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Extremely Fast and Accurate Transcription with Granite Speech 5.0 Turbo CTC

IBM has released two new models in the Granite Speech family, offering strong accuracy and unprecedented speed. The models, Granite Speech 5.0 Turbo CTC, can transcribe more than 3.5 hours of speech in one second using batched inference.

By Brian Kingsbury, George Saon, Samuel Thomas, Vishal Sunder, Jeff Kuo, Takashi Fukuda, Masayuki Suzuki, Madison Lee·Aug 25·huggingface.co·2 min read

Intelligence analysis by Llama

Extremely Fast and Accurate Transcription with Granite Speech 5.0 Turbo CTC
Image: huggingface.co

IBM has released two new models in the Granite Speech family, offering strong accuracy and unprecedented speed. The models, Granite Speech 5.0 Turbo CTC, can transcribe more than 3.5 hours of speech in one second using batched inference. The models are encoder-only, unlike prior Granite Speech models, and have a small memory footprint of only 470M parameters.

Why it matters

The release of these models has significant implications for the field of speech recognition, offering faster and more accurate transcription capabilities. This could have a major impact on industries such as customer service, healthcare, and finance.

Imagine you're having a conversation with a friend, and you want to understand what they're saying. The Granite Speech 5.0 models are like super-fast and accurate listeners that can transcribe what you're saying in real-time, allowing you to focus on the conversation without worrying about taking notes.

Analysis

Performance Metrics

The Granite Speech 5.0 models have been evaluated on the public, English short-form test sets from the OpenASR Leaderboard. The results show that the models offer high accuracy, with the noncommercial model scoring an aggregate 4.85% WER and the Apache 2.0 model scoring 5.00% WER. The models also demonstrate unprecedented aggregate throughput in excess of 12,600 RTFx.

Model Architecture

The Granite Speech 5.0 models are encoder-only models, unlike prior Granite Speech models which comprise an acoustic encoder, projector, and Granite LM with LoRA adapters. The encoder-only design provides strong transcription performance, a small memory footprint of only 470M parameters, and over 20x faster throughput than previous Granite Speech models.

Training Data

The Granite Speech 5.0 models are trained with a combination of natural and synthetic data. The natural data includes a large corpus of transcribed speech, while the synthetic data includes artificially generated speech. The models are trained using a combination of supervised and unsupervised learning techniques.

Key points

  • IBM has released two new models in the Granite Speech family, offering strong accuracy and unprecedented speed.
  • The models, Granite Speech 5.0 Turbo CTC, can transcribe more than 3.5 hours of speech in one second using batched inference.
  • The models are encoder-only, unlike prior Granite Speech models, and have a small memory footprint of only 470M parameters.
  • The models have been evaluated on the public, English short-form test sets from the OpenASR Leaderboard, demonstrating high accuracy and unprecedented aggregate throughput.
  • The models are trained with a combination of natural and synthetic data, using a combination of supervised and unsupervised learning techniques.
The Upside

The release of these models could lead to significant advancements in the field of speech recognition, enabling faster and more accurate transcription capabilities. This could have a major impact on industries such as customer service, healthcare, and finance, leading to improved efficiency and productivity.

The Downside

However, the development and deployment of these models could also raise concerns about data privacy and security. As with any new technology, there is a risk that sensitive information could be compromised, leading to potential security breaches.

Originally reported at

huggingface.co

Discernion covers the story. Read the full piece at the source.

Tagsai-agentsspeech-recognitionnlpmachine-learningibm

Author

Brian Kingsbury, George Saon, Samuel Thomas, Vishal Sunder, Jeff Kuo, Takashi Fukuda, Masayuki Suzuki, Madison Lee

Intelligence analysis by

Llama

Published

Aug 25, 2026

Source

huggingface.co

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Topics

ai-agentsspeech-recognitionnlpmachine-learningibm

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