface = gr.Interface(
fn=generateAudio,
inputs=[Textbox(label="Text to Audio"), Textbox(label="S3 Save As")],
outputs="text"
)
iface.launch()
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