Personalizing a speech synthesis system is a highly desired application, where the system can generate speech with the user's voice with rare enrolled recordings. There are two main approaches to build such a system in recent works: speaker adaptation and speaker encoding. On the one hand, speaker adaptation methods fine-tune a trained multi-speaker text-to-speech (TTS) model with few enrolled samples. However, they require at least thousands of fine-tuning steps for high-quality adaptation, making it hard to apply on devices. On the other hand, speaker encoding methods encode enrollment utterances into a speaker embedding. The trained TTS model can synthesize the user's speech conditioned on the corresponding speaker embedding. Nevertheless, the speaker encoder suffers from the generalization gap between the seen and unseen speakers. In this paper, we propose applying a meta-learning algorithm to the speaker adaptation method. More specifically, we use Model Agnostic Meta-Learning (MAML) as the training algorithm of a multi-speaker TTS model, which aims to find a great meta-initialization to adapt the model to any few-shot speaker adaptation tasks quickly. Therefore, we can also adapt the meta-trained TTS model to unseen speakers efficiently. Our experiments compare the proposed method (Meta-TTS) with two baselines: a speaker adaptation method baseline and a speaker encoding method baseline. The evaluation results show that Meta-TTS can synthesize high speaker-similarity speech from few enrollment samples with fewer adaptation steps than the speaker adaptation baseline and outperforms the speaker encoding baseline under the same training scheme. When the speaker encoder of the baseline is pre-trained with extra 8371 speakers of data, Meta-TTS can still outperform the baseline on LibriTTS dataset and achieve comparable results on VCTK dataset.

EasyOCR is a free developer-friendly OCR "Optical Character Recognition" that supports 80 languages including Latin, Chinese, Arabic, and Cyrillic. EasyOCR is written in the Python programming language. It can be installed as a Python package, and integrates well with other Python Frameworks like Django, Flask, and others. You can test the demo here, as you can upload images in different format and test several languages. It comes with a trainer models that can be used to train for new languages, dozens of example datasets for model training, user-friendly instructions on how to train custom recognition models and more. It also supports vertical text, and PIL images, and more.

Documents have spent decades stubbornly guarding their contents against software. In the late 1960s, the first OCR (optical character recognition) techniques turned scanned documents into raw text. By indexing and searching the text from these digitized documents, software sped up formerly laborious legal discovery and research projects. Today, Google, Microsoft, and Amazon provide high-quality OCR as part of their cloud services offerings. But documents remain underused in software toolchains, and valuable data languish in trillions of PDFs.

Veryfi, using artificial intelligence (AI) technology to transform documents into structured data in just seconds, has announced continued strong business momentum and growth in the second quarter. As economic concerns increase, many companies begin to reduce their staff to control costs; 88 percent of job loss in routine occupations occurs within 12 months of a recession. While economic uncertainty continues, Veryfi has emerged as a trusted, reliable partner for companies seeking greater efficiency and stronger customer relationships, continuing its strong annual recurring revenue (ARR) growth. In the second quarter, Veryfi added over a dozen new logos and major accounts including a top supplier of enterprise resource planning software and one of the world's largest CRM/Direct Marketing Network companies. "As companies seek new ways to increase efficiency and manage costs to position themselves for a challenging economy, Veryfi is leading the way, applying AI to automate routine data entry and streamline business processes," said Ernest Semerda, co-founder and CEO of Veryfi.

Amazon Mechanical Turk (MTurk) is a crowdsourcing website for businesses (known as Requesters) to hire remotely located "crowdworkers" to perform discrete on-demand tasks that computers are currently unable to do. It is operated under Amazon Web Services, and is owned by Amazon.[1] Employers post jobs known as Human Intelligence Tasks (HITs), such as identifying specific content in an image or video, writing product descriptions, or answering questions, among others. Workers, colloquially known as Turkers or crowdworkers, browse among existing jobs and complete them in exchange for a rate set by the employer. To place jobs, the requesting programs use an open application programming interface (API), or the more limited MTurk Requester site.[2] As of April 2019, Requesters could register from only 49 approved countries.[3]

