# Run Open Source AI Models, Transcribe Audio And Search Datasets With Hugging Face **Published:** 2026-11-03 **Author:** Kin Lane **Canonical:** https://apievangelist.com/2026/11/03/run-open-source-ai-models-transcribe-audio-and-search-datasets-with-hugging-face/ Your team wants to summarize customer feedback, transcribe sales calls or sort product photos, and someone has suggested using an open source AI model instead of locking into a single vendor. The question that follows is practical: where do those models live, and how does your product actually use one without your team standing up servers? [Hugging Face](https://apis.io/providers/hugging-face/) is where a great many of them live, and it offers them through an API, a way for software to talk directly to other software, so your product or an AI assistant can run a model, transcribe audio or look inside a dataset for you. ## What Hugging Face does Hugging Face describes itself as the AI community building the future with open-source machine learning models, datasets, and applications. Its Hub is the place people publish those models and datasets, and the APIs let you call them, manage them and deploy dedicated copies of them for your own use. More on the [Hugging Face website](https://huggingface.co). ## What you can get done Our catalog sorts Hugging Face's operations under four business capabilities. Here is what each one gets you. ### [Run AI models on your own text and data](https://apis.io/capabilities/artificial-intelligence-management/) This is the big one, with 34 operations behind it. Inference is the word for asking a trained model to do its job on your input, such as summarizing a document or judging whether a review is positive. Hugging Face lets you pick a specific model from the Hub, or just name the task and let it choose a recommended model. You can also create embeddings, which turn sentences into lists of numbers so your software can find text with similar meaning, the basis of a good internal search. - `POST /models/{model_id}` runs inference on any Hub model. - `POST /pipeline/{task}` runs a task, such as summarization, with a recommended model. - `POST /models/{model_id}/summarization` shortens a long text into a summary. - `POST /models/{model_id}/text-classification` classifies text, such as sentiment. - `POST /v1/embeddings` creates embedding vectors from text. - `GET /endpoint/{namespace}` lists the dedicated Inference Endpoints your account or organization has deployed. ### [Turn speech into text and text into speech](https://apis.io/capabilities/speech-voice/) Three operations cover voice. If you record calls, meetings or voicemails, transcription makes them searchable and quotable. Going the other way, generating speech lets you read content aloud for accessibility or phone systems. - `POST /v1/audio/transcriptions` transcribes an uploaded audio file, with an optional language and a guiding prompt. - `POST /models/{model_id}/automatic-speech-recognition` sends audio to a particular speech recognition model and returns text. - `POST /v1/audio/speech` turns text into spoken audio, with a choice of voices and speed. ### [Understand what is in an image](https://apis.io/capabilities/vision/) Two operations handle pictures. Classification tells you what an image shows, which helps with tagging a product catalog. Object detection goes further and tells you where each thing is in the photo. - `POST /models/{model_id}/image-classification` classifies an image into categories with confidence scores. - `POST /models/{model_id}/object-detection` finds objects and returns their labels and bounding boxes. ### [Chat with a model through one familiar endpoint](https://apis.io/capabilities/model-inference/) One operation here, and it matters. It is OpenAI-compatible, meaning code already written for that style of chat request can point at Hugging Face instead, and the request is routed to an inference provider for you. - `POST /v1/chat/completions` gets a chat completion via the inference router. ## Workflows that put it together A workflow is a recipe of several calls made in order to finish a real job. APIs.io catalogs 14 for Hugging Face, and these show the range: - [Chat Completion with Model Discovery](https://apis.io/arazzos/hugging-face/hugging-face-chat-completion-with-model-discovery-workflow/) finds an available model, confirms it exists, then runs a chat completion. - [Deploy Inference Endpoint and Wait](https://apis.io/arazzos/hugging-face/hugging-face-deploy-inference-endpoint-workflow/) creates a dedicated endpoint and checks its status until it is running. - [Dataset Validate and Preview](https://apis.io/arazzos/hugging-face/hugging-face-dataset-validate-and-preview-workflow/) checks a dataset is ready, lists its splits, then previews the first rows. - [Dataset Search and Statistics](https://apis.io/arazzos/hugging-face/hugging-face-dataset-search-and-statistics-workflow/) searches within a dataset, then pulls column statistics. - [Dataset Filter Rows](https://apis.io/arazzos/hugging-face/hugging-face-dataset-filter-rows-workflow/) applies a SQL-like filter to a dataset. - [Dataset Size and Parquet Files](https://apis.io/arazzos/hugging-face/hugging-face-dataset-size-and-parquet-workflow/) reads a dataset's size and lists its Parquet files, the format analysts load into their tools. - [Create Repository and Configure](https://apis.io/arazzos/hugging-face/hugging-face-create-repo-and-configure-workflow/) creates a new repository and sets its visibility and gating. - [Discover a Space](https://apis.io/arazzos/hugging-face/hugging-face-discover-space-workflow/) searches Hub Spaces, the hosted demo applications, and loads the top one's details. ## Ask for it in plain language Our conversational overlays for Hugging Face map everyday questions to the operation that answers them, so an AI assistant knows which call answers which question. These are the kinds of things you can ask: - "Can I name just a task like summarization and let Hugging Face pick the best model?" - "Can I shorten a long article into a brief summary with a Hub model?" - "Is there an audio transcriptions endpoint where I upload a file and pick the model?" - "Can I convert a paragraph of text into a spoken audio file with Hugging Face?" - "Can a Hugging Face model find every object in a photo and tell me where each one is?" - "Which Inference Endpoints are running under my account or organization?" You can browse one in the [Hugging Face Audio API phrasing overlay](https://apis.io/overlays/hugging-face/hugging-face-audio-api-phrasing-overlay/). ## Getting started Hugging Face is freemium with self-serve signup, and it is open core, an open source project alongside a commercial hosted product. Your developers will want the [Inference API documentation](https://huggingface.co/docs/api-inference/index), the [Hub API documentation](https://huggingface.co/docs/hub/api), the [Inference Endpoints API reference](https://huggingface.co/docs/inference-endpoints/api_reference), and the guide to [security tokens](https://huggingface.co/docs/hub/security-tokens), which is how your software proves who it is. Check [Inference Endpoints pricing](https://huggingface.co/pricing#inference-endpoints) and [Inference Providers pricing](https://huggingface.co/docs/inference-providers/pricing) before you commit. On APIs.io, see the [Hugging Face profile](https://apis.io/providers/hugging-face/), the [Chat Completions API](https://apis.io/apis/hugging-face/hugging-face-chat-completions-api/), the [Audio API](https://apis.io/apis/hugging-face/hugging-face-audio-api/), the [Datasets API](https://apis.io/apis/hugging-face/hugging-face-datasets-api/) and the [Endpoints API](https://apis.io/apis/hugging-face/hugging-face-endpoints-api/). Pick one task your team does by hand today, and have a developer try it against a Hub model this week.