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Hugging Face Speech-to-Speech - AI SDKs Tool

Overview

Hugging Face Speech-to-Speech is an open-source, modular pipeline that integrates Voice Activity Detection, Speech-to-Text, Language Models, and Text-to-Speech. It uses models from the Transformers library (for example, Whisper and Parler-TTS) and is designed for flexible deployment. The project supports server, client, and local setups, providing modular components and examples for building end-to-end speech-to-speech workflows.

Key Features

  • Open-source modular pipeline combining VAD, STT, language models, and TTS.
  • Built on Hugging Face Transformers; examples include Whisper and Parler-TTS.
  • Supports server, client, and local deployment configurations.
  • Components designed for end-to-end speech-to-speech workflows.
  • Repository includes examples and modular components for customization.

Ideal Use Cases

  • Prototyping end-to-end speech-to-speech applications.
  • Building speech translation pipelines combining STT and TTS.
  • Integrating custom Transformer models into audio workflows.
  • Deploying local or server-side solutions for privacy-sensitive audio.

Getting Started

  • Clone the GitHub repository.
  • Install required Python dependencies.
  • Select pretrained models from the Transformers library.
  • Configure VAD, STT, LM, and TTS pipeline components.
  • Run provided example scripts to validate the pipeline.

Pricing

Not disclosed in the repository; project is open-source and does not list hosted or paid pricing.

Limitations

  • No hosted service or pricing details disclosed in the repository.
  • Relies on external Transformer models; model availability and licensing vary.

Key Information

  • Category: SDKs
  • Type: AI SDKs Tool
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