Speech to text api open source9/2/2023 ![]() Storage-Agnostic Data HandlingĪudio data has traditionally been stored on filesystems, but we’re increasingly seeing teams move to the cloud object stores and other cloud native storage services. Figure 3: The same part of a conversation, used to construct the training data for either (A) speech recognition or (B) voice activity detection. One can also glue different cuts together and mix them with some noise to create a new dataset, or augment existing data. For example, one can easily reuse a speech recognition conversational dataset for voice activity detection (see Figure 3). With cuts, it is very easy to repurpose an existing dataset for other tasks. It’s like working with rows in a pandas dataframe, but for audio – and like with dataframe columns, you can extend cuts with any new types of features or metadata you happen to collect. A great feature of specific cuts is that they reference all the relevant items: audio, text transcription, speaker label, and any features you might have extracted for that segment. Think of audio engineers in a professional studio, cutting magnetic tapes in the 1980s. ![]() Lhotse allows users to seamlessly retrieve segments of interest. Sometimes data is nicely “segmented” into single phrases, but other times we have longer recordings such as podcasts. Working with audio is challenging due to the length of recordings. Lhotse provides fifty recipes to prepare data from commonly used audio datasets. Thankfully, an open source project called Lhotse resolves most of these common challenges. We have yet to meet a machine learning engineer who enjoys dealing with the challenges that come with audio data. Lhotse simplifies speech data processing, data integration, and more. Figure 2: Unlocking real-time audio data is challenging. In addition, speech applications and services often involve real-time processing, where models require special considerations for handling incremental inputs. The ad hoc approach to labeling might be sufficient for academic and R&D research, but it makes it very difficult to combine multiple sources of data when building real-world speech applications. Typically every audio dataset has its own way of affixing labels and metadata. Common labels include things like “who’s speaking”, how old they are, change in speaker, emotions, and sentiment. The same cannot be said of metadata such as text transcripts or labels used in model training. While there are many audio codecs, the speech community has standardized around a few formats (WAV/PCM, MP3, OPUS, FLAC, etc.). These channels can all be in a single file, or in multiple files, depending on the mood of the person releasing the data. Often the data has multiple channels (mono, stereo, or more – a popular Microsoft Kinect sensor for gaming has four different microphones). Data is either lossless or lossy and may require different codecs to read, and not all codecs are readily available in Python. What are the main issues with speech data? Historically, many different formats have been developed for storing and compressing speech data. There are several quirks associated with each type of data: tabular data can have missing values or unnormalized records text often needs normalization images often need to be resized, labeled, and checked for duplicates. Figure 1: Three main components of voice applications. In this post we’ll describe a suite of open source software that simplify data processing, data integration, pipelining, and reproducibility for audio data. Up until recently, teams who work with audio data had to build bespoke tools. All ML and AI applications – including speech apps – depend on data. There is one obstacle to making this vision a reality: most data and AI teams are unable to work with speech data due to the current state of tools. In a previous post, we listed many potential applications of speech technologies. We can also generate natural-sounding speech with desired voice timbre and other qualities, or even transform the way people sound. Based on speech, one can discern the age, emotion, or identity of a person. However, voice is a richer medium than text, and there are many interesting products to be built beyond just recognition. Voice and speech recognition market alone is expected to grow from $9.4 billion in 2022 to $28.1 billion by 2027 according to a report by MarketsAndMarkets. Of the many voice applications for AI, speech recognition is the most widely known and deployed as a building block of voice assistants. Introducing Lhotse, a Python library for handling speech data.īy Piotr Żelasko, Jan Vainer, Tomáš Nekvinda, and Ben Lorica.
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