Recently, leveraging BERT pre-training to improve the phoneme encoder in text to speech (TTS) has drawn increasing attention. However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input. Pre-training only with phonemes as input can alleviate the input mismatch but lack the ability to model rich representations and semantic information due to limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a novel variant of the BERT model that uses mixed phoneme and sup-phoneme representations to enhance the learning capability. Specifically, we merge the adjacent phonemes into sup-phonemes and combine the phoneme sequence and the merged sup-phoneme sequence as the model input, which can enhance the model capacity to learn rich contextual representations. Experiment results demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to the previous TTS pre-trained model PnG BERT

As economic concerns increase, many companies begin to reduce their staff to control costs; 88 percent of job loss in routine occupations occurs within 12 months of a recession. While economicda uncertainty continues, Veryfi has emerged as a trusted, reliable partner for companies seeking greater efficiency and stronger customer relationships, continuing its strong annual recurring revenue (ARR) growth. In the second quarter, Veryfi added over a dozen new logos and major accounts including a top supplier of enterprise resource planning software and one of the world's largest CRM/Direct Marketing Network companies. "As companies seek new ways to increase efficiency and manage costs to position themselves for a challenging economy, Veryfi is leading the way, applying AI to automate routine data entry and streamline business processes," said Ernest Semerda, co-founder and CEO of Veryfi. "In Q2, we welcomed over a dozen new customers and multiple strategic accounts spanning key use cases from loyalty marketing to intelligent automation for accounts payable. We are seeing cross-market demand for our Veryfi technology."

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinder their applications to text-to-speech deployment. Through the preliminary study on diffusion model parameterization, we find that previous gradient-based TTS models require hundreds or thousands of iterations to guarantee high sample quality, which poses a challenge for accelerating sampling. In this work, we propose ProDiff, on progressive fast diffusion model for high-quality text-to-speech. Unlike previous work estimating the gradient for data density, ProDiff parameterizes the denoising model by directly predicting clean data to avoid distinct quality degradation in accelerating sampling. To tackle the model convergence challenge with decreased diffusion iterations, ProDiff reduces the data variance in the target site via knowledge distillation. Specifically, the denoising model uses the generated mel-spectrogram from an N-step DDIM teacher as the training target and distills the behavior into a new model with N/2 steps. As such, it allows the TTS model to make sharp predictions and further reduces the sampling time by orders of magnitude. Our evaluation demonstrates that ProDiff needs only 2 iterations to synthesize high-fidelity mel-spectrograms, while it maintains sample quality and diversity competitive with state-of-the-art models using hundreds of steps. ProDiff enables a sampling speed of 24x faster than real-time on a single NVIDIA 2080Ti GPU, making diffusion models practically applicable to text-to-speech synthesis deployment for the first time. Our extensive ablation studies demonstrate that each design in ProDiff is effective, and we further show that ProDiff can be easily extended to the multi-speaker setting. Audio samples are available at \url{https://ProDiff.github.io/.}

Detailed and Comprehensive Documentation Cloud Vendor Text to Speech Prices Notes Please note, for the script to work correctly, you need to have valid AWS account. Latest Changes 22.04.2022 - 2.0 - New: Full redesign with Laravel Framework - New: Powerful integrated Sound Studio - New: Mixing up to 20 voices in a single synthesize task

Existing Text-to-Speech (TTS) systems need to read messages from the email which may have Personal Identifiable Information (PII) to text messages that can have a streak of emojis and punctuation. 92% of the world's online population use emoji with more than 10 billion emojis sent everyday. Lack of preprocessor leads to messages being read as-is including punctuation and infographics like emoticons. This problem worsens if there is a continuous sequence of punctuation/emojis that are quite common in real-world communications like messaging, Social Networking Site (SNS) interactions, etc. In this work, we aim to introduce a lightweight intelligent preprocessor (LIP) that can enhance the readability of a message before being passed downstream to existing TTS systems. We propose multiple sub-modules including: expanding contraction, censoring swear words, and masking of PII, as part of our preprocessor to enhance the readability of text. With a memory footprint of only 3.55 MB and inference time of 4 ms for up to 50-character text, our solution is suitable for real-time deployment. This work being the first of its kind, we try to benchmark with an open independent survey, the result of which shows 76.5% preference towards LIP enabled TTS engine as compared to standard TTS.