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Speech Synthesis and Recognition

8 posts · Feb 2023 – Sep 2026

What this trail covers

Hugging Face now supports SpeechT5, a versatile model for speech synthesis, recognition, and more. This enables users to perform tasks like text-to-speech, speech-to-text, and speech translation directly within the Hugging Face ecosystem. This post introduces the NVIDIA Magpie TTS model, offering low-latency multilingual voice generation with open weights and full deployment control, enhancing the capabilities for speech synthesis. It also details the CohereLabs/cohere-transcribe-03-2026 model, and introduces Falcon-ASR, a 1.6 billion parameter speech recognition model for Arabic, with a particular focus on the Emirati dialect, also supporting English, French, Spanish, and Portuguese.

All Hugging Face trails
  1. Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTSThis post introduces NVIDIA Magpie TTS Multilingual, a 364M-parameter open-weights model supporting 12 languages (English, Spanish, French, German, Italian, Vietnamese, Mandarin, Hindi, Japanese, Modern Standard Arabic, Korean, Brazilian Portuguese). It highlights architectural improvements like frame stacking and local transformers for reduced inference time and natural speech quality. The post also details performance metrics (TTFA, RTFX) on various NVIDIA GPUs and emphasizes the benefits of open weights for deployment control, latency optimization, and customization. It also mentions improvements in character error rates (CER) and speaker similarity (SSIM) for existing languages and establishes baselines for new languages.
  2. Measuring benchmark optimization in speech recognitionThis post details the optimization of benchmark performance for the CohereLabs/cohere-transcribe-03-2026 Automatic Speech Recognition model. It focuses on measuring and improving the efficiency of speech recognition benchmarks, likely involving analysis of inference speed, accuracy metrics, and resource utilization under various testing conditions.
  3. Introducing Falcon ASRIntroduces Falcon-ASR, a 1.6 billion parameter Automatic Speech Recognition (ASR) model for Arabic (including Emirati dialect) and other languages (English, French, Spanish, Portuguese). The post details its performance metrics (WER, CER) on various Arabic and English benchmarks, highlighting its competitive results against existing leaderboards. It also discusses the challenges of Arabic speech variation and the training methodology that accounts for different recording conditions and dialects. The model builds on Falcon3-Audio architecture.
  4. Fine-Tune Whisper For Multilingual ASR with 🤗 TransformersThis post details the process of fine-tuning the Whisper model for multilingual Automatic Speech Recognition (ASR) using the 🤗 Transformers library. It covers data preparation, model loading, and training configurations to adapt Whisper for specific languages or multilingual ASR tasks. The contribution is the practical application and guidance for extending Whisper's capabilities beyond its pre-trained multilingual support.
  5. Speech Synthesis, Recognition, and More With SpeechT5This post introduces the mechanicalsea/speecht5-tts model, a specific implementation of SpeechT5 for text-to-speech, and highlights the NVIDIA Magpie TTS model for low-latency multilingual voice generation, and the CohereLabs/cohere-transcribe-03-2026 model for speech recognition.